From a350a16bb2c11986faf5883a2804d46b03bd7c8f Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Sat, 10 Sep 2016 05:21:52 +0200 Subject: Static documentation rebuilt with current staticdocs Using hadley/staticdocs commit #8c1069d from 8 days ago --- docs/DFOP.solution-2.png | Bin 0 -> 6375 bytes docs/DFOP.solution.html | 129 + docs/Extract.mmkin.html | 1199 ++++++++ docs/FOCUS_2006_DFOP_ref_A_to_B.html | 133 + docs/FOCUS_2006_FOMC_ref_A_to_F.html | 131 + docs/FOCUS_2006_HS_ref_A_to_F.html | 133 + docs/FOCUS_2006_SFO_ref_A_to_F.html | 129 + docs/FOCUS_2006_datasets.html | 130 + docs/FOMC.solution-2.png | Bin 0 -> 6406 bytes docs/FOMC.solution.html | 145 + docs/HS.solution-2.png | Bin 0 -> 6153 bytes docs/HS.solution.html | 127 + docs/IORE.solution-2.png | Bin 0 -> 6168 bytes docs/IORE.solution.html | 150 + docs/SFO.solution.html | 119 + docs/SFORB.solution.html | 128 + docs/add_err-4.png | Bin 0 -> 26860 bytes docs/add_err-6.png | Bin 0 -> 15779 bytes docs/add_err-8.png | Bin 0 -> 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| 163 + docs/mkinsub.html | 149 + docs/mmkin.html | 172 ++ docs/plot.mkinfit-10.png | Bin 0 -> 18197 bytes docs/plot.mkinfit-4.png | Bin 0 -> 13748 bytes docs/plot.mkinfit-6.png | Bin 0 -> 16810 bytes docs/plot.mkinfit-8.png | Bin 0 -> 13728 bytes docs/plot.mkinfit.html | 238 ++ docs/plot.mmkin-2.png | Bin 0 -> 10552 bytes docs/plot.mmkin-4.png | Bin 0 -> 10559 bytes docs/plot.mmkin-6.png | Bin 0 -> 7945 bytes docs/plot.mmkin.html | 158 + docs/print.mkinds.html | 101 + docs/print.mkinmod.html | 101 + docs/reference.html | 288 ++ docs/schaefer07_complex_case.html | 134 + docs/summary.mkinfit.html | 259 ++ docs/synthetic_data_for_UBA.html | 156 + docs/transform_odeparms.html | 281 ++ docs/vignettes/FOCUS_D.html | 332 ++ docs/vignettes/FOCUS_L.html | 830 +++++ docs/vignettes/FOCUS_Z.pdf | Bin 0 -> 238636 bytes docs/vignettes/compiled_models.html | 327 ++ docs/vignettes/mkin.html | 349 +++ inst/web/Extract.mmkin.html | 24 +- inst/web/mccall81_245T.html | 6 +- inst/web/mkinfit.html | 8 +- inst/web/mkinpredict.html | 6 +- inst/web/summary.mkinfit.html | 4 +- inst/web/transform_odeparms.html | 6 +- inst/web/vignettes/FOCUS_D.html | 6 +- inst/web/vignettes/FOCUS_L.html | 40 +- inst/web/vignettes/FOCUS_Z.pdf | Bin 238636 -> 238632 bytes inst/web/vignettes/compiled_models.html | 32 +- vignettes/FOCUS_D.html | 332 -- vignettes/FOCUS_L.html | 830 ----- vignettes/FOCUS_Z.pdf | Bin 238636 -> 0 bytes vignettes/compiled_models.html | 327 -- vignettes/mkin.html | 349 --- 85 files changed, 17440 insertions(+), 1904 deletions(-) create mode 100644 docs/DFOP.solution-2.png create mode 100644 docs/DFOP.solution.html create mode 100644 docs/Extract.mmkin.html create mode 100644 docs/FOCUS_2006_DFOP_ref_A_to_B.html create mode 100644 docs/FOCUS_2006_FOMC_ref_A_to_F.html create mode 100644 docs/FOCUS_2006_HS_ref_A_to_F.html create mode 100644 docs/FOCUS_2006_SFO_ref_A_to_F.html create mode 100644 docs/FOCUS_2006_datasets.html create mode 100644 docs/FOMC.solution-2.png create mode 100644 docs/FOMC.solution.html create mode 100644 docs/HS.solution-2.png create mode 100644 docs/HS.solution.html create mode 100644 docs/IORE.solution-2.png create mode 100644 docs/IORE.solution.html create mode 100644 docs/SFO.solution.html create mode 100644 docs/SFORB.solution.html create mode 100644 docs/add_err-4.png create mode 100644 docs/add_err-6.png create mode 100644 docs/add_err-8.png create mode 100644 docs/add_err.html create mode 100644 docs/css/bootstrap-responsive.css create mode 100644 docs/css/bootstrap-responsive.min.css create mode 100644 docs/css/bootstrap.css create mode 100644 docs/css/bootstrap.min.css create mode 100644 docs/css/highlight.css create mode 100644 docs/css/staticdocs.css create mode 100644 docs/endpoints.html create mode 100644 docs/geometric_mean.html create mode 100644 docs/ilr.html create mode 100644 docs/img/glyphicons-halflings-white.png create mode 100644 docs/img/glyphicons-halflings.png create mode 100644 docs/index.html create mode 100644 docs/js/bootstrap.js create mode 100644 docs/js/bootstrap.min.js create mode 100644 docs/mccall81_245T.html create mode 100644 docs/mkin_long_to_wide.html create mode 100644 docs/mkin_wide_to_long.html create mode 100644 docs/mkinds.html create mode 100644 docs/mkinerrmin.html create mode 100644 docs/mkinfit.html create mode 100644 docs/mkinmod.html create mode 100644 docs/mkinparplot-4.png create mode 100644 docs/mkinparplot.html create mode 100644 docs/mkinplot.html create mode 100644 docs/mkinpredict.html create mode 100644 docs/mkinresplot-4.png create mode 100644 docs/mkinresplot.html create mode 100644 docs/mkinsub.html create mode 100644 docs/mmkin.html create mode 100644 docs/plot.mkinfit-10.png create mode 100644 docs/plot.mkinfit-4.png create mode 100644 docs/plot.mkinfit-6.png create mode 100644 docs/plot.mkinfit-8.png create mode 100644 docs/plot.mkinfit.html create mode 100644 docs/plot.mmkin-2.png create mode 100644 docs/plot.mmkin-4.png create mode 100644 docs/plot.mmkin-6.png create mode 100644 docs/plot.mmkin.html create mode 100644 docs/print.mkinds.html create mode 100644 docs/print.mkinmod.html create mode 100644 docs/reference.html create mode 100644 docs/schaefer07_complex_case.html create mode 100644 docs/summary.mkinfit.html create mode 100644 docs/synthetic_data_for_UBA.html create mode 100644 docs/transform_odeparms.html create mode 100644 docs/vignettes/FOCUS_D.html create mode 100644 docs/vignettes/FOCUS_L.html create mode 100644 docs/vignettes/FOCUS_Z.pdf create mode 100644 docs/vignettes/compiled_models.html create mode 100644 docs/vignettes/mkin.html delete mode 100644 vignettes/FOCUS_D.html delete mode 100644 vignettes/FOCUS_L.html delete mode 100644 vignettes/FOCUS_Z.pdf delete mode 100644 vignettes/compiled_models.html delete mode 100644 vignettes/mkin.html diff --git a/docs/DFOP.solution-2.png b/docs/DFOP.solution-2.png new file mode 100644 index 00000000..91efcebb Binary files /dev/null and b/docs/DFOP.solution-2.png differ diff --git a/docs/DFOP.solution.html b/docs/DFOP.solution.html new file mode 100644 index 00000000..f9df8bf7 --- /dev/null +++ b/docs/DFOP.solution.html @@ -0,0 +1,129 @@ + + + + +DFOP.solution. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
+
+
+ mkin 0.9.44.9000 +
+ +
+
+
+
+ + +
+
+ +
+ +

+Double First-Order in Parallel kinetics +

+ +
+
+

Usage

+ 
DFOP.solution(t, parent.0, k1, k2, g)
+ +

Arguments

+
+
t
+
Time.
+
parent.0
+
Starting value for the response variable at time zero.
+
k1
+
First kinetic constant.
+
k2
+
Second kinetic constant.
+
g
+
Fraction of the starting value declining according to the + first kinetic constant. +
+
+ +
+

Description

+ +

Function describing decline from a defined starting value using the sum + of two exponential decline functions.

+ +
+ +
+

Value

+ +

+ The value of the response variable at time t. +

+ +
+ +
+

References

+ +

FOCUS (2006) “Guidance Document on Estimating Persistence and + Degradation Kinetics from Environmental Fate Studies on Pesticides in EU + Registration” Report of the FOCUS Work Group on Degradation Kinetics, + EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, + http://focus.jrc.ec.europa.eu/dk

+ +
+ +

Examples

+ 
  plot(function(x) DFOP.solution(x, 100, 5, 0.5, 0.3), 0, 4, ylim=c(0,100))

+
+
+
+ + + + + +
+
+ + +
+ + \ No newline at end of file diff --git a/docs/Extract.mmkin.html b/docs/Extract.mmkin.html new file mode 100644 index 00000000..b4686a6c --- /dev/null +++ b/docs/Extract.mmkin.html @@ -0,0 +1,1199 @@ + + + + +[.mmkin. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
+
+
+ mkin 0.9.44.9000 +
+ +
+
+
+
+ + +
+
+ +
+ +

Subsetting method for mmkin objects

+ +
+
+

Usage

+ 
# S3 method for mmkin
+[(x, i, j, ..., drop = FALSE)
+ +

Arguments

+
+
x
+
An mmkin object
+
i
+
Row index selecting the fits for specific models
+
j
+
Column index selecting the fits to specific datasets
+
...
+
Not used, only there to satisfy the generic method definition
+
drop
+
If FALSE, the method always returns an mmkin object, otherwise either + a list of mkinfit objects or a single mkinfit object.
+
+ +
+

Description

+ +

Subsetting method for mmkin objects.

+ +
+ +
+

Value

+ +

+ An object of class mmkin. +

+ +
+ +

Examples

+ 
  # Only use one core, to pass R CMD check --as-cran
+  fits <- mmkin(c("SFO", "FOMC"), list(B = FOCUS_2006_B, C = FOCUS_2006_C),
+                cores = 1, quiet = TRUE)
+  fits["FOMC", ]

+
      dataset
+model  B       C      
+  FOMC List,42 List,42
+attr(,"class")
+[1] "mmkin"
+

+
  fits[, "B"]

+
      dataset
+model  B      
+  SFO  List,42
+  FOMC List,42
+attr(,"class")
+[1] "mmkin"
+

+
  fits[, "B", drop = TRUE]$FOMC

+
$par
+ parent_0 log_alpha  log_beta 
+99.666193  2.549849  5.050586 
+
+$ssr
+[1] 28.58291
+
+$convergence
+[1] 0
+
+$iterations
+[1] 21
+
+$evaluations
+function gradient 
+      25       78 
+
+$counts
+[1] "both X-convergence and relative convergence (5)"
+
+$hessian
+            parent_0   log_alpha    log_beta
+parent_0    4.123033   -95.69983    93.17699
+log_alpha -95.699832  6618.85833 -6352.46648
+log_beta   93.176993 -6352.46648  6101.23483
+
+$residuals
+      parent       parent       parent       parent       parent       parent 
+ 1.046192647 -3.322396479  3.655156669 -1.705316770  0.406306255 -0.123734689 
+      parent       parent 
+-0.036886982 -0.006240458 
+
+$ms
+[1] 3.572863
+
+$var_ms
+  parent 
+3.572863 
+
+$var_ms_unscaled
+  parent 
+3.572863 
+
+$var_ms_unweighted
+  parent 
+3.572863 
+
+$rank
+[1] 3
+
+$df.residual
+[1] 5
+
+$solution_type
+[1] "analytical"
+
+$transform_rates
+[1] TRUE
+
+$transform_fractions
+[1] TRUE
+
+$method.modFit
+[1] "Port"
+
+$maxit.modFit
+[1] "auto"
+
+$calls
+[1] 111
+
+$time
+   user  system elapsed 
+  0.276   0.000   0.277 
+
+$mkinmod
+<mkinmod> model generated with
+Use of formation fractions $use_of_ff: min 
+Specification $spec:
+$parent
+$type: FOMC; $sink: TRUE
+
+$observed
+    name time value
+1 parent    0 98.62
+2 parent    3 81.43
+3 parent    7 53.18
+4 parent   14 34.89
+5 parent   30 10.09
+6 parent   62  1.50
+7 parent   90  0.33
+8 parent  118  0.08
+
+$obs_vars
+[1] "parent"
+
+$predicted
+      name       time       value
+1   parent   0.000000 99.66619265
+2   parent   1.191919 90.41690342
+3   parent   2.383838 82.08630014
+4   parent   3.000000 78.10760352
+5   parent   3.575758 74.57722848
+6   parent   4.767677 67.80342415
+7   parent   5.959596 61.68822425
+8   parent   7.000000 56.83515667
+9   parent   7.151515 56.16343898
+10  parent   8.343434 51.16836285
+11  parent   9.535354 46.64890734
+12  parent  10.727273 42.55683931
+13  parent  11.919192 38.84911158
+14  parent  13.111111 35.48727414
+15  parent  14.000000 33.18468323
+16  parent  14.303030 32.43695565
+17  parent  15.494949 29.66740651
+18  parent  16.686869 27.15109578
+19  parent  17.878788 24.86335532
+20  parent  19.070707 22.78206538
+21  parent  20.262626 20.88737647
+22  parent  21.454545 19.16146324
+23  parent  22.646465 17.58830644
+24  parent  23.838384 16.15349953
+25  parent  25.030303 14.84407724
+26  parent  26.222222 13.64836315
+27  parent  27.414141 12.55583436
+28  parent  28.606061 11.55700107
+29  parent  29.797980 10.64329940
+30  parent  30.000000 10.49630626
+31  parent  30.989899  9.80699593
+32  parent  32.181818  9.04110261
+33  parent  33.373737  8.33930082
+34  parent  34.565657  7.69587362
+35  parent  35.757576  7.10564515
+36  parent  36.949495  6.56392657
+37  parent  38.141414  6.06646759
+38  parent  39.333333  5.60941311
+39  parent  40.525253  5.18926438
+40  parent  41.717172  4.80284421
+41  parent  42.909091  4.44726569
+42  parent  44.101010  4.11990420
+43  parent  45.292929  3.81837216
+44  parent  46.484848  3.54049644
+45  parent  47.676768  3.28429799
+46  parent  48.868687  3.04797350
+47  parent  50.060606  2.82987892
+48  parent  51.252525  2.62851456
+49  parent  52.444444  2.44251172
+50  parent  53.636364  2.27062056
+51  parent  54.828283  2.11169922
+52  parent  56.020202  1.96470393
+53  parent  57.212121  1.82868009
+54  parent  58.404040  1.70275424
+55  parent  59.595960  1.58612677
+56  parent  60.787879  1.47806529
+57  parent  61.979798  1.37789865
+58  parent  62.000000  1.37626531
+59  parent  63.171717  1.28501157
+60  parent  64.363636  1.19883967
+61  parent  65.555556  1.11886504
+62  parent  66.747475  1.04461220
+63  parent  67.939394  0.97564441
+64  parent  69.131313  0.91156031
+65  parent  70.323232  0.85199096
+66  parent  71.515152  0.79659697
+67  parent  72.707071  0.74506609
+68  parent  73.898990  0.69711084
+69  parent  75.090909  0.65246649
+70  parent  76.282828  0.61088912
+71  parent  77.474747  0.57215389
+72  parent  78.666667  0.53605348
+73  parent  79.858586  0.50239663
+74  parent  81.050505  0.47100683
+75  parent  82.242424  0.44172111
+76  parent  83.434343  0.41438896
+77  parent  84.626263  0.38887128
+78  parent  85.818182  0.36503953
+79  parent  87.010101  0.34277481
+80  parent  88.202020  0.32196716
+81  parent  89.393939  0.30251479
+82  parent  90.000000  0.29311302
+83  parent  90.585859  0.28432347
+84  parent  91.777778  0.26730596
+85  parent  92.969697  0.25138141
+86  parent  94.161616  0.23647487
+87  parent  95.353535  0.22251689
+88  parent  96.545455  0.20944302
+89  parent  97.737374  0.19719349
+90  parent  98.929293  0.18571281
+91  parent 100.121212  0.17494947
+92  parent 101.313131  0.16485560
+93  parent 102.505051  0.15538676
+94  parent 103.696970  0.14650163
+95  parent 104.888889  0.13816179
+96  parent 106.080808  0.13033150
+97  parent 107.272727  0.12297753
+98  parent 108.464646  0.11606895
+99  parent 109.656566  0.10957695
+100 parent 110.848485  0.10347470
+101 parent 112.040404  0.09773723
+102 parent 113.232323  0.09234125
+103 parent 114.424242  0.08726506
+104 parent 115.616162  0.08248842
+105 parent 116.808081  0.07799245
+106 parent 118.000000  0.07375954
+
+$cost
+function (P) 
+{
+    assign("calls", calls + 1, inherits = TRUE)
+    if (trace_parms) 
+        cat(P, "\n")
+    if (length(state.ini.optim) > 0) {
+        odeini <- c(P[1:length(state.ini.optim)], state.ini.fixed)
+        names(odeini) <- c(state.ini.optim.boxnames, state.ini.fixed.boxnames)
+    }
+    else {
+        odeini <- state.ini.fixed
+        names(odeini) <- state.ini.fixed.boxnames
+    }
+    odeparms <- c(P[(length(state.ini.optim) + 1):length(P)], 
+        transparms.fixed)
+    parms <- backtransform_odeparms(odeparms, mkinmod, transform_rates = transform_rates, 
+        transform_fractions = transform_fractions)
+    out <- mkinpredict(mkinmod, parms, odeini, outtimes, solution_type = solution_type, 
+        use_compiled = use_compiled, method.ode = method.ode, 
+        atol = atol, rtol = rtol, ...)
+    assign("out_predicted", out, inherits = TRUE)
+    mC <- modCost(out, observed, y = "value", err = err, weight = weight, 
+        scaleVar = scaleVar)
+    if (mC$model < cost.old) {
+        if (!quiet) 
+            cat("Model cost at call ", calls, ": ", mC$model, 
+                "\n")
+        if (plot) {
+            outtimes_plot = seq(min(observed$time), max(observed$time), 
+                length.out = 100)
+            out_plot <- mkinpredict(mkinmod, parms, odeini, outtimes_plot, 
+                solution_type = solution_type, use_compiled = use_compiled, 
+                method.ode = method.ode, atol = atol, rtol = rtol, 
+                ...)
+            plot(0, type = "n", xlim = range(observed$time), 
+                ylim = c(0, max(observed$value, na.rm = TRUE)), 
+                xlab = "Time", ylab = "Observed")
+            col_obs <- pch_obs <- 1:length(obs_vars)
+            lty_obs <- rep(1, length(obs_vars))
+            names(col_obs) <- names(pch_obs) <- names(lty_obs) <- obs_vars
+            for (obs_var in obs_vars) {
+                points(subset(observed, name == obs_var, c(time, 
+                  value)), pch = pch_obs[obs_var], col = col_obs[obs_var])
+            }
+            matlines(out_plot$time, out_plot[-1], col = col_obs, 
+                lty = lty_obs)
+            legend("topright", inset = c(0.05, 0.05), legend = obs_vars, 
+                col = col_obs, pch = pch_obs, lty = 1:length(pch_obs))
+        }
+        assign("cost.old", mC$model, inherits = TRUE)
+    }
+    return(mC)
+}
+<environment: 0x44a0b88>
+
+$cost_notrans
+function (P) 
+{
+    if (length(state.ini.optim) > 0) {
+        odeini <- c(P[1:length(state.ini.optim)], state.ini.fixed)
+        names(odeini) <- c(state.ini.optim.boxnames, state.ini.fixed.boxnames)
+    }
+    else {
+        odeini <- state.ini.fixed
+        names(odeini) <- state.ini.fixed.boxnames
+    }
+    odeparms <- c(P[(length(state.ini.optim) + 1):length(P)], 
+        parms.fixed)
+    out <- mkinpredict(mkinmod, odeparms, odeini, outtimes, solution_type = solution_type, 
+        use_compiled = use_compiled, method.ode = method.ode, 
+        atol = atol, rtol = rtol, ...)
+    mC <- modCost(out, observed, y = "value", err = err, weight = weight, 
+        scaleVar = scaleVar)
+    return(mC)
+}
+<environment: 0x44a0b88>
+
+$hessian_notrans
+           parent_0     alpha       beta
+parent_0  4.1230329 -7.473531  0.5968527
+alpha    -7.4735307 40.365690 -3.1777189
+beta      0.5968527 -3.177719  0.2503425
+
+$start
+         value   type
+parent_0 98.62  state
+alpha     1.00 deparm
+beta     10.00 deparm
+
+$start_transformed
+              value lower upper
+parent_0  98.620000  -Inf   Inf
+log_alpha  0.000000  -Inf   Inf
+log_beta   2.302585  -Inf   Inf
+
+$fixed
+[1] value type 
+<0 rows> (or 0-length row.names)
+
+$data
+  time variable observed   predicted     residual
+1    0   parent    98.62 99.66619265 -1.046192647
+2    3   parent    81.43 78.10760352  3.322396479
+3    7   parent    53.18 56.83515667 -3.655156669
+4   14   parent    34.89 33.18468323  1.705316770
+5   30   parent    10.09 10.49630626 -0.406306255
+6   62   parent     1.50  1.37626531  0.123734689
+7   90   parent     0.33  0.29311302  0.036886982
+8  118   parent     0.08  0.07375954  0.006240458
+
+$atol
+[1] 1e-08
+
+$rtol
+[1] 1e-10
+
+$weight.ini
+[1] "none"
+
+$reweight.tol
+[1] 1e-08
+
+$reweight.max.iter
+[1] 10
+
+$bparms.optim
+ parent_0     alpha      beta 
+ 99.66619  12.80517 156.11390 
+
+$bparms.fixed
+numeric(0)
+
+$bparms.ode
+    alpha      beta 
+ 12.80517 156.11390 
+
+$bparms.state
+  parent 
+99.66619 
+
+$date
+[1] "Sat Sep 10 05:52:53 2016"
+
+attr(,"class")
+[1] "mkinfit" "modFit" 
+

+
  fits["SFO", "B"]

+
     dataset
+model B      
+  SFO List,42
+attr(,"class")
+[1] "mmkin"
+

+
  fits[["SFO", "B"]] # This is equivalent to

+
$par
+         parent_0 log_k_parent_sink 
+        99.174072         -2.549028 
+
+$ssr
+[1] 30.65564
+
+$convergence
+[1] 0
+
+$iterations
+[1] 5
+
+$evaluations
+function gradient 
+       8       15 
+
+$counts
+[1] "relative convergence (4)"
+
+$hessian
+                    parent_0 log_k_parent_sink
+parent_0            4.163631         -94.09343
+log_k_parent_sink -94.093431        6311.34610
+
+$residuals
+     parent      parent      parent      parent      parent      parent 
+ 0.55407218 -2.98452128  4.20445742 -1.68599939 -0.58185357 -0.72033730 
+     parent      parent 
+-0.24260405 -0.07020339 
+
+$ms
+[1] 3.831956
+
+$var_ms
+  parent 
+3.831956 
+
+$var_ms_unscaled
+  parent 
+3.831956 
+
+$var_ms_unweighted
+  parent 
+3.831956 
+
+$rank
+[1] 2
+
+$df.residual
+[1] 6
+
+$solution_type
+[1] "analytical"
+
+$transform_rates
+[1] TRUE
+
+$transform_fractions
+[1] TRUE
+
+$method.modFit
+[1] "Port"
+
+$maxit.modFit
+[1] "auto"
+
+$calls
+[1] 29
+
+$time
+   user  system elapsed 
+  0.108   0.000   0.106 
+
+$mkinmod
+<mkinmod> model generated with
+Use of formation fractions $use_of_ff: min 
+Specification $spec:
+$parent
+$type: SFO; $sink: TRUE
+Coefficient matrix $coefmat available
+
+$observed
+    name time value
+1 parent    0 98.62
+2 parent    3 81.43
+3 parent    7 53.18
+4 parent   14 34.89
+5 parent   30 10.09
+6 parent   62  1.50
+7 parent   90  0.33
+8 parent  118  0.08
+
+$obs_vars
+[1] "parent"
+
+$predicted
+      name       time       value
+1   parent   0.000000 99.17407218
+2   parent   1.191919 90.35253561
+3   parent   2.383838 82.31567498
+4   parent   3.000000 78.44547872
+5   parent   3.575758 74.99369333
+6   parent   4.767677 68.32300215
+7   parent   5.959596 62.24566915
+8   parent   7.000000 57.38445742
+9   parent   7.151515 56.70891509
+10  parent   8.343434 51.66465547
+11  parent   9.535354 47.06908288
+12  parent  10.727273 42.88228661
+13  parent  11.919192 39.06790599
+14  parent  13.111111 35.59281463
+15  parent  14.000000 33.20400061
+16  parent  14.303030 32.42683275
+17  parent  15.494949 29.54246504
+18  parent  16.686869 26.91466193
+19  parent  17.878788 24.52060198
+20  parent  19.070707 22.33949373
+21  parent  20.262626 20.35239512
+22  parent  21.454545 18.54204899
+23  parent  22.646465 16.89273320
+24  parent  23.838384 15.39012410
+25  parent  25.030303 14.02117212
+26  parent  26.222222 12.77398846
+27  parent  27.414141 11.63774182
+28  parent  28.606061 10.60256435
+29  parent  29.797980  9.65946594
+30  parent  30.000000  9.50814643
+31  parent  30.989899  8.80025617
+32  parent  32.181818  8.01747313
+33  parent  33.373737  7.30431867
+34  parent  34.565657  6.65459931
+35  parent  35.757576  6.06267251
+36  parent  36.949495  5.52339762
+37  parent  38.141414  5.03209124
+38  parent  39.333333  4.58448658
+39  parent  40.525253  4.17669637
+40  parent  41.717172  3.80517911
+41  parent  42.909091  3.46670832
+42  parent  44.101010  3.15834451
+43  parent  45.292929  2.87740968
+44  parent  46.484848  2.62146400
+45  parent  47.676768  2.38828471
+46  parent  48.868687  2.17584671
+47  parent  50.060606  1.98230508
+48  parent  51.252525  1.80597899
+49  parent  52.444444  1.64533711
+50  parent  53.636364  1.49898432
+51  parent  54.828283  1.36564963
+52  parent  56.020202  1.24417505
+53  parent  57.212121  1.13350565
+54  parent  58.404040  1.03268029
+55  parent  59.595960  0.94082335
+56  parent  60.787879  0.85713708
+57  parent  61.979798  0.78089471
+58  parent  62.000000  0.77966270
+59  parent  63.171717  0.71143411
+60  parent  64.363636  0.64815202
+61  parent  65.555556  0.59049888
+62  parent  66.747475  0.53797399
+63  parent  67.939394  0.49012119
+64  parent  69.131313  0.44652489
+65  parent  70.323232  0.40680649
+66  parent  71.515152  0.37062104
+67  parent  72.707071  0.33765429
+68  parent  73.898990  0.30761993
+69  parent  75.090909  0.28025713
+70  parent  76.282828  0.25532825
+71  parent  77.474747  0.23261679
+72  parent  78.666667  0.21192552
+73  parent  79.858586  0.19307474
+74  parent  81.050505  0.17590074
+75  parent  82.242424  0.16025436
+76  parent  83.434343  0.14599973
+77  parent  84.626263  0.13301305
+78  parent  85.818182  0.12118154
+79  parent  87.010101  0.11040244
+80  parent  88.202020  0.10058214
+81  parent  89.393939  0.09163535
+82  parent  90.000000  0.08739595
+83  parent  90.585859  0.08348439
+84  parent  91.777778  0.07605845
+85  parent  92.969697  0.06929305
+86  parent  94.161616  0.06312943
+87  parent  95.353535  0.05751406
+88  parent  96.545455  0.05239819
+89  parent  97.737374  0.04773737
+90  parent  98.929293  0.04349113
+91  parent 100.121212  0.03962259
+92  parent 101.313131  0.03609816
+93  parent 102.505051  0.03288723
+94  parent 103.696970  0.02996191
+95  parent 104.888889  0.02729679
+96  parent 106.080808  0.02486874
+97  parent 107.272727  0.02265667
+98  parent 108.464646  0.02064136
+99  parent 109.656566  0.01880531
+100 parent 110.848485  0.01713257
+101 parent 112.040404  0.01560863
+102 parent 113.232323  0.01422024
+103 parent 114.424242  0.01295535
+104 parent 115.616162  0.01180297
+105 parent 116.808081  0.01075310
+106 parent 118.000000  0.00979661
+
+$cost
+function (P) 
+{
+    assign("calls", calls + 1, inherits = TRUE)
+    if (trace_parms) 
+        cat(P, "\n")
+    if (length(state.ini.optim) > 0) {
+        odeini <- c(P[1:length(state.ini.optim)], state.ini.fixed)
+        names(odeini) <- c(state.ini.optim.boxnames, state.ini.fixed.boxnames)
+    }
+    else {
+        odeini <- state.ini.fixed
+        names(odeini) <- state.ini.fixed.boxnames
+    }
+    odeparms <- c(P[(length(state.ini.optim) + 1):length(P)], 
+        transparms.fixed)
+    parms <- backtransform_odeparms(odeparms, mkinmod, transform_rates = transform_rates, 
+        transform_fractions = transform_fractions)
+    out <- mkinpredict(mkinmod, parms, odeini, outtimes, solution_type = solution_type, 
+        use_compiled = use_compiled, method.ode = method.ode, 
+        atol = atol, rtol = rtol, ...)
+    assign("out_predicted", out, inherits = TRUE)
+    mC <- modCost(out, observed, y = "value", err = err, weight = weight, 
+        scaleVar = scaleVar)
+    if (mC$model < cost.old) {
+        if (!quiet) 
+            cat("Model cost at call ", calls, ": ", mC$model, 
+                "\n")
+        if (plot) {
+            outtimes_plot = seq(min(observed$time), max(observed$time), 
+                length.out = 100)
+            out_plot <- mkinpredict(mkinmod, parms, odeini, outtimes_plot, 
+                solution_type = solution_type, use_compiled = use_compiled, 
+                method.ode = method.ode, atol = atol, rtol = rtol, 
+                ...)
+            plot(0, type = "n", xlim = range(observed$time), 
+                ylim = c(0, max(observed$value, na.rm = TRUE)), 
+                xlab = "Time", ylab = "Observed")
+            col_obs <- pch_obs <- 1:length(obs_vars)
+            lty_obs <- rep(1, length(obs_vars))
+            names(col_obs) <- names(pch_obs) <- names(lty_obs) <- obs_vars
+            for (obs_var in obs_vars) {
+                points(subset(observed, name == obs_var, c(time, 
+                  value)), pch = pch_obs[obs_var], col = col_obs[obs_var])
+            }
+            matlines(out_plot$time, out_plot[-1], col = col_obs, 
+                lty = lty_obs)
+            legend("topright", inset = c(0.05, 0.05), legend = obs_vars, 
+                col = col_obs, pch = pch_obs, lty = 1:length(pch_obs))
+        }
+        assign("cost.old", mC$model, inherits = TRUE)
+    }
+    return(mC)
+}
+<environment: 0x43a7a48>
+
+$cost_notrans
+function (P) 
+{
+    if (length(state.ini.optim) > 0) {
+        odeini <- c(P[1:length(state.ini.optim)], state.ini.fixed)
+        names(odeini) <- c(state.ini.optim.boxnames, state.ini.fixed.boxnames)
+    }
+    else {
+        odeini <- state.ini.fixed
+        names(odeini) <- state.ini.fixed.boxnames
+    }
+    odeparms <- c(P[(length(state.ini.optim) + 1):length(P)], 
+        parms.fixed)
+    out <- mkinpredict(mkinmod, odeparms, odeini, outtimes, solution_type = solution_type, 
+        use_compiled = use_compiled, method.ode = method.ode, 
+        atol = atol, rtol = rtol, ...)
+    mC <- modCost(out, observed, y = "value", err = err, weight = weight, 
+        scaleVar = scaleVar)
+    return(mC)
+}
+<environment: 0x43a7a48>
+
+$hessian_notrans
+                  parent_0 k_parent_sink
+parent_0          4.163631     -1203.894
+k_parent_sink -1203.893702   1033188.753
+
+$start
+              value   type
+parent_0      98.62  state
+k_parent_sink  0.10 deparm
+
+$start_transformed
+                      value lower upper
+parent_0          98.620000  -Inf   Inf
+log_k_parent_sink -2.302585  -Inf   Inf
+
+$fixed
+[1] value type 
+<0 rows> (or 0-length row.names)
+
+$data
+  time variable observed   predicted    residual
+1    0   parent    98.62 99.17407218 -0.55407218
+2    3   parent    81.43 78.44547872  2.98452128
+3    7   parent    53.18 57.38445742 -4.20445742
+4   14   parent    34.89 33.20400061  1.68599939
+5   30   parent    10.09  9.50814643  0.58185357
+6   62   parent     1.50  0.77966270  0.72033730
+7   90   parent     0.33  0.08739595  0.24260405
+8  118   parent     0.08  0.00979661  0.07020339
+
+$atol
+[1] 1e-08
+
+$rtol
+[1] 1e-10
+
+$weight.ini
+[1] "none"
+
+$reweight.tol
+[1] 1e-08
+
+$reweight.max.iter
+[1] 10
+
+$bparms.optim
+     parent_0 k_parent_sink 
+  99.17407218    0.07815759 
+
+$bparms.fixed
+numeric(0)
+
+$bparms.ode
+k_parent_sink 
+   0.07815759 
+
+$bparms.state
+  parent 
+99.17407 
+
+$date
+[1] "Sat Sep 10 05:52:53 2016"
+
+attr(,"class")
+[1] "mkinfit" "modFit" 
+

+
  fits["SFO", "B", drop = TRUE]

+
[[1]]
+$par
+         parent_0 log_k_parent_sink 
+        99.174072         -2.549028 
+
+$ssr
+[1] 30.65564
+
+$convergence
+[1] 0
+
+$iterations
+[1] 5
+
+$evaluations
+function gradient 
+       8       15 
+
+$counts
+[1] "relative convergence (4)"
+
+$hessian
+                    parent_0 log_k_parent_sink
+parent_0            4.163631         -94.09343
+log_k_parent_sink -94.093431        6311.34610
+
+$residuals
+     parent      parent      parent      parent      parent      parent 
+ 0.55407218 -2.98452128  4.20445742 -1.68599939 -0.58185357 -0.72033730 
+     parent      parent 
+-0.24260405 -0.07020339 
+
+$ms
+[1] 3.831956
+
+$var_ms
+  parent 
+3.831956 
+
+$var_ms_unscaled
+  parent 
+3.831956 
+
+$var_ms_unweighted
+  parent 
+3.831956 
+
+$rank
+[1] 2
+
+$df.residual
+[1] 6
+
+$solution_type
+[1] "analytical"
+
+$transform_rates
+[1] TRUE
+
+$transform_fractions
+[1] TRUE
+
+$method.modFit
+[1] "Port"
+
+$maxit.modFit
+[1] "auto"
+
+$calls
+[1] 29
+
+$time
+   user  system elapsed 
+  0.108   0.000   0.106 
+
+$mkinmod
+<mkinmod> model generated with
+Use of formation fractions $use_of_ff: min 
+Specification $spec:
+$parent
+$type: SFO; $sink: TRUE
+Coefficient matrix $coefmat available
+
+$observed
+    name time value
+1 parent    0 98.62
+2 parent    3 81.43
+3 parent    7 53.18
+4 parent   14 34.89
+5 parent   30 10.09
+6 parent   62  1.50
+7 parent   90  0.33
+8 parent  118  0.08
+
+$obs_vars
+[1] "parent"
+
+$predicted
+      name       time       value
+1   parent   0.000000 99.17407218
+2   parent   1.191919 90.35253561
+3   parent   2.383838 82.31567498
+4   parent   3.000000 78.44547872
+5   parent   3.575758 74.99369333
+6   parent   4.767677 68.32300215
+7   parent   5.959596 62.24566915
+8   parent   7.000000 57.38445742
+9   parent   7.151515 56.70891509
+10  parent   8.343434 51.66465547
+11  parent   9.535354 47.06908288
+12  parent  10.727273 42.88228661
+13  parent  11.919192 39.06790599
+14  parent  13.111111 35.59281463
+15  parent  14.000000 33.20400061
+16  parent  14.303030 32.42683275
+17  parent  15.494949 29.54246504
+18  parent  16.686869 26.91466193
+19  parent  17.878788 24.52060198
+20  parent  19.070707 22.33949373
+21  parent  20.262626 20.35239512
+22  parent  21.454545 18.54204899
+23  parent  22.646465 16.89273320
+24  parent  23.838384 15.39012410
+25  parent  25.030303 14.02117212
+26  parent  26.222222 12.77398846
+27  parent  27.414141 11.63774182
+28  parent  28.606061 10.60256435
+29  parent  29.797980  9.65946594
+30  parent  30.000000  9.50814643
+31  parent  30.989899  8.80025617
+32  parent  32.181818  8.01747313
+33  parent  33.373737  7.30431867
+34  parent  34.565657  6.65459931
+35  parent  35.757576  6.06267251
+36  parent  36.949495  5.52339762
+37  parent  38.141414  5.03209124
+38  parent  39.333333  4.58448658
+39  parent  40.525253  4.17669637
+40  parent  41.717172  3.80517911
+41  parent  42.909091  3.46670832
+42  parent  44.101010  3.15834451
+43  parent  45.292929  2.87740968
+44  parent  46.484848  2.62146400
+45  parent  47.676768  2.38828471
+46  parent  48.868687  2.17584671
+47  parent  50.060606  1.98230508
+48  parent  51.252525  1.80597899
+49  parent  52.444444  1.64533711
+50  parent  53.636364  1.49898432
+51  parent  54.828283  1.36564963
+52  parent  56.020202  1.24417505
+53  parent  57.212121  1.13350565
+54  parent  58.404040  1.03268029
+55  parent  59.595960  0.94082335
+56  parent  60.787879  0.85713708
+57  parent  61.979798  0.78089471
+58  parent  62.000000  0.77966270
+59  parent  63.171717  0.71143411
+60  parent  64.363636  0.64815202
+61  parent  65.555556  0.59049888
+62  parent  66.747475  0.53797399
+63  parent  67.939394  0.49012119
+64  parent  69.131313  0.44652489
+65  parent  70.323232  0.40680649
+66  parent  71.515152  0.37062104
+67  parent  72.707071  0.33765429
+68  parent  73.898990  0.30761993
+69  parent  75.090909  0.28025713
+70  parent  76.282828  0.25532825
+71  parent  77.474747  0.23261679
+72  parent  78.666667  0.21192552
+73  parent  79.858586  0.19307474
+74  parent  81.050505  0.17590074
+75  parent  82.242424  0.16025436
+76  parent  83.434343  0.14599973
+77  parent  84.626263  0.13301305
+78  parent  85.818182  0.12118154
+79  parent  87.010101  0.11040244
+80  parent  88.202020  0.10058214
+81  parent  89.393939  0.09163535
+82  parent  90.000000  0.08739595
+83  parent  90.585859  0.08348439
+84  parent  91.777778  0.07605845
+85  parent  92.969697  0.06929305
+86  parent  94.161616  0.06312943
+87  parent  95.353535  0.05751406
+88  parent  96.545455  0.05239819
+89  parent  97.737374  0.04773737
+90  parent  98.929293  0.04349113
+91  parent 100.121212  0.03962259
+92  parent 101.313131  0.03609816
+93  parent 102.505051  0.03288723
+94  parent 103.696970  0.02996191
+95  parent 104.888889  0.02729679
+96  parent 106.080808  0.02486874
+97  parent 107.272727  0.02265667
+98  parent 108.464646  0.02064136
+99  parent 109.656566  0.01880531
+100 parent 110.848485  0.01713257
+101 parent 112.040404  0.01560863
+102 parent 113.232323  0.01422024
+103 parent 114.424242  0.01295535
+104 parent 115.616162  0.01180297
+105 parent 116.808081  0.01075310
+106 parent 118.000000  0.00979661
+
+$cost
+function (P) 
+{
+    assign("calls", calls + 1, inherits = TRUE)
+    if (trace_parms) 
+        cat(P, "\n")
+    if (length(state.ini.optim) > 0) {
+        odeini <- c(P[1:length(state.ini.optim)], state.ini.fixed)
+        names(odeini) <- c(state.ini.optim.boxnames, state.ini.fixed.boxnames)
+    }
+    else {
+        odeini <- state.ini.fixed
+        names(odeini) <- state.ini.fixed.boxnames
+    }
+    odeparms <- c(P[(length(state.ini.optim) + 1):length(P)], 
+        transparms.fixed)
+    parms <- backtransform_odeparms(odeparms, mkinmod, transform_rates = transform_rates, 
+        transform_fractions = transform_fractions)
+    out <- mkinpredict(mkinmod, parms, odeini, outtimes, solution_type = solution_type, 
+        use_compiled = use_compiled, method.ode = method.ode, 
+        atol = atol, rtol = rtol, ...)
+    assign("out_predicted", out, inherits = TRUE)
+    mC <- modCost(out, observed, y = "value", err = err, weight = weight, 
+        scaleVar = scaleVar)
+    if (mC$model < cost.old) {
+        if (!quiet) 
+            cat("Model cost at call ", calls, ": ", mC$model, 
+                "\n")
+        if (plot) {
+            outtimes_plot = seq(min(observed$time), max(observed$time), 
+                length.out = 100)
+            out_plot <- mkinpredict(mkinmod, parms, odeini, outtimes_plot, 
+                solution_type = solution_type, use_compiled = use_compiled, 
+                method.ode = method.ode, atol = atol, rtol = rtol, 
+                ...)
+            plot(0, type = "n", xlim = range(observed$time), 
+                ylim = c(0, max(observed$value, na.rm = TRUE)), 
+                xlab = "Time", ylab = "Observed")
+            col_obs <- pch_obs <- 1:length(obs_vars)
+            lty_obs <- rep(1, length(obs_vars))
+            names(col_obs) <- names(pch_obs) <- names(lty_obs) <- obs_vars
+            for (obs_var in obs_vars) {
+                points(subset(observed, name == obs_var, c(time, 
+                  value)), pch = pch_obs[obs_var], col = col_obs[obs_var])
+            }
+            matlines(out_plot$time, out_plot[-1], col = col_obs, 
+                lty = lty_obs)
+            legend("topright", inset = c(0.05, 0.05), legend = obs_vars, 
+                col = col_obs, pch = pch_obs, lty = 1:length(pch_obs))
+        }
+        assign("cost.old", mC$model, inherits = TRUE)
+    }
+    return(mC)
+}
+<environment: 0x43a7a48>
+
+$cost_notrans
+function (P) 
+{
+    if (length(state.ini.optim) > 0) {
+        odeini <- c(P[1:length(state.ini.optim)], state.ini.fixed)
+        names(odeini) <- c(state.ini.optim.boxnames, state.ini.fixed.boxnames)
+    }
+    else {
+        odeini <- state.ini.fixed
+        names(odeini) <- state.ini.fixed.boxnames
+    }
+    odeparms <- c(P[(length(state.ini.optim) + 1):length(P)], 
+        parms.fixed)
+    out <- mkinpredict(mkinmod, odeparms, odeini, outtimes, solution_type = solution_type, 
+        use_compiled = use_compiled, method.ode = method.ode, 
+        atol = atol, rtol = rtol, ...)
+    mC <- modCost(out, observed, y = "value", err = err, weight = weight, 
+        scaleVar = scaleVar)
+    return(mC)
+}
+<environment: 0x43a7a48>
+
+$hessian_notrans
+                  parent_0 k_parent_sink
+parent_0          4.163631     -1203.894
+k_parent_sink -1203.893702   1033188.753
+
+$start
+              value   type
+parent_0      98.62  state
+k_parent_sink  0.10 deparm
+
+$start_transformed
+                      value lower upper
+parent_0          98.620000  -Inf   Inf
+log_k_parent_sink -2.302585  -Inf   Inf
+
+$fixed
+[1] value type 
+<0 rows> (or 0-length row.names)
+
+$data
+  time variable observed   predicted    residual
+1    0   parent    98.62 99.17407218 -0.55407218
+2    3   parent    81.43 78.44547872  2.98452128
+3    7   parent    53.18 57.38445742 -4.20445742
+4   14   parent    34.89 33.20400061  1.68599939
+5   30   parent    10.09  9.50814643  0.58185357
+6   62   parent     1.50  0.77966270  0.72033730
+7   90   parent     0.33  0.08739595  0.24260405
+8  118   parent     0.08  0.00979661  0.07020339
+
+$atol
+[1] 1e-08
+
+$rtol
+[1] 1e-10
+
+$weight.ini
+[1] "none"
+
+$reweight.tol
+[1] 1e-08
+
+$reweight.max.iter
+[1] 10
+
+$bparms.optim
+     parent_0 k_parent_sink 
+  99.17407218    0.07815759 
+
+$bparms.fixed
+numeric(0)
+
+$bparms.ode
+k_parent_sink 
+   0.07815759 
+
+$bparms.state
+  parent 
+99.17407 
+
+$date
+[1] "Sat Sep 10 05:52:53 2016"
+
+attr(,"class")
+[1] "mkinfit" "modFit" 
+
+
+
+
+ + + + +

Author

+ + Johannes Ranke + + +
+
+ + +
+ + \ No newline at end of file diff --git a/docs/FOCUS_2006_DFOP_ref_A_to_B.html b/docs/FOCUS_2006_DFOP_ref_A_to_B.html new file mode 100644 index 00000000..6e1c4986 --- /dev/null +++ b/docs/FOCUS_2006_DFOP_ref_A_to_B.html @@ -0,0 +1,133 @@ + + + + +FOCUS_2006_DFOP_ref_A_to_B. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
+
+
+ mkin 0.9.44.9000 +
+ +
+
+
+
+ + +
+
+ +
+ +

+Results of fitting the DFOP model to Datasets A to B of FOCUS (2006) +

+ +
+
+

Usage

+ 
data(FOCUS_2006_DFOP_ref_A_to_B)
+ +
+

Description

+ +

A table with the fitted parameters and the resulting DT50 and DT90 values +generated with different software packages. Taken directly from FOCUS (2006). +The results from fitting the data with the Topfit software was removed, as +the initial concentration of the parent compound was fixed to a value of 100 +in this fit.

+ +
+ +
+

Format

+ +

A data frame containing the following variables. +

+
package
a factor giving the name of the software package

+ +

M0
The fitted initial concentration of the parent compound

+ +

f
The fitted f parameter

+ +

k1
The fitted k1 parameter

+ +

k2
The fitted k2 parameter

+ +

DT50
The resulting half-life of the parent compound

+ +

DT90
The resulting DT90 of the parent compound

+ +

dataset
The FOCUS dataset that was used

+ +

+ +

+ +
+ +
+

Source

+ +

FOCUS (2006) “Guidance Document on Estimating Persistence and + Degradation Kinetics from Environmental Fate Studies on Pesticides in EU + Registration” Report of the FOCUS Work Group on Degradation Kinetics, + EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, + http://focus.jrc.ec.europa.eu/dk

+ +
+ +

Examples

+ 
data(FOCUS_2006_DFOP_ref_A_to_B)
+
+
+ + + + + +
+
+ + +
+ + \ No newline at end of file diff --git a/docs/FOCUS_2006_FOMC_ref_A_to_F.html b/docs/FOCUS_2006_FOMC_ref_A_to_F.html new file mode 100644 index 00000000..580cdb88 --- /dev/null +++ b/docs/FOCUS_2006_FOMC_ref_A_to_F.html @@ -0,0 +1,131 @@ + + + + +FOCUS_2006_FOMC_ref_A_to_F. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
+
+
+ mkin 0.9.44.9000 +
+ +
+
+
+
+ + +
+
+ +
+ +

+Results of fitting the FOMC model to Datasets A to F of FOCUS (2006) +

+ +
+
+

Usage

+ 
data(FOCUS_2006_FOMC_ref_A_to_F)
+ +
+

Description

+ +

A table with the fitted parameters and the resulting DT50 and DT90 values +generated with different software packages. Taken directly from FOCUS (2006). +The results from fitting the data with the Topfit software was removed, as +the initial concentration of the parent compound was fixed to a value of 100 +in this fit.

+ +
+ +
+

Format

+ +

A data frame containing the following variables. +

+
package
a factor giving the name of the software package

+ +

M0
The fitted initial concentration of the parent compound

+ +

alpha
The fitted alpha parameter

+ +

beta
The fitted beta parameter

+ +

DT50
The resulting half-life of the parent compound

+ +

DT90
The resulting DT90 of the parent compound

+ +

dataset
The FOCUS dataset that was used

+ +

+ +

+ +
+ +
+

Source

+ +

FOCUS (2006) “Guidance Document on Estimating Persistence and + Degradation Kinetics from Environmental Fate Studies on Pesticides in EU + Registration” Report of the FOCUS Work Group on Degradation Kinetics, + EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, + http://focus.jrc.ec.europa.eu/dk

+ +
+ +

Examples

+ 
data(FOCUS_2006_FOMC_ref_A_to_F)
+
+
+ + + + + +
+
+ + +
+ + \ No newline at end of file diff --git a/docs/FOCUS_2006_HS_ref_A_to_F.html b/docs/FOCUS_2006_HS_ref_A_to_F.html new file mode 100644 index 00000000..65c11f59 --- /dev/null +++ b/docs/FOCUS_2006_HS_ref_A_to_F.html @@ -0,0 +1,133 @@ + + + + +FOCUS_2006_HS_ref_A_to_F. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
+
+
+ mkin 0.9.44.9000 +
+ +
+
+
+
+ + +
+
+ +
+ +

+Results of fitting the HS model to Datasets A to F of FOCUS (2006) +

+ +
+
+

Usage

+ 
data(FOCUS_2006_HS_ref_A_to_F)
+ +
+

Description

+ +

A table with the fitted parameters and the resulting DT50 and DT90 values +generated with different software packages. Taken directly from FOCUS (2006). +The results from fitting the data with the Topfit software was removed, as +the initial concentration of the parent compound was fixed to a value of 100 +in this fit.

+ +
+ +
+

Format

+ +

A data frame containing the following variables. +

+
package
a factor giving the name of the software package

+ +

M0
The fitted initial concentration of the parent compound

+ +

tb
The fitted tb parameter

+ +

k1
The fitted k1 parameter

+ +

k2
The fitted k2 parameter

+ +

DT50
The resulting half-life of the parent compound

+ +

DT90
The resulting DT90 of the parent compound

+ +

dataset
The FOCUS dataset that was used

+ +

+ +

+ +
+ +
+

Source

+ +

FOCUS (2006) “Guidance Document on Estimating Persistence and + Degradation Kinetics from Environmental Fate Studies on Pesticides in EU + Registration” Report of the FOCUS Work Group on Degradation Kinetics, + EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, + http://focus.jrc.ec.europa.eu/dk

+ +
+ +

Examples

+ 
data(FOCUS_2006_HS_ref_A_to_F)
+
+
+ + + + + +
+
+ + +
+ + \ No newline at end of file diff --git a/docs/FOCUS_2006_SFO_ref_A_to_F.html b/docs/FOCUS_2006_SFO_ref_A_to_F.html new file mode 100644 index 00000000..ab33a3a5 --- /dev/null +++ b/docs/FOCUS_2006_SFO_ref_A_to_F.html @@ -0,0 +1,129 @@ + + + + +FOCUS_2006_SFO_ref_A_to_F. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
+
+
+ mkin 0.9.44.9000 +
+ +
+
+
+
+ + +
+
+ +
+ +

+Results of fitting the SFO model to Datasets A to F of FOCUS (2006) +

+ +
+
+

Usage

+ 
data(FOCUS_2006_SFO_ref_A_to_F)
+ +
+

Description

+ +

A table with the fitted parameters and the resulting DT50 and DT90 values +generated with different software packages. Taken directly from FOCUS (2006). +The results from fitting the data with the Topfit software was removed, as +the initial concentration of the parent compound was fixed to a value of 100 +in this fit.

+ +
+ +
+

Format

+ +

A data frame containing the following variables. +

+
package
a factor giving the name of the software package

+ +

M0
The fitted initial concentration of the parent compound

+ +

k
The fitted first-order degradation rate constant

+ +

DT50
The resulting half-life of the parent compound

+ +

DT90
The resulting DT90 of the parent compound

+ +

dataset
The FOCUS dataset that was used

+ +

+ +

+ +
+ +
+

Source

+ +

FOCUS (2006) “Guidance Document on Estimating Persistence and + Degradation Kinetics from Environmental Fate Studies on Pesticides in EU + Registration” Report of the FOCUS Work Group on Degradation Kinetics, + EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, + http://focus.jrc.ec.europa.eu/dk

+ +
+ +

Examples

+ 
data(FOCUS_2006_SFO_ref_A_to_F)
+
+
+ + + + + +
+
+ + +
+ + \ No newline at end of file diff --git a/docs/FOCUS_2006_datasets.html b/docs/FOCUS_2006_datasets.html new file mode 100644 index 00000000..4de4d54e --- /dev/null +++ b/docs/FOCUS_2006_datasets.html @@ -0,0 +1,130 @@ + + + + +FOCUS_2006_datasets. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
+
+
+ mkin 0.9.44.9000 +
+ +
+
+
+
+ + +
+
+ +
+ +

+Datasets A to F from the FOCUS Kinetics report from 2006 +

+ +
+
+

Usage

+ 
FOCUS_2006_datasets
+ +
+

Description

+ +

Data taken from FOCUS (2006), p. 258.

+ +
+ +
+

Format

+ +

6 datasets with observations on the following variables. +

+
name
a factor containing the name of the observed variable

+ +

time
a numeric vector containing time points

+ +

value
a numeric vector containing concentrations in percent of applied radioactivity

+ +

+ +

+ +
+ +
+

Source

+ +

FOCUS (2006) “Guidance Document on Estimating Persistence and + Degradation Kinetics from Environmental Fate Studies on Pesticides in EU + Registration” Report of the FOCUS Work Group on Degradation Kinetics, + EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, + http://focus.jrc.ec.europa.eu/dk

+ +
+ +

Examples

+ 
FOCUS_2006_C

+
    name time value
+1 parent    0  85.1
+2 parent    1  57.9
+3 parent    3  29.9
+4 parent    7  14.6
+5 parent   14   9.7
+6 parent   28   6.6
+7 parent   63   4.0
+8 parent   91   3.9
+9 parent  119   0.6
+
+
+
+ + + + + +
+
+ + +
+ + \ No newline at end of file diff --git a/docs/FOMC.solution-2.png b/docs/FOMC.solution-2.png new file mode 100644 index 00000000..0efe8f07 Binary files /dev/null and b/docs/FOMC.solution-2.png differ diff --git a/docs/FOMC.solution.html b/docs/FOMC.solution.html new file mode 100644 index 00000000..fe8b875d --- /dev/null +++ b/docs/FOMC.solution.html @@ -0,0 +1,145 @@ + + + + +FOMC.solution. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
+
+
+ mkin 0.9.44.9000 +
+ +
+
+
+
+ + +
+
+ +
+ +

First-Order Multi-Compartment kinetics

+ +
+
+

Usage

+ 
FOMC.solution(t, parent.0, alpha, beta)
+ +

Arguments

+
+
t
+
Time.
+
parent.0
+
Starting value for the response variable at time zero.
+
alpha
+
+ Shape parameter determined by coefficient of variation of rate constant + values.
+
beta
+
+ Location parameter. +
+
+ +
+

Description

+ +

Function describing exponential decline from a defined starting value, with + a decreasing rate constant.

+ +

The form given here differs slightly from the original reference by Gustafson + and Holden (1990). The parameter beta corresponds to 1/beta in the + original equation.

+ +
+ +
+

Note

+ +

The solution of the FOMC kinetic model reduces to the + SFO.solution for large values of alpha and + beta with + $k = beta/alpha$.

+ +
+ +
+

Value

+ +

+ The value of the response variable at time t. +

+ +
+ +
+

References

+ +

FOCUS (2006) “Guidance Document on Estimating Persistence and + Degradation Kinetics from Environmental Fate Studies on Pesticides in EU + Registration” Report of the FOCUS Work Group on Degradation Kinetics, + EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, + http://focus.jrc.ec.europa.eu/dk

+ +

Gustafson DI and Holden LR (1990) Nonlinear pesticide dissipation in soil: A + new model based on spatial variability. Environmental Science and + Technology 24, 1032-1038

+ +
+ +

Examples

+ 
  plot(function(x) FOMC.solution(x, 100, 10, 2), 0, 2, ylim = c(0, 100))

+
+
+
+ + + + + +
+
+ + +
+ + \ No newline at end of file diff --git a/docs/HS.solution-2.png b/docs/HS.solution-2.png new file mode 100644 index 00000000..0bdb37a9 Binary files /dev/null and b/docs/HS.solution-2.png differ diff --git a/docs/HS.solution.html b/docs/HS.solution.html new file mode 100644 index 00000000..836a6063 --- /dev/null +++ b/docs/HS.solution.html @@ -0,0 +1,127 @@ + + + + +HS.solution. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
+
+
+ mkin 0.9.44.9000 +
+ +
+
+
+
+ + +
+
+ +
+ +

Hockey-Stick kinetics

+ +
+
+

Usage

+ 
HS.solution(t, parent.0, k1, k2, tb)
+ +

Arguments

+
+
t
+
Time.
+
parent.0
+
Starting value for the response variable at time zero.
+
k1
+
First kinetic constant.
+
k2
+
Second kinetic constant.
+
tb
+
Break point. Before this time, exponential decline according + to k1 is calculated, after this time, exponential decline proceeds + according to k2.
+
+ +
+

Description

+ +

Function describing two exponential decline functions with a break point + between them.

+ +
+ +
+

Value

+ +

+ The value of the response variable at time t. +

+ +
+ +
+

References

+ +

FOCUS (2006) “Guidance Document on Estimating Persistence and + Degradation Kinetics from Environmental Fate Studies on Pesticides in EU + Registration” Report of the FOCUS Work Group on Degradation Kinetics, + EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, + http://focus.jrc.ec.europa.eu/dk

+ +
+ +

Examples

+ 
  plot(function(x) HS.solution(x, 100, 2, 0.3, 0.5), 0, 2, ylim=c(0,100))

+
+
+
+ + + + + +
+
+ + +
+ + \ No newline at end of file diff --git a/docs/IORE.solution-2.png b/docs/IORE.solution-2.png new file mode 100644 index 00000000..df268b64 Binary files /dev/null and b/docs/IORE.solution-2.png differ diff --git a/docs/IORE.solution.html b/docs/IORE.solution.html new file mode 100644 index 00000000..df94ef72 --- /dev/null +++ b/docs/IORE.solution.html @@ -0,0 +1,150 @@ + + + + +IORE.solution. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
+
+
+ mkin 0.9.44.9000 +
+ +
+
+
+
+ + +
+
+ +
+ +

Indeterminate order rate equation kinetics

+ +
+
+

Usage

+ 
IORE.solution(t, parent.0, k__iore, N)
+ +

Arguments

+
+
t
+
Time.
+
parent.0
+
Starting value for the response variable at time zero.
+
k__iore
+
Rate constant. Note that this depends on the concentration units used.
+
N
+
Exponent describing the nonlinearity of the rate equation
+
+ +
+

Description

+ +

Function describing exponential decline from a defined starting value, with + a concentration dependent rate constant.

+ +
+ +
+

Note

+ +

The solution of the IORE kinetic model reduces to the + SFO.solution if N = 1. + The parameters of the IORE model can be transformed to equivalent parameters + of the FOMC mode - see the NAFTA guidance for details.

+ +
+ +
+

Value

+ +

+ The value of the response variable at time t. +

+ +
+ +
+

References

+ +

NAFTA Technical Working Group on Pesticides (not dated) Guidance for + Evaluating and Calculating Degradation Kinetics in Environmental + Media

+ +
+ +

Examples

+ 
  plot(function(x) IORE.solution(x, 100, 0.2, 1.3), 0, 2,
+                                       ylim = c(0, 100))

+

+
  fit.fomc <- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE)
+  fit.iore <- mkinfit("IORE", FOCUS_2006_C, quiet = TRUE)
+  fit.iore.deS <- mkinfit("IORE", FOCUS_2006_C, solution_type = "deSolve", quiet = TRUE)
+
+  print(data.frame(coef(fit.fomc), coef(fit.iore), coef(fit.iore.deS),
+                   row.names = paste("model par", 1:3)))

+
            coef.fit.fomc. coef.fit.iore. coef.fit.iore.deS.
+model par 1    85.87489063      85.874891          85.874890
+model par 2     0.05192238      -4.826631          -4.826631
+model par 3     0.65096665       1.949403           1.949403
+

+
  print(rbind(fomc = endpoints(fit.fomc)$distimes, iore = endpoints(fit.iore)$distimes,
+              iore.deS = endpoints(fit.iore)$distimes))

+
             DT50    DT90 DT50back
+fomc     1.785233 15.1479 4.559973
+iore     1.785233 15.1479 4.559973
+iore.deS 1.785233 15.1479 4.559973
+
+
+
+ + + + + +
+
+ + +
+ + \ No newline at end of file diff --git a/docs/SFO.solution.html b/docs/SFO.solution.html new file mode 100644 index 00000000..2321f15b --- /dev/null +++ b/docs/SFO.solution.html @@ -0,0 +1,119 @@ + + + + +SFO.solution. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
+
+
+ mkin 0.9.44.9000 +
+ +
+
+
+
+ + +
+
+ +
+ +

Single First-Order kinetics

+ +
+
+

Usage

+ 
SFO.solution(t, parent.0, k)
+ +

Arguments

+
+
t
+
Time.
+
parent.0
+
Starting value for the response variable at time zero.
+
k
+
Kinetic constant.
+
+ +
+

Description

+ +

Function describing exponential decline from a defined starting value.

+ +
+ +
+

Value

+ +

+ The value of the response variable at time t. +

+ +
+ +
+

References

+ +

FOCUS (2006) “Guidance Document on Estimating Persistence and + Degradation Kinetics from Environmental Fate Studies on Pesticides in EU + Registration” Report of the FOCUS Work Group on Degradation Kinetics, + EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, + http://focus.jrc.ec.europa.eu/dk

+ +
+ +

Examples

+ 
  ## Not run: plot(function(x) SFO.solution(x, 100, 3), 0, 2)
+
+
+ + + + + +
+
+ + +
+ + \ No newline at end of file diff --git a/docs/SFORB.solution.html b/docs/SFORB.solution.html new file mode 100644 index 00000000..6926b770 --- /dev/null +++ b/docs/SFORB.solution.html @@ -0,0 +1,128 @@ + + + + +SFORB.solution. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
+
+
+ mkin 0.9.44.9000 +
+ +
+
+
+
+ + +
+
+ +
+ +

Single First-Order Reversible Binding kinetics

+ +
+
+

Usage

+ 
SFORB.solution(t, parent.0, k_12, k_21, k_1output)
+ +

Arguments

+
+
t
+
Time.
+
parent.0
+
Starting value for the response variable at time zero.
+
k_12
+
Kinetic constant describing transfer from free to bound.
+
k_21
+
Kinetic constant describing transfer from bound to free.
+
k_1output
+
Kinetic constant describing degradation of the free fraction.
+
+ +
+

Description

+ +

Function describing the solution of the differential equations describing + the kinetic model with first-order terms for a two-way transfer from a free + to a bound fraction, and a first-order degradation term for the free + fraction. The initial condition is a defined amount in the free fraction and + no substance in the bound fraction.

+ +
+ +
+

Value

+ +

+ The value of the response variable, which is the sum of free and bound + fractions at time t. +

+ +
+ +
+

References

+ +

FOCUS (2006) “Guidance Document on Estimating Persistence and + Degradation Kinetics from Environmental Fate Studies on Pesticides in EU + Registration” Report of the FOCUS Work Group on Degradation Kinetics, + EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, + http://focus.jrc.ec.europa.eu/dk

+ +
+ +

Examples

+ 
  ## Not run: plot(function(x) SFORB.solution(x, 100, 0.5, 2, 3), 0, 2)
+
+
+ + + + + +
+
+ + +
+ + \ No newline at end of file diff --git a/docs/add_err-4.png b/docs/add_err-4.png new file mode 100644 index 00000000..f3d04134 Binary files /dev/null and b/docs/add_err-4.png differ diff --git a/docs/add_err-6.png b/docs/add_err-6.png new file mode 100644 index 00000000..05b27247 Binary files /dev/null and b/docs/add_err-6.png differ diff --git a/docs/add_err-8.png b/docs/add_err-8.png new file mode 100644 index 00000000..b0182d2a Binary files /dev/null and b/docs/add_err-8.png differ diff --git a/docs/add_err.html b/docs/add_err.html new file mode 100644 index 00000000..78c69d3e --- /dev/null +++ b/docs/add_err.html @@ -0,0 +1,201 @@ + + + + +add_err. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
+
+
+ mkin 0.9.44.9000 +
+ +
+
+
+
+ + +
+
+ +
+ +

+ Add normally distributed errors to simulated kinetic degradation data +

+ +
+
+

Usage

+ 
add_err(prediction, sdfunc,
+          n = 1000, LOD = 0.1, reps = 2,
+          digits = 1, seed = NA)
+ +

Arguments

+
+
prediction
+
+ A prediction from a kinetic model as produced by mkinpredict. +
+
sdfunc
+
+ A function taking the predicted value as its only argument and returning + a standard deviation that should be used for generating the random error + terms for this value. +
+
n
+
+ The number of datasets to be generated. +
+
LOD
+
+ The limit of detection (LOD). Values that are below the LOD after adding + the random error will be set to NA. +
+
reps
+
+ The number of replicates to be generated within the datasets. +
+
digits
+
+ The number of digits to which the values will be rounded. +
+
seed
+
+ The seed used for the generation of random numbers. If NA, the seed + is not set. +
+
+ +
+

Description

+ +

Normally distributed errors are added to data predicted for a specific + degradation model using mkinpredict. The variance of the error + may depend on the predicted value and is specified as a standard deviation.

+ +
+ +
+

Value

+ +

+ A list of datasets compatible with mmkin, i.e. + the components of the list are datasets compatible with + mkinfit. +

+ +
+ +
+

References

+ +

Ranke J and Lehmann R (2015) To t-test or not to t-test, that is the question. XV Symposium on Pesticide Chemistry 2-4 September 2015, Piacenza, Italy + http://chem.uft.uni-bremen.de/ranke/posters/piacenza_2015.pdf

+ +
+ +

Examples

+ 
# The kinetic model
+m_SFO_SFO <- mkinmod(parent = mkinsub("SFO", "M1"),
+                     M1 = mkinsub("SFO"), use_of_ff = "max")

+Successfully compiled differential equation model from auto-generated C code.
+

+# Generate a prediction for a specific set of parameters
+sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
+
+# This is the prediction used for the "Type 2 datasets" on the Piacenza poster
+# from 2015
+d_SFO_SFO <- mkinpredict(m_SFO_SFO,
+                         c(k_parent = 0.1, f_parent_to_M1 = 0.5,
+                           k_M1 = log(2)/1000),
+                         c(parent = 100, M1 = 0),
+                         sampling_times)
+
+# Add an error term with a constant (independent of the value) standard deviation
+# of 10, and generate three datasets
+d_SFO_SFO_err <- add_err(d_SFO_SFO, function(x) 10, n = 3, seed = 123456789 )
+
+# Name the datasets for nicer plotting
+names(d_SFO_SFO_err) <- paste("Dataset", 1:3)
+
+# Name the model in the list of models (with only one member in this case)
+# for nicer plotting later on.
+# Be quiet and use the faster Levenberg-Marquardt algorithm, as the datasets
+# are easy and examples are run often. Use only one core not to offend CRAN
+# checks
+f_SFO_SFO <- mmkin(list("SFO-SFO" = m_SFO_SFO),
+                   d_SFO_SFO_err, cores = 1,
+                   quiet = TRUE, method.modFit = "Marq")
+
+plot(f_SFO_SFO)

+

+

+# We would like to inspect the fit for dataset 3 more closely
+# Using double brackets makes the returned object an mkinfit object
+# instead of a list of mkinfit objects, so plot.mkinfit is used
+plot(f_SFO_SFO[[3]], show_residuals = TRUE)

+

+

+# If we use single brackets, we should give two indices (model and dataset),
+# and plot.mmkin is used
+plot(f_SFO_SFO[1, 3])

+

+
+
+
+ + + + +

Author

+ + Johannes Ranke + + +
+
+ + +
+ + \ No newline at end of file diff --git a/docs/css/bootstrap-responsive.css b/docs/css/bootstrap-responsive.css new file mode 100644 index 00000000..06e55c0b --- /dev/null +++ b/docs/css/bootstrap-responsive.css @@ -0,0 +1,815 @@ +/*! + * Bootstrap Responsive v2.0.4 + * + * Copyright 2012 Twitter, Inc + * Licensed under the Apache License v2.0 + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Designed and built with all the love in the world @twitter by @mdo and @fat. + */ + +.clearfix { + *zoom: 1; +} + +.clearfix:before, +.clearfix:after { + display: table; + content: ""; +} + +.clearfix:after { + clear: both; +} + +.hide-text { + font: 0/0 a; + color: transparent; + text-shadow: none; + background-color: transparent; + border: 0; +} + +.input-block-level { + display: block; + width: 100%; + min-height: 28px; + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + -ms-box-sizing: border-box; + box-sizing: border-box; +} + +.hidden { + display: none; + visibility: hidden; +} + +.visible-phone { + display: none !important; +} + +.visible-tablet { + display: none !important; +} + +.hidden-desktop { + display: none !important; +} + +@media (max-width: 767px) { + .visible-phone { + display: inherit !important; + } + .hidden-phone { + display: none !important; + } + .hidden-desktop { + display: inherit !important; + } + .visible-desktop { + display: none !important; + } +} + +@media (min-width: 768px) and (max-width: 979px) { + .visible-tablet { + display: inherit !important; + } + .hidden-tablet { + display: none !important; + } + .hidden-desktop { + display: inherit !important; + } + .visible-desktop { + display: none !important ; + } +} + +@media (max-width: 480px) { + .nav-collapse { + -webkit-transform: translate3d(0, 0, 0); + } + .page-header h1 small { + display: block; + line-height: 18px; + } + input[type="checkbox"], + input[type="radio"] { + border: 1px solid #ccc; + } + .form-horizontal .control-group > label { + float: none; + width: 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display: block; + float: left; + width: 100%; + min-height: 28px; + margin-left: 2.762430939%; + *margin-left: 2.709239449638298%; + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + -ms-box-sizing: border-box; + box-sizing: border-box; + } + .row-fluid [class*="span"]:first-child { + margin-left: 0; + } + .row-fluid .span12 { + width: 99.999999993%; + *width: 99.9468085036383%; + } + .row-fluid .span11 { + width: 91.436464082%; + *width: 91.38327259263829%; + } + .row-fluid .span10 { + width: 82.87292817100001%; + *width: 82.8197366816383%; + } + .row-fluid .span9 { + width: 74.30939226%; + *width: 74.25620077063829%; + } + .row-fluid .span8 { + width: 65.74585634900001%; + *width: 65.6926648596383%; + } + .row-fluid .span7 { + width: 57.182320438000005%; + *width: 57.129128948638304%; + } + .row-fluid .span6 { + width: 48.618784527%; + *width: 48.5655930376383%; + } + .row-fluid .span5 { + width: 40.055248616%; + *width: 40.0020571266383%; + } + .row-fluid .span4 { + 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18px; + color: #555555; +} + +input, +textarea { + width: 210px; +} + +textarea { + height: auto; +} + +textarea, +input[type="text"], +input[type="password"], +input[type="datetime"], +input[type="datetime-local"], +input[type="date"], +input[type="month"], +input[type="time"], +input[type="week"], +input[type="number"], +input[type="email"], +input[type="url"], +input[type="search"], +input[type="tel"], +input[type="color"], +.uneditable-input { + background-color: #ffffff; + border: 1px solid #cccccc; + -webkit-border-radius: 3px; + -moz-border-radius: 3px; + border-radius: 3px; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + -webkit-transition: border linear 0.2s, box-shadow linear 0.2s; + -moz-transition: border linear 0.2s, box-shadow linear 0.2s; + -ms-transition: border linear 0.2s, box-shadow linear 0.2s; + -o-transition: border linear 0.2s, box-shadow linear 0.2s; + transition: border linear 0.2s, box-shadow linear 0.2s; +} + +textarea:focus, +input[type="text"]:focus, +input[type="password"]:focus, +input[type="datetime"]:focus, +input[type="datetime-local"]:focus, +input[type="date"]:focus, +input[type="month"]:focus, +input[type="time"]:focus, +input[type="week"]:focus, +input[type="number"]:focus, +input[type="email"]:focus, +input[type="url"]:focus, +input[type="search"]:focus, +input[type="tel"]:focus, +input[type="color"]:focus, +.uneditable-input:focus { + border-color: rgba(82, 168, 236, 0.8); + outline: 0; + outline: thin dotted \9; + /* IE6-9 */ + + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 8px rgba(82, 168, 236, 0.6); + -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 8px rgba(82, 168, 236, 0.6); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 8px rgba(82, 168, 236, 0.6); +} + +input[type="radio"], +input[type="checkbox"] { + margin: 3px 0; + *margin-top: 0; + /* IE7 */ + + line-height: normal; + cursor: pointer; +} + +input[type="submit"], +input[type="reset"], +input[type="button"], +input[type="radio"], +input[type="checkbox"] { + width: auto; +} + +.uneditable-textarea { + width: auto; + height: auto; +} + +select, +input[type="file"] { + height: 28px; + /* In IE7, the height of the select element cannot be changed by height, only font-size */ + + *margin-top: 4px; + /* For IE7, add top margin to align select with labels */ + + line-height: 28px; +} + +select { + width: 220px; + border: 1px solid #bbb; +} + +select[multiple], +select[size] { + height: auto; +} + +select:focus, +input[type="file"]:focus, +input[type="radio"]:focus, +input[type="checkbox"]:focus { + outline: thin dotted #333; + outline: 5px auto -webkit-focus-ring-color; + outline-offset: -2px; +} + +.radio, +.checkbox { + min-height: 18px; + padding-left: 18px; +} + +.radio input[type="radio"], +.checkbox input[type="checkbox"] { + float: left; + margin-left: -18px; +} + +.controls > .radio:first-child, +.controls > .checkbox:first-child { + padding-top: 5px; +} + +.radio.inline, +.checkbox.inline { + display: inline-block; + padding-top: 5px; + margin-bottom: 0; + vertical-align: middle; +} + +.radio.inline + .radio.inline, +.checkbox.inline + .checkbox.inline { + margin-left: 10px; +} + +.input-mini { + width: 60px; +} + +.input-small { + width: 90px; +} + +.input-medium { + width: 150px; +} + +.input-large { + width: 210px; +} + +.input-xlarge { + width: 270px; +} + +.input-xxlarge { + width: 530px; +} + +input[class*="span"], +select[class*="span"], +textarea[class*="span"], +.uneditable-input[class*="span"], +.row-fluid input[class*="span"], +.row-fluid select[class*="span"], +.row-fluid textarea[class*="span"], +.row-fluid .uneditable-input[class*="span"] { + float: none; + margin-left: 0; +} + +.input-append input[class*="span"], +.input-append .uneditable-input[class*="span"], +.input-prepend input[class*="span"], +.input-prepend .uneditable-input[class*="span"], +.row-fluid .input-prepend [class*="span"], +.row-fluid .input-append [class*="span"] { + display: inline-block; +} + +input, +textarea, +.uneditable-input { + margin-left: 0; +} + +input.span12, +textarea.span12, +.uneditable-input.span12 { + width: 930px; +} + +input.span11, +textarea.span11, +.uneditable-input.span11 { + width: 850px; +} + +input.span10, +textarea.span10, +.uneditable-input.span10 { + width: 770px; +} + +input.span9, +textarea.span9, +.uneditable-input.span9 { + width: 690px; +} + +input.span8, +textarea.span8, +.uneditable-input.span8 { + width: 610px; +} + +input.span7, +textarea.span7, +.uneditable-input.span7 { + width: 530px; +} + +input.span6, +textarea.span6, +.uneditable-input.span6 { + width: 450px; +} + +input.span5, +textarea.span5, +.uneditable-input.span5 { + width: 370px; +} + +input.span4, +textarea.span4, +.uneditable-input.span4 { + width: 290px; +} + +input.span3, +textarea.span3, +.uneditable-input.span3 { + width: 210px; +} + +input.span2, +textarea.span2, +.uneditable-input.span2 { + width: 130px; +} + +input.span1, +textarea.span1, +.uneditable-input.span1 { + width: 50px; +} + +input[disabled], +select[disabled], +textarea[disabled], +input[readonly], +select[readonly], +textarea[readonly] { + cursor: not-allowed; + background-color: #eeeeee; + border-color: #ddd; +} + +input[type="radio"][disabled], +input[type="checkbox"][disabled], +input[type="radio"][readonly], +input[type="checkbox"][readonly] { + background-color: transparent; +} + +.control-group.warning > label, +.control-group.warning .help-block, +.control-group.warning .help-inline { + color: #c09853; +} + +.control-group.warning .checkbox, +.control-group.warning .radio, +.control-group.warning input, +.control-group.warning select, +.control-group.warning textarea { + color: #c09853; + border-color: #c09853; +} + +.control-group.warning .checkbox:focus, +.control-group.warning .radio:focus, +.control-group.warning input:focus, +.control-group.warning select:focus, +.control-group.warning textarea:focus { + border-color: #a47e3c; + -webkit-box-shadow: 0 0 6px #dbc59e; + -moz-box-shadow: 0 0 6px #dbc59e; + box-shadow: 0 0 6px #dbc59e; +} + +.control-group.warning .input-prepend .add-on, +.control-group.warning .input-append .add-on { + color: #c09853; + background-color: #fcf8e3; + border-color: #c09853; +} + +.control-group.error > label, +.control-group.error .help-block, +.control-group.error .help-inline { + color: #b94a48; +} + +.control-group.error .checkbox, +.control-group.error .radio, +.control-group.error input, +.control-group.error select, +.control-group.error textarea { + color: #b94a48; + border-color: #b94a48; +} + +.control-group.error .checkbox:focus, +.control-group.error .radio:focus, +.control-group.error input:focus, +.control-group.error select:focus, +.control-group.error textarea:focus { + border-color: #953b39; + -webkit-box-shadow: 0 0 6px #d59392; + -moz-box-shadow: 0 0 6px #d59392; + box-shadow: 0 0 6px #d59392; +} + +.control-group.error .input-prepend .add-on, +.control-group.error .input-append .add-on { + color: #b94a48; + background-color: #f2dede; + border-color: #b94a48; +} + +.control-group.success > label, +.control-group.success .help-block, +.control-group.success .help-inline { + color: #468847; +} + +.control-group.success .checkbox, +.control-group.success .radio, +.control-group.success input, +.control-group.success select, +.control-group.success textarea { + color: #468847; + border-color: #468847; +} + +.control-group.success .checkbox:focus, +.control-group.success .radio:focus, +.control-group.success input:focus, +.control-group.success select:focus, +.control-group.success textarea:focus { + border-color: #356635; + -webkit-box-shadow: 0 0 6px #7aba7b; + -moz-box-shadow: 0 0 6px #7aba7b; + box-shadow: 0 0 6px #7aba7b; +} + +.control-group.success .input-prepend .add-on, +.control-group.success .input-append .add-on { + color: #468847; + background-color: #dff0d8; + border-color: #468847; +} + +input:focus:required:invalid, +textarea:focus:required:invalid, +select:focus:required:invalid { + color: #b94a48; + border-color: #ee5f5b; +} + +input:focus:required:invalid:focus, +textarea:focus:required:invalid:focus, +select:focus:required:invalid:focus { + border-color: #e9322d; + -webkit-box-shadow: 0 0 6px #f8b9b7; + -moz-box-shadow: 0 0 6px #f8b9b7; + box-shadow: 0 0 6px #f8b9b7; +} + +.form-actions { + padding: 17px 20px 18px; + margin-top: 18px; + margin-bottom: 18px; + background-color: #f5f5f5; + border-top: 1px solid #e5e5e5; + *zoom: 1; +} + +.form-actions:before, +.form-actions:after { + display: table; + content: ""; +} + +.form-actions:after { + clear: both; +} + +.uneditable-input { + overflow: hidden; + white-space: nowrap; + cursor: not-allowed; + background-color: #ffffff; + border-color: #eee; + -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.025); + -moz-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.025); + box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.025); +} + +:-moz-placeholder { + color: #999999; +} + +:-ms-input-placeholder { + color: #999999; +} + +::-webkit-input-placeholder { + color: #999999; +} + +.help-block, +.help-inline { + color: #555555; +} + +.help-block { + display: block; + margin-bottom: 9px; +} + +.help-inline { + display: inline-block; + *display: inline; + padding-left: 5px; + vertical-align: middle; + *zoom: 1; +} + +.input-prepend, +.input-append { + margin-bottom: 5px; +} + +.input-prepend input, +.input-append input, +.input-prepend select, +.input-append select, +.input-prepend .uneditable-input, +.input-append .uneditable-input { + position: relative; + margin-bottom: 0; + *margin-left: 0; + vertical-align: middle; + -webkit-border-radius: 0 3px 3px 0; + -moz-border-radius: 0 3px 3px 0; + border-radius: 0 3px 3px 0; +} + +.input-prepend input:focus, +.input-append input:focus, +.input-prepend select:focus, +.input-append select:focus, +.input-prepend .uneditable-input:focus, +.input-append .uneditable-input:focus { + z-index: 2; +} + +.input-prepend .uneditable-input, +.input-append .uneditable-input { + border-left-color: #ccc; +} + +.input-prepend .add-on, +.input-append .add-on { + display: inline-block; + width: auto; + height: 18px; + min-width: 16px; + padding: 4px 5px; + font-weight: normal; + line-height: 18px; + text-align: center; + text-shadow: 0 1px 0 #ffffff; + vertical-align: middle; + background-color: #eeeeee; + border: 1px solid #ccc; +} + +.input-prepend .add-on, +.input-append .add-on, +.input-prepend .btn, +.input-append .btn { + margin-left: -1px; + -webkit-border-radius: 0; + -moz-border-radius: 0; + border-radius: 0; +} + +.input-prepend .active, +.input-append .active { + background-color: #a9dba9; + border-color: #46a546; +} + +.input-prepend .add-on, +.input-prepend .btn { + margin-right: -1px; +} + +.input-prepend .add-on:first-child, +.input-prepend .btn:first-child { + -webkit-border-radius: 3px 0 0 3px; + -moz-border-radius: 3px 0 0 3px; + border-radius: 3px 0 0 3px; +} + +.input-append input, +.input-append select, +.input-append .uneditable-input { + -webkit-border-radius: 3px 0 0 3px; + -moz-border-radius: 3px 0 0 3px; + border-radius: 3px 0 0 3px; +} + +.input-append .uneditable-input { + border-right-color: #ccc; + border-left-color: #eee; +} + +.input-append .add-on:last-child, +.input-append .btn:last-child { + -webkit-border-radius: 0 3px 3px 0; + -moz-border-radius: 0 3px 3px 0; + border-radius: 0 3px 3px 0; +} + +.input-prepend.input-append input, +.input-prepend.input-append select, +.input-prepend.input-append .uneditable-input { + -webkit-border-radius: 0; + -moz-border-radius: 0; + border-radius: 0; +} + +.input-prepend.input-append .add-on:first-child, +.input-prepend.input-append .btn:first-child { + margin-right: -1px; + -webkit-border-radius: 3px 0 0 3px; + -moz-border-radius: 3px 0 0 3px; + border-radius: 3px 0 0 3px; +} + +.input-prepend.input-append .add-on:last-child, +.input-prepend.input-append .btn:last-child { + margin-left: -1px; + -webkit-border-radius: 0 3px 3px 0; + -moz-border-radius: 0 3px 3px 0; + border-radius: 0 3px 3px 0; +} + +.search-query { + padding-right: 14px; + padding-right: 4px \9; + padding-left: 14px; + padding-left: 4px \9; + /* IE7-8 doesn't have border-radius, so don't indent the padding */ + + margin-bottom: 0; + -webkit-border-radius: 14px; + -moz-border-radius: 14px; + border-radius: 14px; +} + +.form-search input, +.form-inline input, +.form-horizontal input, +.form-search textarea, +.form-inline textarea, +.form-horizontal textarea, +.form-search select, +.form-inline select, +.form-horizontal select, +.form-search .help-inline, +.form-inline .help-inline, +.form-horizontal .help-inline, +.form-search .uneditable-input, +.form-inline .uneditable-input, +.form-horizontal .uneditable-input, +.form-search .input-prepend, +.form-inline .input-prepend, +.form-horizontal .input-prepend, +.form-search .input-append, +.form-inline .input-append, +.form-horizontal .input-append { + display: inline-block; + *display: inline; + margin-bottom: 0; + *zoom: 1; +} + +.form-search .hide, +.form-inline .hide, +.form-horizontal .hide { + display: none; +} + +.form-search label, +.form-inline label { + display: inline-block; +} + +.form-search .input-append, +.form-inline .input-append, +.form-search .input-prepend, +.form-inline .input-prepend { + margin-bottom: 0; +} + +.form-search .radio, +.form-search .checkbox, +.form-inline .radio, +.form-inline .checkbox { + padding-left: 0; + margin-bottom: 0; + vertical-align: middle; +} + +.form-search .radio input[type="radio"], +.form-search .checkbox input[type="checkbox"], +.form-inline .radio input[type="radio"], +.form-inline .checkbox input[type="checkbox"] { + float: left; + margin-right: 3px; + margin-left: 0; +} + +.control-group { + margin-bottom: 9px; +} + +legend + .control-group { + margin-top: 18px; + -webkit-margin-top-collapse: separate; +} + +.form-horizontal .control-group { + margin-bottom: 18px; + *zoom: 1; +} + +.form-horizontal .control-group:before, +.form-horizontal .control-group:after { + display: table; + content: ""; +} + +.form-horizontal .control-group:after { + clear: both; +} + +.form-horizontal .control-label { + float: left; + width: 140px; + padding-top: 5px; + text-align: right; +} + +.form-horizontal .controls { + *display: inline-block; + *padding-left: 20px; + margin-left: 160px; + *margin-left: 0; +} + +.form-horizontal .controls:first-child { + *padding-left: 160px; +} + +.form-horizontal .help-block { + margin-top: 9px; + margin-bottom: 0; +} + +.form-horizontal .form-actions { + padding-left: 160px; +} + +table { + max-width: 100%; + background-color: transparent; + border-collapse: collapse; + border-spacing: 0; +} + +.table { + width: 100%; + margin-bottom: 18px; +} + +.table th, +.table td { + padding: 8px; + line-height: 18px; + text-align: left; + vertical-align: top; + border-top: 1px solid #dddddd; +} + +.table th { + font-weight: bold; +} + +.table thead th { + vertical-align: bottom; +} + +.table caption + thead tr:first-child th, +.table caption + thead tr:first-child td, +.table colgroup + thead tr:first-child th, +.table colgroup + thead tr:first-child td, +.table thead:first-child tr:first-child th, +.table thead:first-child tr:first-child td { + border-top: 0; +} + +.table tbody + tbody { + border-top: 2px solid #dddddd; +} + +.table-condensed th, +.table-condensed td { + padding: 4px 5px; +} + +.table-bordered { + border: 1px solid #dddddd; + border-collapse: separate; + *border-collapse: collapsed; + border-left: 0; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; +} + +.table-bordered th, +.table-bordered td { + border-left: 1px solid #dddddd; +} + +.table-bordered caption + thead tr:first-child th, +.table-bordered caption + tbody tr:first-child th, +.table-bordered caption + tbody tr:first-child td, +.table-bordered colgroup + thead tr:first-child th, +.table-bordered colgroup + tbody tr:first-child th, +.table-bordered colgroup + tbody tr:first-child td, +.table-bordered thead:first-child tr:first-child th, +.table-bordered tbody:first-child tr:first-child th, +.table-bordered tbody:first-child tr:first-child td { + border-top: 0; +} + +.table-bordered thead:first-child tr:first-child th:first-child, +.table-bordered tbody:first-child tr:first-child td:first-child { + -webkit-border-top-left-radius: 4px; + border-top-left-radius: 4px; + -moz-border-radius-topleft: 4px; +} + +.table-bordered thead:first-child tr:first-child th:last-child, +.table-bordered tbody:first-child tr:first-child td:last-child { + -webkit-border-top-right-radius: 4px; + border-top-right-radius: 4px; + -moz-border-radius-topright: 4px; +} + +.table-bordered thead:last-child tr:last-child th:first-child, +.table-bordered tbody:last-child tr:last-child td:first-child { + -webkit-border-radius: 0 0 0 4px; + -moz-border-radius: 0 0 0 4px; + border-radius: 0 0 0 4px; + -webkit-border-bottom-left-radius: 4px; + border-bottom-left-radius: 4px; + -moz-border-radius-bottomleft: 4px; +} + +.table-bordered thead:last-child tr:last-child th:last-child, +.table-bordered tbody:last-child tr:last-child td:last-child { + -webkit-border-bottom-right-radius: 4px; + border-bottom-right-radius: 4px; + -moz-border-radius-bottomright: 4px; +} + +.table-striped tbody tr:nth-child(odd) td, +.table-striped tbody tr:nth-child(odd) th { + background-color: #f9f9f9; +} + +.table tbody tr:hover td, +.table tbody tr:hover th { + background-color: #f5f5f5; +} + +table .span1 { + float: none; + width: 44px; + margin-left: 0; +} + +table .span2 { + float: none; + width: 124px; + margin-left: 0; +} + +table .span3 { + float: none; + width: 204px; + margin-left: 0; +} + +table .span4 { + float: none; + width: 284px; + margin-left: 0; +} + +table .span5 { + float: none; + width: 364px; + margin-left: 0; +} + +table .span6 { + float: none; + width: 444px; + margin-left: 0; +} + +table .span7 { + float: none; + width: 524px; + margin-left: 0; +} + +table .span8 { + float: none; + width: 604px; + margin-left: 0; +} + +table .span9 { + float: none; + width: 684px; + margin-left: 0; +} + +table .span10 { + float: none; + width: 764px; + margin-left: 0; +} + +table .span11 { + float: none; + width: 844px; + margin-left: 0; +} + +table .span12 { + float: none; + width: 924px; + margin-left: 0; +} + +table .span13 { + float: none; + width: 1004px; + margin-left: 0; +} + +table .span14 { + float: none; + width: 1084px; + margin-left: 0; +} + +table .span15 { + float: none; + width: 1164px; + margin-left: 0; +} + +table .span16 { + float: none; + width: 1244px; + margin-left: 0; +} + +table .span17 { + float: none; + width: 1324px; + margin-left: 0; +} + +table .span18 { + float: none; + width: 1404px; + margin-left: 0; +} + +table .span19 { + float: none; + width: 1484px; + margin-left: 0; +} + +table .span20 { + float: none; + width: 1564px; + margin-left: 0; +} + +table .span21 { + float: none; + width: 1644px; + margin-left: 0; +} + +table .span22 { + float: none; + width: 1724px; + margin-left: 0; +} + +table .span23 { + float: none; + width: 1804px; + margin-left: 0; +} + +table .span24 { + float: none; + width: 1884px; + margin-left: 0; +} + +[class^="icon-"], +[class*=" icon-"] { + display: inline-block; + width: 14px; + height: 14px; + *margin-right: .3em; + line-height: 14px; + vertical-align: text-top; + background-image: url("../img/glyphicons-halflings.png"); + background-position: 14px 14px; + background-repeat: no-repeat; +} + +[class^="icon-"]:last-child, +[class*=" icon-"]:last-child { + *margin-left: 0; +} + +.icon-white { + background-image: url("../img/glyphicons-halflings-white.png"); +} + +.icon-glass { + background-position: 0 0; +} + +.icon-music { + background-position: -24px 0; +} + +.icon-search { + background-position: -48px 0; +} + +.icon-envelope { + background-position: -72px 0; +} + +.icon-heart { + background-position: -96px 0; +} + +.icon-star { + background-position: -120px 0; +} + +.icon-star-empty { + background-position: -144px 0; +} + +.icon-user { + background-position: -168px 0; +} + +.icon-film { + background-position: -192px 0; +} + +.icon-th-large { + background-position: -216px 0; +} + +.icon-th { + background-position: -240px 0; +} + +.icon-th-list { + background-position: -264px 0; +} + +.icon-ok { + background-position: -288px 0; +} + +.icon-remove { + background-position: -312px 0; +} + +.icon-zoom-in { + background-position: -336px 0; +} + +.icon-zoom-out { + background-position: -360px 0; +} + +.icon-off { + background-position: -384px 0; +} + +.icon-signal { + background-position: -408px 0; +} + +.icon-cog { + background-position: -432px 0; +} + +.icon-trash { + background-position: -456px 0; +} + +.icon-home { + background-position: 0 -24px; +} + +.icon-file { + background-position: -24px -24px; +} + +.icon-time { + background-position: -48px -24px; +} + +.icon-road { + background-position: -72px -24px; +} + +.icon-download-alt { + background-position: -96px -24px; +} + +.icon-download { + background-position: -120px -24px; +} + +.icon-upload { + background-position: -144px -24px; +} + +.icon-inbox { + background-position: -168px -24px; +} + +.icon-play-circle { + background-position: -192px -24px; +} + +.icon-repeat { + background-position: -216px -24px; +} + +.icon-refresh { + background-position: -240px -24px; +} + +.icon-list-alt { + background-position: -264px -24px; +} + +.icon-lock { + background-position: -287px -24px; +} + +.icon-flag { + background-position: -312px -24px; +} + +.icon-headphones { + background-position: -336px -24px; +} + +.icon-volume-off { + background-position: -360px -24px; +} + +.icon-volume-down { + background-position: -384px -24px; +} + +.icon-volume-up { + background-position: -408px -24px; +} + +.icon-qrcode { + background-position: -432px -24px; +} + +.icon-barcode { + background-position: -456px -24px; +} + +.icon-tag { + background-position: 0 -48px; +} + +.icon-tags { + background-position: -25px -48px; +} + +.icon-book { + background-position: -48px -48px; +} + +.icon-bookmark { + background-position: -72px -48px; +} + +.icon-print { + background-position: -96px -48px; +} + +.icon-camera { + background-position: -120px -48px; +} + +.icon-font { + background-position: -144px -48px; +} + +.icon-bold { + background-position: -167px -48px; +} + +.icon-italic { + background-position: -192px -48px; +} + +.icon-text-height { + background-position: -216px -48px; +} + +.icon-text-width { + background-position: -240px -48px; +} + +.icon-align-left { + background-position: -264px -48px; +} + +.icon-align-center { + background-position: -288px -48px; +} + +.icon-align-right { + background-position: -312px -48px; +} + +.icon-align-justify { + background-position: -336px -48px; +} + +.icon-list { + background-position: -360px -48px; +} + +.icon-indent-left { + background-position: -384px -48px; +} + +.icon-indent-right { + background-position: -408px -48px; +} + +.icon-facetime-video { + background-position: -432px -48px; +} + +.icon-picture { + background-position: -456px -48px; +} + +.icon-pencil { + background-position: 0 -72px; +} + +.icon-map-marker { + background-position: -24px -72px; +} + +.icon-adjust { + background-position: -48px -72px; +} + +.icon-tint { + background-position: -72px -72px; +} + +.icon-edit { + background-position: -96px -72px; +} + +.icon-share { + background-position: -120px -72px; +} + +.icon-check { + background-position: -144px -72px; +} + +.icon-move { + background-position: -168px -72px; +} + +.icon-step-backward { + background-position: -192px -72px; +} + +.icon-fast-backward { + background-position: -216px -72px; +} + +.icon-backward { + background-position: -240px -72px; +} + +.icon-play { + background-position: -264px -72px; +} + +.icon-pause { + background-position: -288px -72px; +} + +.icon-stop { + background-position: -312px -72px; +} + +.icon-forward { + background-position: -336px -72px; +} + +.icon-fast-forward { + background-position: -360px -72px; +} + +.icon-step-forward { + background-position: -384px -72px; +} + +.icon-eject { + background-position: -408px -72px; +} + +.icon-chevron-left { + background-position: -432px -72px; +} + +.icon-chevron-right { + background-position: -456px -72px; +} + +.icon-plus-sign { + background-position: 0 -96px; +} + +.icon-minus-sign { + background-position: -24px -96px; +} + +.icon-remove-sign { + background-position: -48px -96px; +} + +.icon-ok-sign { + background-position: -72px -96px; +} + +.icon-question-sign { + background-position: -96px -96px; +} + +.icon-info-sign { + background-position: -120px -96px; +} + +.icon-screenshot { + background-position: -144px -96px; +} + +.icon-remove-circle { + background-position: -168px -96px; +} + +.icon-ok-circle { + background-position: -192px -96px; +} + +.icon-ban-circle { + background-position: -216px -96px; +} + +.icon-arrow-left { + background-position: -240px -96px; +} + +.icon-arrow-right { + background-position: -264px -96px; +} + +.icon-arrow-up { + background-position: -289px -96px; +} + +.icon-arrow-down { + background-position: -312px -96px; +} + +.icon-share-alt { + background-position: -336px -96px; +} + +.icon-resize-full { + background-position: -360px -96px; +} + +.icon-resize-small { + background-position: -384px -96px; +} + +.icon-plus { + background-position: -408px -96px; +} + +.icon-minus { + background-position: -433px -96px; +} + +.icon-asterisk { + background-position: -456px -96px; +} + +.icon-exclamation-sign { + background-position: 0 -120px; +} + +.icon-gift { + background-position: -24px -120px; +} + +.icon-leaf { + background-position: -48px -120px; +} + +.icon-fire { + background-position: -72px -120px; +} + +.icon-eye-open { + background-position: -96px -120px; +} + +.icon-eye-close { + background-position: -120px -120px; +} + +.icon-warning-sign { + background-position: -144px -120px; +} + +.icon-plane { + background-position: -168px -120px; +} + +.icon-calendar { + background-position: -192px -120px; +} + +.icon-random { + background-position: -216px -120px; +} + +.icon-comment { + background-position: -240px -120px; +} + +.icon-magnet { + background-position: -264px -120px; +} + +.icon-chevron-up { + background-position: -288px -120px; +} + +.icon-chevron-down { + background-position: -313px -119px; +} + +.icon-retweet { + background-position: -336px -120px; +} + +.icon-shopping-cart { + background-position: -360px -120px; +} + +.icon-folder-close { + background-position: -384px -120px; +} + +.icon-folder-open { + background-position: -408px -120px; +} + +.icon-resize-vertical { + background-position: -432px -119px; +} + +.icon-resize-horizontal { + background-position: -456px -118px; +} + +.icon-hdd { + background-position: 0 -144px; +} + +.icon-bullhorn { + background-position: -24px -144px; +} + +.icon-bell { + background-position: -48px -144px; +} + +.icon-certificate { + background-position: -72px -144px; +} + +.icon-thumbs-up { + background-position: -96px -144px; +} + +.icon-thumbs-down { + background-position: -120px -144px; +} + +.icon-hand-right { + background-position: -144px -144px; +} + +.icon-hand-left { + background-position: -168px -144px; +} + +.icon-hand-up { + background-position: -192px -144px; +} + +.icon-hand-down { + background-position: -216px -144px; +} + +.icon-circle-arrow-right { + background-position: -240px -144px; +} + +.icon-circle-arrow-left { + background-position: -264px -144px; +} + +.icon-circle-arrow-up { + background-position: -288px -144px; +} + +.icon-circle-arrow-down { + background-position: -312px -144px; +} + +.icon-globe { + background-position: -336px -144px; +} + +.icon-wrench { + background-position: -360px -144px; +} + +.icon-tasks { + background-position: -384px -144px; +} + +.icon-filter { + background-position: -408px -144px; +} + +.icon-briefcase { + background-position: -432px -144px; +} + +.icon-fullscreen { + background-position: -456px -144px; +} + +.dropup, +.dropdown { + position: relative; +} + +.dropdown-toggle { + *margin-bottom: -3px; +} + +.dropdown-toggle:active, +.open .dropdown-toggle { + outline: 0; +} + +.caret { + display: inline-block; + width: 0; + height: 0; + vertical-align: top; + border-top: 4px solid #000000; + border-right: 4px solid transparent; + border-left: 4px solid transparent; + content: ""; + opacity: 0.3; + filter: alpha(opacity=30); +} + +.dropdown .caret { + margin-top: 8px; + margin-left: 2px; +} + +.dropdown:hover .caret, +.open .caret { + opacity: 1; + filter: alpha(opacity=100); +} + +.dropdown-menu { + position: absolute; + top: 100%; + left: 0; + z-index: 1000; + display: none; + float: left; + min-width: 160px; + padding: 4px 0; + margin: 1px 0 0; + list-style: none; + background-color: #ffffff; + border: 1px solid #ccc; + border: 1px solid rgba(0, 0, 0, 0.2); + *border-right-width: 2px; + *border-bottom-width: 2px; + -webkit-border-radius: 5px; + -moz-border-radius: 5px; + border-radius: 5px; + -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); + -moz-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); + box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); + -webkit-background-clip: padding-box; + -moz-background-clip: padding; + background-clip: padding-box; +} + +.dropdown-menu.pull-right { + right: 0; + left: auto; +} + +.dropdown-menu .divider { + *width: 100%; + height: 1px; + margin: 8px 1px; + *margin: -5px 0 5px; + overflow: hidden; + background-color: #e5e5e5; + border-bottom: 1px solid #ffffff; +} + +.dropdown-menu a { + display: block; + padding: 3px 15px; + clear: both; + font-weight: normal; + line-height: 18px; + color: #333333; + white-space: nowrap; +} + +.dropdown-menu li > a:hover, +.dropdown-menu .active > a, +.dropdown-menu .active > a:hover { + color: #ffffff; + text-decoration: none; + background-color: #0088cc; +} + +.open { + *z-index: 1000; +} + +.open > .dropdown-menu { + display: block; +} + +.pull-right > .dropdown-menu { + right: 0; + left: auto; +} + +.dropup .caret, +.navbar-fixed-bottom .dropdown .caret { + border-top: 0; + border-bottom: 4px solid #000000; + content: "\2191"; +} + +.dropup .dropdown-menu, +.navbar-fixed-bottom .dropdown .dropdown-menu { + top: auto; + bottom: 100%; + margin-bottom: 1px; +} + +.typeahead { + margin-top: 2px; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; +} + +.well { + min-height: 20px; + padding: 19px; + margin-bottom: 20px; + background-color: #f5f5f5; + border: 1px solid #eee; + border: 1px solid rgba(0, 0, 0, 0.05); + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); + -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); +} + +.well blockquote { + border-color: #ddd; + border-color: rgba(0, 0, 0, 0.15); +} + +.well-large { + padding: 24px; + -webkit-border-radius: 6px; + -moz-border-radius: 6px; + border-radius: 6px; +} + +.well-small { + padding: 9px; + -webkit-border-radius: 3px; + -moz-border-radius: 3px; + border-radius: 3px; +} + +.fade { + opacity: 0; + -webkit-transition: opacity 0.15s linear; + -moz-transition: opacity 0.15s linear; + -ms-transition: opacity 0.15s linear; + -o-transition: opacity 0.15s linear; + transition: opacity 0.15s linear; +} + +.fade.in { + opacity: 1; +} + +.collapse { + position: relative; + height: 0; + overflow: hidden; + -webkit-transition: height 0.35s ease; + -moz-transition: height 0.35s ease; + -ms-transition: height 0.35s ease; + -o-transition: height 0.35s ease; + transition: height 0.35s ease; +} + +.collapse.in { + height: auto; +} + +.close { + float: right; + font-size: 20px; + font-weight: bold; + line-height: 18px; + color: #000000; + text-shadow: 0 1px 0 #ffffff; + opacity: 0.2; + filter: alpha(opacity=20); +} + +.close:hover { + color: #000000; + text-decoration: none; + cursor: pointer; + opacity: 0.4; + filter: alpha(opacity=40); +} + +button.close { + padding: 0; + cursor: pointer; + background: transparent; + border: 0; + -webkit-appearance: none; +} + +.btn { + display: inline-block; + *display: inline; + padding: 4px 10px 4px; + margin-bottom: 0; + *margin-left: .3em; + font-size: 13px; + line-height: 18px; + *line-height: 20px; + color: #333333; + text-align: center; + text-shadow: 0 1px 1px rgba(255, 255, 255, 0.75); + vertical-align: middle; + cursor: pointer; + background-color: #f5f5f5; + *background-color: #e6e6e6; + background-image: -ms-linear-gradient(top, #ffffff, #e6e6e6); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ffffff), to(#e6e6e6)); + background-image: -webkit-linear-gradient(top, #ffffff, #e6e6e6); + background-image: -o-linear-gradient(top, #ffffff, #e6e6e6); + background-image: linear-gradient(top, #ffffff, #e6e6e6); + background-image: -moz-linear-gradient(top, #ffffff, #e6e6e6); + background-repeat: repeat-x; + border: 1px solid #cccccc; + *border: 0; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); + border-color: #e6e6e6 #e6e6e6 #bfbfbf; + border-bottom-color: #b3b3b3; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#ffffff', endColorstr='#e6e6e6', GradientType=0); + filter: progid:dximagetransform.microsoft.gradient(enabled=false); + *zoom: 1; + -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); + -moz-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); + box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); +} + +.btn:hover, +.btn:active, +.btn.active, +.btn.disabled, +.btn[disabled] { + background-color: #e6e6e6; + *background-color: #d9d9d9; +} + +.btn:active, +.btn.active { + background-color: #cccccc \9; +} + +.btn:first-child { + *margin-left: 0; +} + +.btn:hover { + color: #333333; + text-decoration: none; + background-color: #e6e6e6; + *background-color: #d9d9d9; + /* Buttons in IE7 don't get borders, so darken on hover */ + + background-position: 0 -15px; + -webkit-transition: background-position 0.1s linear; + -moz-transition: background-position 0.1s linear; + -ms-transition: background-position 0.1s linear; + -o-transition: background-position 0.1s linear; + transition: background-position 0.1s linear; +} + +.btn:focus { + outline: thin dotted #333; + outline: 5px auto -webkit-focus-ring-color; + outline-offset: -2px; +} + +.btn.active, +.btn:active { + background-color: #e6e6e6; + background-color: #d9d9d9 \9; + background-image: none; + outline: 0; + -webkit-box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); + -moz-box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); + box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); +} + +.btn.disabled, +.btn[disabled] { + cursor: default; + background-color: #e6e6e6; + background-image: none; + opacity: 0.65; + filter: alpha(opacity=65); + -webkit-box-shadow: none; + -moz-box-shadow: none; + box-shadow: none; +} + +.btn-large { + padding: 9px 14px; + font-size: 15px; + line-height: normal; + -webkit-border-radius: 5px; + -moz-border-radius: 5px; + border-radius: 5px; +} + +.btn-large [class^="icon-"] { + margin-top: 1px; +} + +.btn-small { + padding: 5px 9px; + font-size: 11px; + line-height: 16px; +} + +.btn-small [class^="icon-"] { + margin-top: -1px; +} + +.btn-mini { + padding: 2px 6px; + font-size: 11px; + line-height: 14px; +} + +.btn-primary, +.btn-primary:hover, +.btn-warning, +.btn-warning:hover, +.btn-danger, +.btn-danger:hover, +.btn-success, +.btn-success:hover, +.btn-info, +.btn-info:hover, +.btn-inverse, +.btn-inverse:hover { + color: #ffffff; + text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); +} + +.btn-primary.active, +.btn-warning.active, +.btn-danger.active, +.btn-success.active, +.btn-info.active, +.btn-inverse.active { + color: rgba(255, 255, 255, 0.75); +} + +.btn { + border-color: #ccc; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); +} + +.btn-primary { + background-color: #0074cc; + *background-color: #0055cc; + background-image: -ms-linear-gradient(top, #0088cc, #0055cc); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#0088cc), to(#0055cc)); + background-image: -webkit-linear-gradient(top, #0088cc, #0055cc); + background-image: -o-linear-gradient(top, #0088cc, #0055cc); + background-image: -moz-linear-gradient(top, #0088cc, #0055cc); + background-image: linear-gradient(top, #0088cc, #0055cc); + background-repeat: repeat-x; + border-color: #0055cc #0055cc #003580; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#0088cc', endColorstr='#0055cc', GradientType=0); + filter: progid:dximagetransform.microsoft.gradient(enabled=false); +} + +.btn-primary:hover, +.btn-primary:active, +.btn-primary.active, +.btn-primary.disabled, +.btn-primary[disabled] { + background-color: #0055cc; + *background-color: #004ab3; +} + +.btn-primary:active, +.btn-primary.active { + background-color: #004099 \9; +} + +.btn-warning { + background-color: #faa732; + *background-color: #f89406; + background-image: -ms-linear-gradient(top, #fbb450, #f89406); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#fbb450), to(#f89406)); + background-image: -webkit-linear-gradient(top, #fbb450, #f89406); + background-image: -o-linear-gradient(top, #fbb450, #f89406); + background-image: -moz-linear-gradient(top, #fbb450, #f89406); + background-image: linear-gradient(top, #fbb450, #f89406); + background-repeat: repeat-x; + border-color: #f89406 #f89406 #ad6704; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#fbb450', endColorstr='#f89406', GradientType=0); + filter: progid:dximagetransform.microsoft.gradient(enabled=false); +} + +.btn-warning:hover, +.btn-warning:active, +.btn-warning.active, +.btn-warning.disabled, +.btn-warning[disabled] { + background-color: #f89406; + *background-color: #df8505; +} + +.btn-warning:active, +.btn-warning.active { + background-color: #c67605 \9; +} + +.btn-danger { + background-color: #da4f49; + *background-color: #bd362f; + background-image: -ms-linear-gradient(top, #ee5f5b, #bd362f); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ee5f5b), to(#bd362f)); + background-image: -webkit-linear-gradient(top, #ee5f5b, #bd362f); + background-image: -o-linear-gradient(top, #ee5f5b, #bd362f); + background-image: -moz-linear-gradient(top, #ee5f5b, #bd362f); + background-image: linear-gradient(top, #ee5f5b, #bd362f); + background-repeat: repeat-x; + border-color: #bd362f #bd362f #802420; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#ee5f5b', endColorstr='#bd362f', GradientType=0); + filter: progid:dximagetransform.microsoft.gradient(enabled=false); +} + +.btn-danger:hover, +.btn-danger:active, +.btn-danger.active, +.btn-danger.disabled, +.btn-danger[disabled] { + background-color: #bd362f; + *background-color: #a9302a; +} + +.btn-danger:active, +.btn-danger.active { + background-color: #942a25 \9; +} + +.btn-success { + background-color: #5bb75b; + *background-color: #51a351; + background-image: -ms-linear-gradient(top, #62c462, #51a351); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#62c462), to(#51a351)); + background-image: -webkit-linear-gradient(top, #62c462, #51a351); + background-image: -o-linear-gradient(top, #62c462, #51a351); + background-image: -moz-linear-gradient(top, #62c462, #51a351); + background-image: linear-gradient(top, #62c462, #51a351); + background-repeat: repeat-x; + border-color: #51a351 #51a351 #387038; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#62c462', endColorstr='#51a351', GradientType=0); + filter: progid:dximagetransform.microsoft.gradient(enabled=false); +} + +.btn-success:hover, +.btn-success:active, +.btn-success.active, +.btn-success.disabled, +.btn-success[disabled] { + background-color: #51a351; + *background-color: #499249; +} + +.btn-success:active, +.btn-success.active { + background-color: #408140 \9; +} + +.btn-info { + background-color: #49afcd; + *background-color: #2f96b4; + background-image: -ms-linear-gradient(top, #5bc0de, #2f96b4); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#5bc0de), to(#2f96b4)); + background-image: -webkit-linear-gradient(top, #5bc0de, #2f96b4); + background-image: -o-linear-gradient(top, #5bc0de, #2f96b4); + background-image: -moz-linear-gradient(top, #5bc0de, #2f96b4); + background-image: linear-gradient(top, #5bc0de, #2f96b4); + background-repeat: repeat-x; + border-color: #2f96b4 #2f96b4 #1f6377; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#5bc0de', endColorstr='#2f96b4', GradientType=0); + filter: progid:dximagetransform.microsoft.gradient(enabled=false); +} + +.btn-info:hover, +.btn-info:active, +.btn-info.active, +.btn-info.disabled, +.btn-info[disabled] { + background-color: #2f96b4; + *background-color: #2a85a0; +} + +.btn-info:active, +.btn-info.active { + background-color: #24748c \9; +} + +.btn-inverse { + background-color: #414141; + *background-color: #222222; + background-image: -ms-linear-gradient(top, #555555, #222222); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#555555), to(#222222)); + background-image: -webkit-linear-gradient(top, #555555, #222222); + background-image: -o-linear-gradient(top, #555555, #222222); + background-image: -moz-linear-gradient(top, #555555, #222222); + background-image: linear-gradient(top, #555555, #222222); + background-repeat: repeat-x; + border-color: #222222 #222222 #000000; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#555555', endColorstr='#222222', GradientType=0); + filter: progid:dximagetransform.microsoft.gradient(enabled=false); +} + +.btn-inverse:hover, +.btn-inverse:active, +.btn-inverse.active, +.btn-inverse.disabled, +.btn-inverse[disabled] { + background-color: #222222; + *background-color: #151515; +} + +.btn-inverse:active, +.btn-inverse.active { + background-color: #080808 \9; +} + +button.btn, +input[type="submit"].btn { + *padding-top: 2px; + *padding-bottom: 2px; +} + +button.btn::-moz-focus-inner, +input[type="submit"].btn::-moz-focus-inner { + padding: 0; + border: 0; +} + +button.btn.btn-large, +input[type="submit"].btn.btn-large { + *padding-top: 7px; + *padding-bottom: 7px; +} + +button.btn.btn-small, +input[type="submit"].btn.btn-small { + *padding-top: 3px; + *padding-bottom: 3px; +} + +button.btn.btn-mini, +input[type="submit"].btn.btn-mini { + *padding-top: 1px; + *padding-bottom: 1px; +} + +.btn-group { + position: relative; + *margin-left: .3em; + *zoom: 1; +} + +.btn-group:before, +.btn-group:after { + display: table; + content: ""; +} + +.btn-group:after { + clear: both; +} + +.btn-group:first-child { + *margin-left: 0; +} + +.btn-group + .btn-group { + margin-left: 5px; +} + +.btn-toolbar { + margin-top: 9px; + margin-bottom: 9px; +} + +.btn-toolbar .btn-group { + display: inline-block; + *display: inline; + /* IE7 inline-block hack */ + + *zoom: 1; +} + +.btn-group > .btn { + position: relative; + float: left; + margin-left: -1px; + -webkit-border-radius: 0; + -moz-border-radius: 0; + border-radius: 0; +} + +.btn-group > .btn:first-child { + margin-left: 0; + -webkit-border-bottom-left-radius: 4px; + border-bottom-left-radius: 4px; + -webkit-border-top-left-radius: 4px; + border-top-left-radius: 4px; + -moz-border-radius-bottomleft: 4px; + -moz-border-radius-topleft: 4px; +} + +.btn-group > .btn:last-child, +.btn-group > .dropdown-toggle { + -webkit-border-top-right-radius: 4px; + border-top-right-radius: 4px; + -webkit-border-bottom-right-radius: 4px; + border-bottom-right-radius: 4px; + -moz-border-radius-topright: 4px; + -moz-border-radius-bottomright: 4px; +} + +.btn-group > .btn.large:first-child { + margin-left: 0; + -webkit-border-bottom-left-radius: 6px; + border-bottom-left-radius: 6px; + -webkit-border-top-left-radius: 6px; + border-top-left-radius: 6px; + -moz-border-radius-bottomleft: 6px; + -moz-border-radius-topleft: 6px; +} + +.btn-group > .btn.large:last-child, +.btn-group > .large.dropdown-toggle { + -webkit-border-top-right-radius: 6px; + border-top-right-radius: 6px; + -webkit-border-bottom-right-radius: 6px; + border-bottom-right-radius: 6px; + -moz-border-radius-topright: 6px; + -moz-border-radius-bottomright: 6px; +} + +.btn-group > .btn:hover, +.btn-group > .btn:focus, +.btn-group > .btn:active, +.btn-group > .btn.active { + z-index: 2; +} + +.btn-group .dropdown-toggle:active, +.btn-group.open .dropdown-toggle { + outline: 0; +} + +.btn-group > .dropdown-toggle { + *padding-top: 4px; + padding-right: 8px; + *padding-bottom: 4px; + padding-left: 8px; + -webkit-box-shadow: inset 1px 0 0 rgba(255, 255, 255, 0.125), inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); + -moz-box-shadow: inset 1px 0 0 rgba(255, 255, 255, 0.125), inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); + box-shadow: inset 1px 0 0 rgba(255, 255, 255, 0.125), inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); +} + +.btn-group > .btn-mini.dropdown-toggle { + padding-right: 5px; + padding-left: 5px; +} + +.btn-group > .btn-small.dropdown-toggle { + *padding-top: 4px; + *padding-bottom: 4px; +} + +.btn-group > .btn-large.dropdown-toggle { + padding-right: 12px; + padding-left: 12px; +} + +.btn-group.open .dropdown-toggle { + background-image: none; + -webkit-box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); + -moz-box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); + box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); +} + +.btn-group.open .btn.dropdown-toggle { + background-color: #e6e6e6; +} + +.btn-group.open .btn-primary.dropdown-toggle { + background-color: #0055cc; +} + +.btn-group.open .btn-warning.dropdown-toggle { + background-color: #f89406; +} + +.btn-group.open .btn-danger.dropdown-toggle { + background-color: #bd362f; +} + +.btn-group.open .btn-success.dropdown-toggle { + background-color: #51a351; +} + +.btn-group.open .btn-info.dropdown-toggle { + background-color: #2f96b4; +} + +.btn-group.open .btn-inverse.dropdown-toggle { + background-color: #222222; +} + +.btn .caret { + margin-top: 7px; + margin-left: 0; +} + +.btn:hover .caret, +.open.btn-group .caret { + opacity: 1; + filter: alpha(opacity=100); +} + +.btn-mini .caret { + margin-top: 5px; +} + +.btn-small .caret { + margin-top: 6px; +} + +.btn-large .caret { + margin-top: 6px; + border-top-width: 5px; + border-right-width: 5px; + border-left-width: 5px; +} + +.dropup .btn-large .caret { + border-top: 0; + border-bottom: 5px solid #000000; +} + +.btn-primary .caret, +.btn-warning .caret, +.btn-danger .caret, +.btn-info .caret, +.btn-success .caret, +.btn-inverse .caret { + border-top-color: #ffffff; + border-bottom-color: #ffffff; + opacity: 0.75; + filter: alpha(opacity=75); +} + +.alert { + padding: 8px 35px 8px 14px; + margin-bottom: 18px; + color: #c09853; + text-shadow: 0 1px 0 rgba(255, 255, 255, 0.5); + background-color: #fcf8e3; + border: 1px solid #fbeed5; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; +} + +.alert-heading { + color: inherit; +} + +.alert .close { + position: relative; + top: -2px; + right: -21px; + line-height: 18px; +} + +.alert-success { + color: #468847; + background-color: #dff0d8; + border-color: #d6e9c6; +} + +.alert-danger, +.alert-error { + color: #b94a48; + background-color: #f2dede; + border-color: #eed3d7; +} + +.alert-info { + color: #3a87ad; + background-color: #d9edf7; + border-color: #bce8f1; +} + +.alert-block { + padding-top: 14px; + padding-bottom: 14px; +} + +.alert-block > p, +.alert-block > ul { + margin-bottom: 0; +} + +.alert-block p + p { + margin-top: 5px; +} + +.nav { + margin-bottom: 18px; + margin-left: 0; + list-style: none; +} + +.nav > li > a { + display: block; +} + +.nav > li > a:hover { + text-decoration: none; + background-color: #eeeeee; +} + +.nav > .pull-right { + float: right; +} + +.nav .nav-header { + display: block; + padding: 3px 15px; + font-size: 11px; + font-weight: bold; + line-height: 18px; + color: #999999; + text-shadow: 0 1px 0 rgba(255, 255, 255, 0.5); + text-transform: uppercase; +} + +.nav li + .nav-header { + margin-top: 9px; +} + +.nav-list { + padding-right: 15px; + padding-left: 15px; + margin-bottom: 0; +} + +.nav-list > li > a, +.nav-list .nav-header { + margin-right: -15px; + margin-left: -15px; + text-shadow: 0 1px 0 rgba(255, 255, 255, 0.5); +} + +.nav-list > li > a { + padding: 3px 15px; +} + +.nav-list > .active > a, +.nav-list > .active > a:hover { + color: #ffffff; + text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.2); + background-color: #0088cc; +} + +.nav-list [class^="icon-"] { + margin-right: 2px; +} + +.nav-list .divider { + *width: 100%; + height: 1px; + margin: 8px 1px; + *margin: -5px 0 5px; + overflow: hidden; + background-color: #e5e5e5; + border-bottom: 1px solid #ffffff; +} + +.nav-tabs, +.nav-pills { + *zoom: 1; +} + +.nav-tabs:before, +.nav-pills:before, +.nav-tabs:after, +.nav-pills:after { + display: table; + content: ""; +} + +.nav-tabs:after, +.nav-pills:after { + clear: both; +} + +.nav-tabs > li, +.nav-pills > li { + float: left; +} + +.nav-tabs > li > a, +.nav-pills > li > a { + padding-right: 12px; + padding-left: 12px; + margin-right: 2px; + line-height: 14px; +} + +.nav-tabs { + border-bottom: 1px solid #ddd; +} + +.nav-tabs > li { + margin-bottom: -1px; +} + +.nav-tabs > li > a { + padding-top: 8px; + padding-bottom: 8px; + line-height: 18px; + border: 1px solid transparent; + -webkit-border-radius: 4px 4px 0 0; + -moz-border-radius: 4px 4px 0 0; + border-radius: 4px 4px 0 0; +} + +.nav-tabs > li > a:hover { + border-color: #eeeeee #eeeeee #dddddd; +} + +.nav-tabs > .active > a, +.nav-tabs > .active > a:hover { + color: #555555; + cursor: default; + background-color: #ffffff; + border: 1px solid #ddd; + border-bottom-color: transparent; +} + +.nav-pills > li > a { + padding-top: 8px; + padding-bottom: 8px; + margin-top: 2px; + margin-bottom: 2px; + -webkit-border-radius: 5px; + -moz-border-radius: 5px; + border-radius: 5px; +} + +.nav-pills > .active > a, +.nav-pills > .active > a:hover { + color: #ffffff; + background-color: #0088cc; +} + +.nav-stacked > li { + float: none; +} + +.nav-stacked > li > a { + margin-right: 0; +} + +.nav-tabs.nav-stacked { + border-bottom: 0; +} + +.nav-tabs.nav-stacked > li > a { + border: 1px solid #ddd; + -webkit-border-radius: 0; + -moz-border-radius: 0; + border-radius: 0; +} + +.nav-tabs.nav-stacked > li:first-child > a { + -webkit-border-radius: 4px 4px 0 0; + -moz-border-radius: 4px 4px 0 0; + border-radius: 4px 4px 0 0; +} + +.nav-tabs.nav-stacked > li:last-child > a { + -webkit-border-radius: 0 0 4px 4px; + -moz-border-radius: 0 0 4px 4px; + border-radius: 0 0 4px 4px; +} + +.nav-tabs.nav-stacked > li > a:hover { + z-index: 2; + border-color: #ddd; +} + +.nav-pills.nav-stacked > li > a { + margin-bottom: 3px; +} + +.nav-pills.nav-stacked > li:last-child > a { + margin-bottom: 1px; +} + +.nav-tabs .dropdown-menu { + -webkit-border-radius: 0 0 5px 5px; + -moz-border-radius: 0 0 5px 5px; + border-radius: 0 0 5px 5px; +} + +.nav-pills .dropdown-menu { + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; +} + +.nav-tabs .dropdown-toggle .caret, +.nav-pills .dropdown-toggle .caret { + margin-top: 6px; + border-top-color: #0088cc; + border-bottom-color: #0088cc; +} + +.nav-tabs .dropdown-toggle:hover .caret, +.nav-pills .dropdown-toggle:hover .caret { + border-top-color: #005580; + border-bottom-color: #005580; +} + +.nav-tabs .active .dropdown-toggle .caret, +.nav-pills .active .dropdown-toggle .caret { + border-top-color: #333333; + border-bottom-color: #333333; +} + +.nav > .dropdown.active > a:hover { + color: #000000; + cursor: pointer; +} + +.nav-tabs .open .dropdown-toggle, +.nav-pills .open .dropdown-toggle, +.nav > li.dropdown.open.active > a:hover { + color: #ffffff; + background-color: #999999; + border-color: #999999; +} + +.nav li.dropdown.open .caret, +.nav li.dropdown.open.active .caret, +.nav li.dropdown.open a:hover .caret { + border-top-color: #ffffff; + border-bottom-color: #ffffff; + opacity: 1; + filter: alpha(opacity=100); +} + +.tabs-stacked .open > a:hover { + border-color: #999999; +} + +.tabbable { + *zoom: 1; +} + +.tabbable:before, +.tabbable:after { + display: table; + content: ""; +} + +.tabbable:after { + clear: both; +} + +.tab-content { + overflow: auto; +} + +.tabs-below > .nav-tabs, +.tabs-right > .nav-tabs, +.tabs-left > .nav-tabs { + border-bottom: 0; +} + +.tab-content > .tab-pane, +.pill-content > .pill-pane { + display: none; +} + +.tab-content > .active, +.pill-content > .active { + display: block; +} + +.tabs-below > .nav-tabs { + border-top: 1px solid #ddd; +} + +.tabs-below > .nav-tabs > li { + margin-top: -1px; + margin-bottom: 0; +} + +.tabs-below > .nav-tabs > li > a { + -webkit-border-radius: 0 0 4px 4px; + -moz-border-radius: 0 0 4px 4px; + border-radius: 0 0 4px 4px; +} + +.tabs-below > .nav-tabs > li > a:hover { + border-top-color: #ddd; + border-bottom-color: transparent; +} + +.tabs-below > .nav-tabs > .active > a, +.tabs-below > .nav-tabs > .active > a:hover { + border-color: transparent #ddd #ddd #ddd; +} + +.tabs-left > .nav-tabs > li, +.tabs-right > .nav-tabs > li { + float: none; +} + +.tabs-left > .nav-tabs > li > a, +.tabs-right > .nav-tabs > li > a { + min-width: 74px; + margin-right: 0; + margin-bottom: 3px; +} + +.tabs-left > .nav-tabs { + float: left; + margin-right: 19px; + border-right: 1px solid #ddd; +} + +.tabs-left > .nav-tabs > li > a { + margin-right: -1px; + -webkit-border-radius: 4px 0 0 4px; + -moz-border-radius: 4px 0 0 4px; + border-radius: 4px 0 0 4px; +} + +.tabs-left > .nav-tabs > li > a:hover { + border-color: #eeeeee #dddddd #eeeeee #eeeeee; +} + +.tabs-left > .nav-tabs .active > a, +.tabs-left > .nav-tabs .active > a:hover { + border-color: #ddd transparent #ddd #ddd; + *border-right-color: #ffffff; +} + +.tabs-right > .nav-tabs { + float: right; + margin-left: 19px; + border-left: 1px solid #ddd; +} + +.tabs-right > .nav-tabs > li > a { + margin-left: -1px; + -webkit-border-radius: 0 4px 4px 0; + -moz-border-radius: 0 4px 4px 0; + border-radius: 0 4px 4px 0; +} + +.tabs-right > .nav-tabs > li > a:hover { + border-color: #eeeeee #eeeeee #eeeeee #dddddd; +} + +.tabs-right > .nav-tabs .active > a, +.tabs-right > .nav-tabs .active > a:hover { + border-color: #ddd #ddd #ddd transparent; + *border-left-color: #ffffff; +} + +.navbar { + *position: relative; + *z-index: 2; + margin-bottom: 18px; + overflow: visible; +} + +.navbar-inner { + min-height: 40px; + padding-right: 20px; + padding-left: 20px; + background-color: #2c2c2c; + background-image: -moz-linear-gradient(top, #333333, #222222); + background-image: -ms-linear-gradient(top, #333333, #222222); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#333333), to(#222222)); + background-image: -webkit-linear-gradient(top, #333333, #222222); + background-image: -o-linear-gradient(top, #333333, #222222); + background-image: linear-gradient(top, #333333, #222222); + background-repeat: repeat-x; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#333333', endColorstr='#222222', GradientType=0); + -webkit-box-shadow: 0 1px 3px rgba(0, 0, 0, 0.25), inset 0 -1px 0 rgba(0, 0, 0, 0.1); + -moz-box-shadow: 0 1px 3px rgba(0, 0, 0, 0.25), inset 0 -1px 0 rgba(0, 0, 0, 0.1); + box-shadow: 0 1px 3px rgba(0, 0, 0, 0.25), inset 0 -1px 0 rgba(0, 0, 0, 0.1); +} + +.navbar .container { + width: auto; +} + +.nav-collapse.collapse { + height: auto; +} + +.navbar { + color: #999999; +} + +.navbar .brand:hover { + text-decoration: none; +} + +.navbar .brand { + display: block; + float: left; + padding: 8px 20px 12px; + margin-left: -20px; + font-size: 20px; + font-weight: 200; + line-height: 1; + color: #999999; +} + +.navbar .navbar-text { + margin-bottom: 0; + line-height: 40px; +} + +.navbar .navbar-link { + color: #999999; +} + +.navbar .navbar-link:hover { + color: #ffffff; +} + +.navbar .btn, +.navbar .btn-group { + margin-top: 5px; +} + +.navbar .btn-group .btn { + margin: 0; +} + +.navbar-form { + margin-bottom: 0; + *zoom: 1; +} + +.navbar-form:before, +.navbar-form:after { + display: table; + content: ""; +} + +.navbar-form:after { + clear: both; +} + +.navbar-form input, +.navbar-form select, +.navbar-form .radio, +.navbar-form .checkbox { + margin-top: 5px; +} + +.navbar-form input, +.navbar-form select { + display: inline-block; + margin-bottom: 0; +} + +.navbar-form input[type="image"], +.navbar-form input[type="checkbox"], +.navbar-form input[type="radio"] { + margin-top: 3px; +} + +.navbar-form .input-append, +.navbar-form .input-prepend { + margin-top: 6px; + white-space: nowrap; +} + +.navbar-form .input-append input, +.navbar-form .input-prepend input { + margin-top: 0; +} + +.navbar-search { + position: relative; + float: left; + margin-top: 6px; + margin-bottom: 0; +} + +.navbar-search .search-query { + padding: 4px 9px; + font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; + font-size: 13px; + font-weight: normal; + line-height: 1; + color: #ffffff; + background-color: #626262; + border: 1px solid #151515; + -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1), 0 1px 0 rgba(255, 255, 255, 0.15); + -moz-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1), 0 1px 0 rgba(255, 255, 255, 0.15); + box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1), 0 1px 0 rgba(255, 255, 255, 0.15); + -webkit-transition: none; + -moz-transition: none; + -ms-transition: none; + -o-transition: none; + transition: none; +} + +.navbar-search .search-query:-moz-placeholder { + color: #cccccc; +} + +.navbar-search .search-query:-ms-input-placeholder { + color: #cccccc; +} + +.navbar-search .search-query::-webkit-input-placeholder { + color: #cccccc; +} + +.navbar-search .search-query:focus, +.navbar-search .search-query.focused { + padding: 5px 10px; + color: #333333; + text-shadow: 0 1px 0 #ffffff; + background-color: #ffffff; + border: 0; + outline: 0; + -webkit-box-shadow: 0 0 3px rgba(0, 0, 0, 0.15); + -moz-box-shadow: 0 0 3px rgba(0, 0, 0, 0.15); + box-shadow: 0 0 3px rgba(0, 0, 0, 0.15); +} + +.navbar-fixed-top, +.navbar-fixed-bottom { + position: fixed; + right: 0; + left: 0; + z-index: 1030; + margin-bottom: 0; +} + +.navbar-fixed-top .navbar-inner, +.navbar-fixed-bottom .navbar-inner { + padding-right: 0; + padding-left: 0; + -webkit-border-radius: 0; + -moz-border-radius: 0; + border-radius: 0; +} + +.navbar-fixed-top .container, +.navbar-fixed-bottom .container { + width: 940px; +} + +.navbar-fixed-top { + top: 0; +} + +.navbar-fixed-bottom { + bottom: 0; +} + +.navbar .nav { + position: relative; + left: 0; + display: block; + float: left; + margin: 0 10px 0 0; +} + +.navbar .nav.pull-right { + float: right; +} + +.navbar .nav > li { + display: block; + float: left; +} + +.navbar .nav > li > a { + float: none; + padding: 9px 10px 11px; + line-height: 19px; + color: #999999; + text-decoration: none; + text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); +} + +.navbar .btn { + display: inline-block; + padding: 4px 10px 4px; + margin: 5px 5px 6px; + line-height: 18px; +} + +.navbar .btn-group { + padding: 5px 5px 6px; + margin: 0; +} + +.navbar .nav > li > a:hover { + color: #ffffff; + text-decoration: none; + background-color: transparent; +} + +.navbar .nav .active > a, +.navbar .nav .active > a:hover { + color: #ffffff; + text-decoration: none; + background-color: #222222; +} + +.navbar .divider-vertical { + width: 1px; + height: 40px; + margin: 0 9px; + overflow: hidden; + background-color: #222222; + border-right: 1px solid #333333; +} + +.navbar .nav.pull-right { + margin-right: 0; + margin-left: 10px; +} + +.navbar .btn-navbar { + display: none; + float: right; + padding: 7px 10px; + margin-right: 5px; + margin-left: 5px; + background-color: #2c2c2c; + *background-color: #222222; + background-image: -ms-linear-gradient(top, #333333, #222222); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#333333), to(#222222)); + background-image: -webkit-linear-gradient(top, #333333, #222222); + background-image: -o-linear-gradient(top, #333333, #222222); + background-image: linear-gradient(top, #333333, #222222); + background-image: -moz-linear-gradient(top, #333333, #222222); + background-repeat: repeat-x; + border-color: #222222 #222222 #000000; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#333333', endColorstr='#222222', GradientType=0); + filter: progid:dximagetransform.microsoft.gradient(enabled=false); + -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.075); + -moz-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.075); + box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.075); +} + +.navbar .btn-navbar:hover, +.navbar .btn-navbar:active, +.navbar .btn-navbar.active, +.navbar .btn-navbar.disabled, +.navbar .btn-navbar[disabled] { + background-color: #222222; + *background-color: #151515; +} + +.navbar .btn-navbar:active, +.navbar .btn-navbar.active { + background-color: #080808 \9; +} + +.navbar .btn-navbar .icon-bar { + display: block; + width: 18px; + height: 2px; + background-color: #f5f5f5; + -webkit-border-radius: 1px; + -moz-border-radius: 1px; + border-radius: 1px; + -webkit-box-shadow: 0 1px 0 rgba(0, 0, 0, 0.25); + -moz-box-shadow: 0 1px 0 rgba(0, 0, 0, 0.25); + box-shadow: 0 1px 0 rgba(0, 0, 0, 0.25); +} + +.btn-navbar .icon-bar + .icon-bar { + margin-top: 3px; +} + +.navbar .dropdown-menu:before { + position: absolute; + top: -7px; + left: 9px; + display: inline-block; + border-right: 7px solid transparent; + border-bottom: 7px solid #ccc; + border-left: 7px solid transparent; + border-bottom-color: rgba(0, 0, 0, 0.2); + content: ''; +} + +.navbar .dropdown-menu:after { + position: absolute; + top: -6px; + left: 10px; + display: inline-block; + border-right: 6px solid transparent; + border-bottom: 6px solid #ffffff; + border-left: 6px solid transparent; + content: ''; +} + +.navbar-fixed-bottom .dropdown-menu:before { + top: auto; + bottom: -7px; + border-top: 7px solid #ccc; + border-bottom: 0; + border-top-color: rgba(0, 0, 0, 0.2); +} + +.navbar-fixed-bottom .dropdown-menu:after { + top: auto; + bottom: -6px; + border-top: 6px solid #ffffff; + border-bottom: 0; +} + +.navbar .nav li.dropdown .dropdown-toggle .caret, +.navbar .nav li.dropdown.open .caret { + border-top-color: #ffffff; + border-bottom-color: #ffffff; +} + +.navbar .nav li.dropdown.active .caret { + opacity: 1; + filter: alpha(opacity=100); +} + +.navbar .nav li.dropdown.open > .dropdown-toggle, +.navbar .nav li.dropdown.active > .dropdown-toggle, +.navbar .nav li.dropdown.open.active > .dropdown-toggle { + background-color: transparent; +} + +.navbar .nav li.dropdown.active > .dropdown-toggle:hover { + color: #ffffff; +} + +.navbar .pull-right .dropdown-menu, +.navbar .dropdown-menu.pull-right { + right: 0; + left: auto; +} + +.navbar .pull-right .dropdown-menu:before, +.navbar .dropdown-menu.pull-right:before { + right: 12px; + left: auto; +} + +.navbar .pull-right .dropdown-menu:after, +.navbar .dropdown-menu.pull-right:after { + right: 13px; + left: auto; +} + +.breadcrumb { + padding: 7px 14px; + margin: 0 0 18px; + list-style: none; + background-color: #fbfbfb; + background-image: -moz-linear-gradient(top, #ffffff, #f5f5f5); + background-image: -ms-linear-gradient(top, #ffffff, #f5f5f5); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ffffff), to(#f5f5f5)); + background-image: -webkit-linear-gradient(top, #ffffff, #f5f5f5); + background-image: -o-linear-gradient(top, #ffffff, #f5f5f5); + background-image: linear-gradient(top, #ffffff, #f5f5f5); + background-repeat: repeat-x; + border: 1px solid #ddd; + -webkit-border-radius: 3px; + -moz-border-radius: 3px; + border-radius: 3px; + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#ffffff', endColorstr='#f5f5f5', GradientType=0); + -webkit-box-shadow: inset 0 1px 0 #ffffff; + -moz-box-shadow: inset 0 1px 0 #ffffff; + box-shadow: inset 0 1px 0 #ffffff; +} + +.breadcrumb li { + display: inline-block; + *display: inline; + text-shadow: 0 1px 0 #ffffff; + *zoom: 1; +} + +.breadcrumb .divider { + padding: 0 5px; + color: #999999; +} + +.breadcrumb .active a { + color: #333333; +} + +.pagination { + height: 36px; + margin: 18px 0; +} + +.pagination ul { + display: inline-block; + *display: inline; + margin-bottom: 0; + margin-left: 0; + -webkit-border-radius: 3px; + -moz-border-radius: 3px; + border-radius: 3px; + *zoom: 1; + -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05); + -moz-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05); + box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05); +} + +.pagination li { + display: inline; +} + +.pagination a { + float: left; + padding: 0 14px; + line-height: 34px; + text-decoration: none; + border: 1px solid #ddd; + border-left-width: 0; +} + +.pagination a:hover, +.pagination .active a { + background-color: #f5f5f5; +} + +.pagination .active a { + color: #999999; + cursor: default; +} + +.pagination .disabled span, +.pagination .disabled a, +.pagination .disabled a:hover { + color: #999999; + cursor: default; + background-color: transparent; +} + +.pagination li:first-child a { + border-left-width: 1px; + -webkit-border-radius: 3px 0 0 3px; + -moz-border-radius: 3px 0 0 3px; + border-radius: 3px 0 0 3px; +} + +.pagination li:last-child a { + -webkit-border-radius: 0 3px 3px 0; + -moz-border-radius: 0 3px 3px 0; + border-radius: 0 3px 3px 0; +} + +.pagination-centered { + text-align: center; +} + +.pagination-right { + text-align: right; +} + +.pager { + margin-bottom: 18px; + margin-left: 0; + text-align: center; + list-style: none; + *zoom: 1; +} + +.pager:before, +.pager:after { + display: table; + content: ""; +} + +.pager:after { + clear: both; +} + +.pager li { + display: inline; +} + +.pager a { + display: inline-block; + padding: 5px 14px; + background-color: #fff; + border: 1px solid #ddd; + -webkit-border-radius: 15px; + -moz-border-radius: 15px; + border-radius: 15px; +} + +.pager a:hover { + text-decoration: none; + background-color: #f5f5f5; +} + +.pager .next a { + float: right; +} + +.pager .previous a { + float: left; +} + +.pager .disabled a, +.pager .disabled a:hover { + color: #999999; + cursor: default; + background-color: #fff; +} + +.modal-open .dropdown-menu { + z-index: 2050; +} + +.modal-open .dropdown.open { + *z-index: 2050; +} + +.modal-open .popover { + z-index: 2060; +} + +.modal-open .tooltip { + z-index: 2070; +} + +.modal-backdrop { + position: fixed; + top: 0; + right: 0; + bottom: 0; + left: 0; + z-index: 1040; + background-color: #000000; +} + +.modal-backdrop.fade { + opacity: 0; +} + +.modal-backdrop, +.modal-backdrop.fade.in { + opacity: 0.8; + filter: alpha(opacity=80); +} + +.modal { + position: fixed; + top: 50%; + left: 50%; + z-index: 1050; + width: 560px; + margin: -250px 0 0 -280px; + overflow: auto; + background-color: #ffffff; + border: 1px solid #999; + border: 1px solid rgba(0, 0, 0, 0.3); + *border: 1px solid #999; + -webkit-border-radius: 6px; + -moz-border-radius: 6px; + border-radius: 6px; + -webkit-box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3); + -moz-box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3); + box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3); + -webkit-background-clip: padding-box; + -moz-background-clip: padding-box; + background-clip: padding-box; +} + +.modal.fade { + top: -25%; + -webkit-transition: opacity 0.3s linear, top 0.3s ease-out; + -moz-transition: opacity 0.3s linear, top 0.3s ease-out; + -ms-transition: opacity 0.3s linear, top 0.3s ease-out; + -o-transition: opacity 0.3s linear, top 0.3s ease-out; + transition: opacity 0.3s linear, top 0.3s ease-out; +} + +.modal.fade.in { + top: 50%; +} + +.modal-header { + padding: 9px 15px; + border-bottom: 1px solid #eee; +} + +.modal-header .close { + margin-top: 2px; +} + +.modal-body { + max-height: 400px; + padding: 15px; + overflow-y: auto; +} + +.modal-form { + margin-bottom: 0; +} + +.modal-footer { + padding: 14px 15px 15px; + margin-bottom: 0; + text-align: right; + background-color: #f5f5f5; + border-top: 1px solid #ddd; + -webkit-border-radius: 0 0 6px 6px; + -moz-border-radius: 0 0 6px 6px; + border-radius: 0 0 6px 6px; + *zoom: 1; + -webkit-box-shadow: inset 0 1px 0 #ffffff; + -moz-box-shadow: inset 0 1px 0 #ffffff; + box-shadow: inset 0 1px 0 #ffffff; +} + +.modal-footer:before, +.modal-footer:after { + display: table; + content: ""; +} + +.modal-footer:after { + clear: both; +} + +.modal-footer .btn + .btn { + margin-bottom: 0; + margin-left: 5px; +} + +.modal-footer .btn-group .btn + .btn { + margin-left: -1px; +} + +.tooltip { + position: absolute; + z-index: 1020; + display: block; + padding: 5px; + font-size: 11px; + opacity: 0; + filter: alpha(opacity=0); + visibility: visible; +} + +.tooltip.in { + opacity: 0.8; + filter: alpha(opacity=80); +} + +.tooltip.top { + margin-top: -2px; +} + +.tooltip.right { + margin-left: 2px; +} + +.tooltip.bottom { + margin-top: 2px; +} + +.tooltip.left { + margin-left: -2px; +} + +.tooltip.top .tooltip-arrow { + bottom: 0; + left: 50%; + margin-left: -5px; + border-top: 5px solid #000000; + border-right: 5px solid transparent; + border-left: 5px solid transparent; +} + +.tooltip.left .tooltip-arrow { + top: 50%; + right: 0; + margin-top: -5px; + border-top: 5px solid transparent; + border-bottom: 5px solid transparent; + border-left: 5px solid #000000; +} + +.tooltip.bottom .tooltip-arrow { + top: 0; + left: 50%; + margin-left: -5px; + border-right: 5px solid transparent; + border-bottom: 5px solid #000000; + border-left: 5px solid transparent; +} + +.tooltip.right .tooltip-arrow { + top: 50%; + left: 0; + margin-top: -5px; + border-top: 5px solid transparent; + border-right: 5px solid #000000; + border-bottom: 5px solid transparent; +} + +.tooltip-inner { + max-width: 200px; + padding: 3px 8px; + color: #ffffff; + text-align: center; + text-decoration: none; + background-color: #000000; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; +} + +.tooltip-arrow { + position: absolute; + width: 0; + height: 0; +} + +.popover { + position: absolute; + top: 0; + left: 0; + z-index: 1010; + display: none; + padding: 5px; +} + +.popover.top { + margin-top: -5px; +} + +.popover.right { + margin-left: 5px; +} + +.popover.bottom { + margin-top: 5px; +} + +.popover.left { + margin-left: -5px; +} + +.popover.top .arrow { + bottom: 0; + left: 50%; + margin-left: -5px; + border-top: 5px solid #000000; + border-right: 5px solid transparent; + border-left: 5px solid transparent; +} + +.popover.right .arrow { + top: 50%; + left: 0; + margin-top: -5px; + border-top: 5px solid transparent; + border-right: 5px solid #000000; + border-bottom: 5px solid transparent; +} + +.popover.bottom .arrow { + top: 0; + left: 50%; + margin-left: -5px; + border-right: 5px solid transparent; + border-bottom: 5px solid #000000; + border-left: 5px solid transparent; +} + +.popover.left .arrow { + top: 50%; + right: 0; + margin-top: -5px; + border-top: 5px solid transparent; + border-bottom: 5px solid transparent; + border-left: 5px solid #000000; +} + +.popover .arrow { + position: absolute; + width: 0; + height: 0; +} + +.popover-inner { + width: 280px; + padding: 3px; + overflow: hidden; + background: #000000; + background: rgba(0, 0, 0, 0.8); + -webkit-border-radius: 6px; + -moz-border-radius: 6px; + border-radius: 6px; + -webkit-box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3); + -moz-box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3); + box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3); +} + +.popover-title { + padding: 9px 15px; + line-height: 1; + background-color: #f5f5f5; + border-bottom: 1px solid #eee; + -webkit-border-radius: 3px 3px 0 0; + -moz-border-radius: 3px 3px 0 0; + border-radius: 3px 3px 0 0; +} + +.popover-content { + padding: 14px; + background-color: #ffffff; + -webkit-border-radius: 0 0 3px 3px; + -moz-border-radius: 0 0 3px 3px; + border-radius: 0 0 3px 3px; + -webkit-background-clip: padding-box; + -moz-background-clip: padding-box; + background-clip: padding-box; +} + +.popover-content p, +.popover-content ul, +.popover-content ol { + margin-bottom: 0; +} + +.thumbnails { + margin-left: -20px; + list-style: none; + *zoom: 1; +} + +.thumbnails:before, +.thumbnails:after { + display: table; + content: ""; +} + +.thumbnails:after { + clear: both; +} + +.row-fluid .thumbnails { + margin-left: 0; +} + +.thumbnails > li { + float: left; + margin-bottom: 18px; + margin-left: 20px; +} + +.thumbnail { + display: block; + padding: 4px; + line-height: 1; + border: 1px solid #ddd; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; + -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.075); + -moz-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.075); + box-shadow: 0 1px 1px rgba(0, 0, 0, 0.075); +} + +a.thumbnail:hover { + border-color: #0088cc; + -webkit-box-shadow: 0 1px 4px rgba(0, 105, 214, 0.25); + -moz-box-shadow: 0 1px 4px rgba(0, 105, 214, 0.25); + box-shadow: 0 1px 4px rgba(0, 105, 214, 0.25); +} + +.thumbnail > img { + display: block; + max-width: 100%; + margin-right: auto; + margin-left: auto; +} + +.thumbnail .caption { + padding: 9px; +} + +.label, +.badge { + font-size: 10.998px; + font-weight: bold; + line-height: 14px; + color: #ffffff; + text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); + white-space: nowrap; + vertical-align: baseline; + background-color: #999999; +} + +.label { + padding: 1px 4px 2px; + -webkit-border-radius: 3px; + -moz-border-radius: 3px; + border-radius: 3px; +} + +.badge { + padding: 1px 9px 2px; + -webkit-border-radius: 9px; + -moz-border-radius: 9px; + border-radius: 9px; +} + +a.label:hover, +a.badge:hover { + color: #ffffff; + text-decoration: none; + cursor: pointer; +} + +.label-important, +.badge-important { + background-color: #b94a48; +} + +.label-important[href], +.badge-important[href] { + background-color: #953b39; +} + +.label-warning, +.badge-warning { + background-color: #f89406; +} + +.label-warning[href], +.badge-warning[href] { + background-color: #c67605; +} + +.label-success, +.badge-success { + background-color: #468847; +} + +.label-success[href], +.badge-success[href] { + background-color: #356635; +} + +.label-info, +.badge-info { + background-color: #3a87ad; +} + +.label-info[href], +.badge-info[href] { + background-color: #2d6987; +} + +.label-inverse, +.badge-inverse { + background-color: #333333; +} + +.label-inverse[href], +.badge-inverse[href] { + background-color: #1a1a1a; +} + +@-webkit-keyframes progress-bar-stripes { + from { + background-position: 40px 0; + } + to { + background-position: 0 0; + } +} + +@-moz-keyframes progress-bar-stripes { + from { + background-position: 40px 0; + } + to { + background-position: 0 0; + } +} + +@-ms-keyframes progress-bar-stripes { + from { + background-position: 40px 0; + } + to { + background-position: 0 0; + } +} + +@-o-keyframes progress-bar-stripes { + from { + background-position: 0 0; + } + to { + background-position: 40px 0; + } +} + +@keyframes progress-bar-stripes { + from { + background-position: 40px 0; + } + to { + background-position: 0 0; + } +} + +.progress { + height: 18px; + margin-bottom: 18px; + overflow: hidden; + background-color: #f7f7f7; + background-image: -moz-linear-gradient(top, #f5f5f5, #f9f9f9); + background-image: -ms-linear-gradient(top, #f5f5f5, #f9f9f9); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#f5f5f5), to(#f9f9f9)); + background-image: -webkit-linear-gradient(top, #f5f5f5, #f9f9f9); + background-image: -o-linear-gradient(top, #f5f5f5, #f9f9f9); + background-image: linear-gradient(top, #f5f5f5, #f9f9f9); + background-repeat: repeat-x; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#f5f5f5', endColorstr='#f9f9f9', GradientType=0); + -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); + -moz-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); + box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); +} + +.progress .bar { + width: 0; + height: 18px; + font-size: 12px; + color: #ffffff; + text-align: center; + text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); + background-color: #0e90d2; + background-image: -moz-linear-gradient(top, #149bdf, #0480be); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#149bdf), to(#0480be)); + background-image: -webkit-linear-gradient(top, #149bdf, #0480be); + background-image: -o-linear-gradient(top, #149bdf, #0480be); + background-image: linear-gradient(top, #149bdf, #0480be); + background-image: -ms-linear-gradient(top, #149bdf, #0480be); + background-repeat: repeat-x; + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#149bdf', endColorstr='#0480be', GradientType=0); + -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15); + -moz-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15); + box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15); + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + -ms-box-sizing: border-box; + box-sizing: border-box; + -webkit-transition: width 0.6s ease; + -moz-transition: width 0.6s ease; + -ms-transition: width 0.6s ease; + -o-transition: width 0.6s ease; + transition: width 0.6s ease; +} + +.progress-striped .bar { + background-color: #149bdf; + background-image: -o-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -webkit-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -moz-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -ms-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent)); + background-image: linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + -webkit-background-size: 40px 40px; + -moz-background-size: 40px 40px; + -o-background-size: 40px 40px; + background-size: 40px 40px; +} + +.progress.active .bar { + -webkit-animation: progress-bar-stripes 2s linear infinite; + -moz-animation: progress-bar-stripes 2s linear infinite; + -ms-animation: progress-bar-stripes 2s linear infinite; + -o-animation: progress-bar-stripes 2s linear infinite; + animation: progress-bar-stripes 2s linear infinite; +} + +.progress-danger .bar { + background-color: #dd514c; + background-image: -moz-linear-gradient(top, #ee5f5b, #c43c35); + background-image: -ms-linear-gradient(top, #ee5f5b, #c43c35); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ee5f5b), to(#c43c35)); + background-image: -webkit-linear-gradient(top, #ee5f5b, #c43c35); + background-image: -o-linear-gradient(top, #ee5f5b, #c43c35); + background-image: linear-gradient(top, #ee5f5b, #c43c35); + background-repeat: repeat-x; + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#ee5f5b', endColorstr='#c43c35', GradientType=0); +} + +.progress-danger.progress-striped .bar { + background-color: #ee5f5b; + background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent)); + background-image: -webkit-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -moz-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -ms-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -o-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); +} + +.progress-success .bar { + background-color: #5eb95e; + background-image: -moz-linear-gradient(top, #62c462, #57a957); + background-image: -ms-linear-gradient(top, #62c462, #57a957); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#62c462), to(#57a957)); + background-image: -webkit-linear-gradient(top, #62c462, #57a957); + background-image: -o-linear-gradient(top, #62c462, #57a957); + background-image: linear-gradient(top, #62c462, #57a957); + background-repeat: repeat-x; + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#62c462', endColorstr='#57a957', GradientType=0); +} + +.progress-success.progress-striped .bar { + background-color: #62c462; + background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent)); + background-image: -webkit-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -moz-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -ms-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -o-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); +} + +.progress-info .bar { + background-color: #4bb1cf; + background-image: -moz-linear-gradient(top, #5bc0de, #339bb9); + background-image: -ms-linear-gradient(top, #5bc0de, #339bb9); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#5bc0de), to(#339bb9)); + background-image: -webkit-linear-gradient(top, #5bc0de, #339bb9); + background-image: -o-linear-gradient(top, #5bc0de, #339bb9); + background-image: linear-gradient(top, #5bc0de, #339bb9); + background-repeat: repeat-x; + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#5bc0de', endColorstr='#339bb9', GradientType=0); +} + +.progress-info.progress-striped .bar { + background-color: #5bc0de; + background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent)); + background-image: -webkit-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -moz-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -ms-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -o-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); +} + +.progress-warning .bar { + background-color: #faa732; + background-image: -moz-linear-gradient(top, #fbb450, #f89406); + background-image: -ms-linear-gradient(top, #fbb450, #f89406); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#fbb450), to(#f89406)); + background-image: -webkit-linear-gradient(top, #fbb450, #f89406); + background-image: -o-linear-gradient(top, #fbb450, #f89406); + background-image: linear-gradient(top, #fbb450, #f89406); + background-repeat: repeat-x; + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#fbb450', endColorstr='#f89406', GradientType=0); +} + +.progress-warning.progress-striped .bar { + background-color: #fbb450; + background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent)); + background-image: -webkit-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -moz-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -ms-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -o-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); +} + +.accordion { + margin-bottom: 18px; +} + +.accordion-group { + margin-bottom: 2px; + border: 1px solid #e5e5e5; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; +} + +.accordion-heading { + border-bottom: 0; +} + +.accordion-heading .accordion-toggle { + display: block; + padding: 8px 15px; +} + +.accordion-toggle { + cursor: pointer; +} + +.accordion-inner { + padding: 9px 15px; + border-top: 1px solid #e5e5e5; +} + +.carousel { + position: relative; + margin-bottom: 18px; + line-height: 1; +} + +.carousel-inner { + position: relative; + width: 100%; + overflow: hidden; +} + +.carousel .item { + position: relative; + display: none; + -webkit-transition: 0.6s ease-in-out left; + -moz-transition: 0.6s ease-in-out left; + -ms-transition: 0.6s ease-in-out left; + -o-transition: 0.6s ease-in-out left; + transition: 0.6s ease-in-out left; +} + +.carousel .item > img { + display: block; + line-height: 1; +} + +.carousel .active, +.carousel .next, +.carousel .prev { + display: block; +} + +.carousel .active { + left: 0; +} + +.carousel .next, +.carousel .prev { + position: absolute; + top: 0; + width: 100%; +} + +.carousel .next { + left: 100%; +} + +.carousel .prev { + left: -100%; +} + +.carousel .next.left, +.carousel .prev.right { + left: 0; +} + +.carousel .active.left { + left: -100%; +} + +.carousel .active.right { + left: 100%; +} + +.carousel-control { + position: absolute; + top: 40%; + left: 15px; + width: 40px; + height: 40px; + margin-top: -20px; + font-size: 60px; + font-weight: 100; + line-height: 30px; + color: #ffffff; + text-align: center; + background: #222222; + border: 3px solid #ffffff; + -webkit-border-radius: 23px; + -moz-border-radius: 23px; + border-radius: 23px; + opacity: 0.5; + filter: alpha(opacity=50); +} + +.carousel-control.right { + right: 15px; + left: auto; +} + +.carousel-control:hover { + color: #ffffff; + text-decoration: none; + opacity: 0.9; + filter: alpha(opacity=90); +} + +.carousel-caption { + position: absolute; + right: 0; + bottom: 0; + left: 0; + padding: 10px 15px 5px; + background: #333333; + background: rgba(0, 0, 0, 0.75); +} + +.carousel-caption h4, +.carousel-caption p { + color: #ffffff; +} + +.hero-unit { + padding: 60px; + margin-bottom: 30px; + background-color: #eeeeee; + -webkit-border-radius: 6px; + -moz-border-radius: 6px; + border-radius: 6px; +} + +.hero-unit h1 { + margin-bottom: 0; + font-size: 60px; + line-height: 1; + letter-spacing: -1px; + color: inherit; +} + +.hero-unit p { + font-size: 18px; + font-weight: 200; + line-height: 27px; + color: inherit; +} + +.pull-right { + float: right; +} + +.pull-left { + float: left; +} + +.hide { + display: none; +} + +.show { + display: block; +} + +.invisible { + visibility: hidden; +} diff --git a/docs/css/bootstrap.min.css b/docs/css/bootstrap.min.css new file mode 100644 index 00000000..b74b4546 --- /dev/null +++ b/docs/css/bootstrap.min.css @@ -0,0 +1,9 @@ +/*! + * Bootstrap v2.0.4 + * + * Copyright 2012 Twitter, Inc + * Licensed under the Apache License v2.0 + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Designed and built with all the love in the world @twitter by @mdo and @fat. + 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#fff;-webkit-border-radius:23px;-moz-border-radius:23px;border-radius:23px;opacity:.5;filter:alpha(opacity=50)}.carousel-control.right{right:15px;left:auto}.carousel-control:hover{color:#fff;text-decoration:none;opacity:.9;filter:alpha(opacity=90)}.carousel-caption{position:absolute;right:0;bottom:0;left:0;padding:10px 15px 5px;background:#333;background:rgba(0,0,0,0.75)}.carousel-caption h4,.carousel-caption p{color:#fff}.hero-unit{padding:60px;margin-bottom:30px;background-color:#eee;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}.hero-unit h1{margin-bottom:0;font-size:60px;line-height:1;letter-spacing:-1px;color:inherit}.hero-unit p{font-size:18px;font-weight:200;line-height:27px;color:inherit}.pull-right{float:right}.pull-left{float:left}.hide{display:none}.show{display:block}.invisible{visibility:hidden} diff --git a/docs/css/highlight.css b/docs/css/highlight.css new file mode 100644 index 00000000..cd3ead6f --- /dev/null +++ b/docs/css/highlight.css @@ -0,0 +1,28 @@ +/* Syntax highlighting ---------------------------------------------------- */ + +pre .input { + border-left: 3px solid #ccc; + padding-left: 0.5em; +} +pre .output { + background-color: #eee; +} + +.number {color:rgb(21,20,181);} +.functioncall {color:#264D66 ;} +.string {color:#375D81 ;} +.keyword {font-weight:bolder ;color:black;} +.argument {color:#264D66 ;} +.comment {color: #4C886B;} +.formalargs {color: #264D66;} +.eqformalargs {color:#264D66;} +.slot {font-style:italic;} +.symbol {color:black ;} +.prompt {color:black ;} + +pre img { + background-color: #fff; + border: 1px solid #ccc; + display: block; + margin: 0.5em auto 0.5em auto; +} diff --git a/docs/css/staticdocs.css b/docs/css/staticdocs.css new file mode 100644 index 00000000..15d74084 --- /dev/null +++ b/docs/css/staticdocs.css @@ -0,0 +1,25 @@ +h2 {padding-top: 20px} + +.icon img { + float: right; + border: 1px solid #ccc; +} +.index .internal {display: none;} +ul.index li {margin-bottom: 0.5em; clear: both;} + +footer { + margin-top: 45px; + padding: 35px 0 36px; + border-top: 1px solid #e5e5e5; +} +footer p { + margin-bottom: 0; + color: #555; +} + +.sidebar h2 { + font-size: 1.1em; + padding-top: 0.5em; +} + +.sidebar li {margin-bottom: 0.5em;} diff --git a/docs/endpoints.html b/docs/endpoints.html new file mode 100644 index 00000000..376eefa9 --- /dev/null +++ b/docs/endpoints.html @@ -0,0 +1,121 @@ + + + + +endpoints. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
+
+
+ mkin 0.9.44.9000 +
+ +
+
+
+
+ + +
+
+ +
+ +

+Function to calculate endpoints for further use from kinetic models fitted with mkinfit +

+ +
+
+

Usage

+ 
endpoints(fit)
+ +

Arguments

+
+
fit
+
+ An object of class mkinfit. +
+
+ +
+

Description

+ +

This function calculates DT50 and DT90 values as well as formation fractions from kinetic models +fitted with mkinfit. If the SFORB model was specified for one of the parents or metabolites, +the Eigenvalues are returned. These are equivalent to the rate constantes of the DFOP model, but +with the advantage that the SFORB model can also be used for metabolites.

+ +
+ +
+

Note

+ +

The function is used internally by summary.mkinfit.

+ +
+ +
+

Value

+ +

+ A list with the components mentioned above. +

+ +
+
+
+ + + + +

Author

+ + Johannes Ranke + + +
+
+ + +
+ + \ No newline at end of file diff --git a/docs/geometric_mean.html b/docs/geometric_mean.html new file mode 100644 index 00000000..7b746fa3 --- /dev/null +++ b/docs/geometric_mean.html @@ -0,0 +1,114 @@ + + + + +geometric_mean. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
+
+
+ mkin 0.9.44.9000 +
+ +
+
+
+
+ + +
+
+ +
+ +

Calculate the geometric mean

+ +
+
+

Usage

+ 
geometric_mean(x, na.rm = FALSE)
+ +

Arguments

+
+
x
+
A numeric vector
+
na.rm
+
Should NA values be ignored
+
+ +
+

Description

+ +

Function calculating the geometric mean of numeric vectors

+ +
+ +
+

Value

+ +

+ The geometric mean. +

+ +
+ +

Examples

+ 
  geometric_mean(c(1,3, 9))

+
[1] 3
+

+
  geometric_mean(c(1,3, NA))

+
[1] NA
+

+
  geometric_mean(c(1,3, NA), na.rm = TRUE)

+
[1] 1.732051
+
+
+
+ + + + + +
+
+ + +
+ + \ No newline at end of file diff --git a/docs/ilr.html b/docs/ilr.html new file mode 100644 index 00000000..c5fea9cb --- /dev/null +++ b/docs/ilr.html @@ -0,0 +1,168 @@ + + + + +ilr. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
+
+
+ mkin 0.9.44.9000 +
+ +
+
+
+
+ + +
+
+ +
+ +

+ Function to perform isometric log-ratio transformation +

+ +
+
+

Usage

+ 
ilr(x)
+  invilr(x)
+ +

Arguments

+
+
x
+
+ A numeric vector. Naturally, the forward transformation is only sensible for + vectors with all elements being greater than zero. +
+
+ +
+

Description

+ +

This implementation is a special case of the class of isometric log-ratio transformations.

+ +
+ +
+

Value

+ +

+ The result of the forward or backward transformation. The returned components always + sum to 1 for the case of the inverse log-ratio transformation. +

+ +
+ +
+

References

+ +

Peter Filzmoser, Karel Hron (2008) Outlier Detection for Compositional Data Using Robust Methods. Math Geosci 40 233-248

+ +
+ +

Examples

+ 
# Order matters
+ilr(c(0.1, 1, 10))

+
[1] -1.628174 -2.820079
+

+
ilr(c(10, 1, 0.1))

+
[1] 1.628174 2.820079
+

+
# Equal entries give ilr transformations with zeros as elements
+ilr(c(3, 3, 3))

+
[1] 0 0
+

+
# Almost equal entries give small numbers
+ilr(c(0.3, 0.4, 0.3))

+
[1] -0.2034219  0.1174457
+

+
# Only the ratio between the numbers counts, not their sum
+invilr(ilr(c(0.7, 0.29, 0.01)))

+
[1] 0.70 0.29 0.01
+

+
invilr(ilr(2.1 * c(0.7, 0.29, 0.01)))

+
[1] 0.70 0.29 0.01
+

+
# Inverse transformation of larger numbers gives unequal elements
+invilr(-10)

+
[1] 7.213536e-07 9.999993e-01
+

+
invilr(c(-10, 0))

+
[1] 7.207415e-07 9.991507e-01 8.486044e-04
+

+
# The sum of the elements of the inverse ilr is 1
+sum(invilr(c(-10, 0)))

+
[1] 1
+

+
# This is why we do not need all elements of the inverse transformation to go back:
+a <- c(0.1, 0.3, 0.5)
+b <- invilr(a)
+length(b) # Four elements

+
[1] 4
+

+
ilr(c(b[1:3], 1 - sum(b[1:3]))) # Gives c(0.1, 0.3, 0.5)

+
[1] 0.1 0.3 0.5
+
+
+
+ + +

See also

+

Another implementation can be found in R package robCompositions.

+ + + +

Author

+ + René Lehmann and Johannes Ranke + + +
+
+ + +
+ + \ No newline at end of file diff --git a/docs/img/glyphicons-halflings-white.png b/docs/img/glyphicons-halflings-white.png new file mode 100644 index 00000000..3bf6484a Binary files /dev/null and b/docs/img/glyphicons-halflings-white.png differ diff --git a/docs/img/glyphicons-halflings.png b/docs/img/glyphicons-halflings.png new file mode 100644 index 00000000..79bc568c Binary files /dev/null and b/docs/img/glyphicons-halflings.png differ diff --git a/docs/index.html b/docs/index.html new file mode 100644 index 00000000..0f351a2d --- /dev/null +++ b/docs/index.html @@ -0,0 +1,244 @@ + + + + +Home. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
+
+
+ mkin 0.9.44.9000 +
+ +
+
+
+
+ + +
+
+ +
+ +
+
+

mkin

+ +

+ +

The R package mkin provides calculation routines for the analysis of +chemical degradation data, including multicompartment kinetics as +needed for modelling the formation and decline of transformation products, or +if several compartments are involved.

+ +

Installation

+ +

You can install the latest released version from +CRAN from within R:

+ +
install.packages("mkin")
+
+ +

Background

+ +

In the regulatory evaluation of chemical substances like plant protection +products (pesticides), biocides and other chemicals, degradation data play an +important role. For the evaluation of pesticide degradation experiments, +detailed guidance and helpful tools have been developed as detailed in +'Credits and historical remarks' below.

+ +

Usage

+ +

For a start, have a look a the code examples provided for +plot.mkinfit +and +plot.mmkin, and +at the package vignettes +FOCUS L and +FOCUS D.

+ +

Documentation

+ +

The HTML documentation is +maintained at the R-Forge project site.

+ +

Features

+ +
    +
  • Highly flexible model specification using +mkinmod, +including equilibrium reactions and using the single first-order +reversible binding (SFORB) model, which will automatically create +two latent state variables for the observed variable.
    • +
  • As of version 0.9-39, fitting of several models to several datasets, optionally in +parallel, is supported, see for example +plot.mmkin.
    • +
  • Model solution (forward modelling) in the function +mkinpredict +is performed either using the analytical solution for the case of +parent only degradation, an eigenvalue based solution if only simple +first-order (SFO) or SFORB kinetics are used in the model, or +using a numeric solver from the deSolve package (default is lsoda).
    • +
  • If a C compiler is installed, the kinetic models are compiled from automatically +generated C code, see
    +vignette compiled_models. +The autogeneration of C code was +inspired by the ccSolve package. Thanks +to Karline Soetaert for her work on that.
    • +
  • By default, kinetic rate constants and kinetic formation fractions are +transformed internally using +transform_odeparms +so their estimators can more reasonably be expected to follow +a normal distribution. This has the side effect that no constraints +are needed in the optimisation. Thanks to René Lehmann for the nice +cooperation on this, especially the isometric logration transformation +that is now used for the formation fractions.
    • +
  • A side effect of this is that when parameter estimates are backtransformed +to match the model definition, confidence intervals calculated from +standard errors are also backtransformed to the correct scale, and will +not include meaningless values like negative rate constants or +formation fractions adding up to more than 1, which can not occur in +a single experiment with a single defined radiolabel position.
    • +
  • The usual one-sided t-test for significant difference from zero is nevertheless +shown based on estimators for the untransformed parameters.
    • +
  • Summary and plotting functions. The summary of an mkinfit object is in +fact a full report that should give enough information to be able to +approximately reproduce the fit with other tools.
    • +
  • The chi-squared error level as defined in the FOCUS kinetics guidance +(see below) is calculated for each observed variable.
    • +
  • Iteratively reweighted least squares fitting is implemented in a similar way +as in KinGUII and CAKE (see below). Simply add the argument +reweight = "obs" to your call to mkinfit and a separate variance +componenent for each of the observed variables will be optimised +in a second stage after the primary optimisation algorithm has converged.
    • +
  • When a metabolite decline phase is not described well by SFO kinetics, +SFORB kinetics can be used for the metabolite.
    • +
+ +

GUI

+ +

There is a graphical user interface that I consider useful for real work. Please +refer to its documentation page +for installation instructions and a manual.

+ +

News

+ +

Yes, there is a ChangeLog, for the latest CRAN release +and one for the github master branch.

+ +

Credits and historical remarks

+ +

mkin would not be possible without the underlying software stack consisting +of R and the packages deSolve +and FME, to say the least.

+ +

It could not have been written without me being introduced to regulatory fate +modelling of pesticides by Adrian Gurney during my time at Harlan Laboratories +Ltd (formerly RCC Ltd). mkin greatly profits from and largely follows +the work done by the +FOCUS Degradation Kinetics Workgroup, +as detailed in their guidance document from 2006, slightly updated in 2011 and +2014.

+ +

Also, it was inspired by the first version of KinGUI developed by +BayerCropScience, which is based on the MatLab runtime environment.

+ +

The companion package +kinfit (now deprecated) was +started in 2008 and +first published on +CRAN on 01 May 2010.

+ +

The first mkin code was +published on 11 May 2010 and the +first CRAN version +on 18 May 2010.

+ +

In 2011, Bayer Crop Science started to distribute an R based successor to KinGUI named +KinGUII whose R code is based on mkin, but which added, amongst other +refinements, a closed source graphical user interface (GUI), iteratively +reweighted least squares (IRLS) optimisation of the variance for each of the +observed variables, and Markov Chain Monte Carlo (MCMC) simulation +functionality, similar to what is available e.g. in the FME package.

+ +

Somewhat in parallel, Syngenta has sponsored the development of an mkin and +KinGUII based GUI application called CAKE, which also adds IRLS and MCMC, is +more limited in the model formulation, but puts more weight on usability. +CAKE is available for download from the CAKE +website, where you can also +find a zip archive of the R scripts derived from mkin, published under the GPL +license.

+ +

Finally, there is +KineticEval, which contains +a further development of the scripts used for KinGUII, so the different tools +will hopefully be able to learn from each other in the future as well.

+ +

Development

+ +

Contributions are welcome! Your +mkin fork is just a mouse click +away… The master branch on github should always be in good shape, I implement +new features in separate branches now. If you prefer subversion, project +members for the +r-forge project are welcome as well. +Generally, the source code of the latest CRAN version should be available there. +You can also browse the source code at cgit.jrwb.de/mkin.

+ +
+ +
+

Vignettes

+ +

Authors

+
    +
  • Johannes Ranke [aut, cre, cph]
    • +
  • Katrin Lindenberger [ctb]
    • +
  • René Lehmann [ctb]
    • +
  • Eurofins Regulatory AG [cph]
    • +
+
+
+ + +
+ + \ No newline at end of file diff --git a/docs/js/bootstrap.js b/docs/js/bootstrap.js new file mode 100644 index 00000000..5d6e65b8 --- /dev/null +++ b/docs/js/bootstrap.js @@ -0,0 +1,1825 @@ +/* =================================================== + * bootstrap-transition.js v2.0.4 + * http://twitter.github.com/bootstrap/javascript.html#transitions + * =================================================== + * Copyright 2012 Twitter, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ========================================================== */ + + +!function ($) { + + $(function () { + + "use strict"; // jshint ;_; + + + /* CSS TRANSITION SUPPORT (http://www.modernizr.com/) + * ======================================================= */ + + $.support.transition = (function () { + + var transitionEnd = (function () { + + var el = document.createElement('bootstrap') + , transEndEventNames = { + 'WebkitTransition' : 'webkitTransitionEnd' + , 'MozTransition' : 'transitionend' + , 'OTransition' : 'oTransitionEnd' + , 'msTransition' : 'MSTransitionEnd' + , 'transition' : 'transitionend' + } + , name + + for (name in transEndEventNames){ + if (el.style[name] !== undefined) { + return transEndEventNames[name] + } + } + + }()) + + return transitionEnd && { + end: transitionEnd + } + + })() + + }) + +}(window.jQuery);/* ========================================================== + * bootstrap-alert.js v2.0.4 + * http://twitter.github.com/bootstrap/javascript.html#alerts + * ========================================================== + * Copyright 2012 Twitter, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ========================================================== */ + + +!function ($) { + + "use strict"; // jshint ;_; + + + /* ALERT CLASS DEFINITION + * ====================== */ + + var dismiss = '[data-dismiss="alert"]' + , Alert = function (el) { + $(el).on('click', dismiss, this.close) + } + + Alert.prototype.close = function (e) { + var $this = $(this) + , selector = $this.attr('data-target') + , $parent + + if (!selector) { + selector = $this.attr('href') + selector = selector && selector.replace(/.*(?=#[^\s]*$)/, '') //strip for ie7 + } + + $parent = $(selector) + + e && e.preventDefault() + + $parent.length || ($parent = $this.hasClass('alert') ? $this : $this.parent()) + + $parent.trigger(e = $.Event('close')) + + if (e.isDefaultPrevented()) return + + $parent.removeClass('in') + + function removeElement() { + $parent + .trigger('closed') + .remove() + } + + $.support.transition && $parent.hasClass('fade') ? + $parent.on($.support.transition.end, removeElement) : + removeElement() + } + + + /* ALERT PLUGIN DEFINITION + * ======================= */ + + $.fn.alert = function (option) { + return this.each(function () { + var $this = $(this) + , data = $this.data('alert') + if (!data) $this.data('alert', (data = new Alert(this))) + if (typeof option == 'string') data[option].call($this) + }) + } + + $.fn.alert.Constructor = Alert + + + /* ALERT DATA-API + * ============== */ + + $(function () { + $('body').on('click.alert.data-api', dismiss, Alert.prototype.close) + }) + +}(window.jQuery);/* ============================================================ + * bootstrap-button.js v2.0.4 + * http://twitter.github.com/bootstrap/javascript.html#buttons + * ============================================================ + * Copyright 2012 Twitter, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================ */ + + +!function ($) { + + "use strict"; // jshint ;_; + + + /* BUTTON PUBLIC CLASS DEFINITION + * ============================== */ + + var Button = function (element, options) { + this.$element = $(element) + this.options = $.extend({}, $.fn.button.defaults, options) + } + + Button.prototype.setState = function (state) { + var d = 'disabled' + , $el = this.$element + , data = $el.data() + , val = $el.is('input') ? 'val' : 'html' + + state = state + 'Text' + data.resetText || $el.data('resetText', $el[val]()) + + $el[val](data[state] || this.options[state]) + + // push to event loop to allow forms to submit + setTimeout(function () { + state == 'loadingText' ? + $el.addClass(d).attr(d, d) : + $el.removeClass(d).removeAttr(d) + }, 0) + } + + Button.prototype.toggle = function () { + var $parent = this.$element.parent('[data-toggle="buttons-radio"]') + + $parent && $parent + .find('.active') + .removeClass('active') + + this.$element.toggleClass('active') + } + + + /* BUTTON PLUGIN DEFINITION + * ======================== */ + + $.fn.button = function (option) { + return this.each(function () { + var $this = $(this) + , data = $this.data('button') + , options = typeof option == 'object' && option + if (!data) $this.data('button', (data = new Button(this, options))) + if (option == 'toggle') data.toggle() + else if (option) data.setState(option) + }) + } + + $.fn.button.defaults = { + loadingText: 'loading...' + } + + $.fn.button.Constructor = Button + + + /* BUTTON DATA-API + * =============== */ + + $(function () { + $('body').on('click.button.data-api', '[data-toggle^=button]', function ( e ) { + var $btn = $(e.target) + if (!$btn.hasClass('btn')) $btn = $btn.closest('.btn') + $btn.button('toggle') + }) + }) + +}(window.jQuery);/* ========================================================== + * bootstrap-carousel.js v2.0.4 + * http://twitter.github.com/bootstrap/javascript.html#carousel + * ========================================================== + * Copyright 2012 Twitter, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ========================================================== */ + + +!function ($) { + + "use strict"; // jshint ;_; + + + /* CAROUSEL CLASS DEFINITION + * ========================= */ + + var Carousel = function (element, options) { + this.$element = $(element) + this.options = options + this.options.slide && this.slide(this.options.slide) + this.options.pause == 'hover' && this.$element + .on('mouseenter', $.proxy(this.pause, this)) + .on('mouseleave', $.proxy(this.cycle, this)) + } + + Carousel.prototype = { + + cycle: function (e) { + if (!e) this.paused = false + this.options.interval + && !this.paused + && (this.interval = setInterval($.proxy(this.next, this), this.options.interval)) + return this + } + + , to: function (pos) { + var $active = this.$element.find('.active') + , children = $active.parent().children() + , activePos = children.index($active) + , that = this + + if (pos > (children.length - 1) || pos < 0) return + + if (this.sliding) { + return this.$element.one('slid', function () { + that.to(pos) + }) + } + + if (activePos == pos) { + return this.pause().cycle() + } + + return this.slide(pos > activePos ? 'next' : 'prev', $(children[pos])) + } + + , pause: function (e) { + if (!e) this.paused = true + clearInterval(this.interval) + this.interval = null + return this + } + + , next: function () { + if (this.sliding) return + return this.slide('next') + } + + , prev: function () { + if (this.sliding) return + return this.slide('prev') + } + + , slide: function (type, next) { + var $active = this.$element.find('.active') + , $next = next || $active[type]() + , isCycling = this.interval + , direction = type == 'next' ? 'left' : 'right' + , fallback = type == 'next' ? 'first' : 'last' + , that = this + , e = $.Event('slide') + + this.sliding = true + + isCycling && this.pause() + + $next = $next.length ? $next : this.$element.find('.item')[fallback]() + + if ($next.hasClass('active')) return + + if ($.support.transition && this.$element.hasClass('slide')) { + this.$element.trigger(e) + if (e.isDefaultPrevented()) return + $next.addClass(type) + $next[0].offsetWidth // force reflow + $active.addClass(direction) + $next.addClass(direction) + this.$element.one($.support.transition.end, function () { + $next.removeClass([type, direction].join(' ')).addClass('active') + $active.removeClass(['active', direction].join(' ')) + that.sliding = false + setTimeout(function () { that.$element.trigger('slid') }, 0) + }) + } else { + this.$element.trigger(e) + if (e.isDefaultPrevented()) return + $active.removeClass('active') + $next.addClass('active') + this.sliding = false + this.$element.trigger('slid') + } + + isCycling && this.cycle() + + return this + } + + } + + + /* CAROUSEL PLUGIN DEFINITION + * ========================== */ + + $.fn.carousel = function (option) { + return this.each(function () { + var $this = $(this) + , data = $this.data('carousel') + , options = $.extend({}, $.fn.carousel.defaults, typeof option == 'object' && option) + if (!data) $this.data('carousel', (data = new Carousel(this, options))) + if (typeof option == 'number') data.to(option) + else if (typeof option == 'string' || (option = options.slide)) data[option]() + else if (options.interval) data.cycle() + }) + } + + $.fn.carousel.defaults = { + interval: 5000 + , pause: 'hover' + } + + $.fn.carousel.Constructor = Carousel + + + /* CAROUSEL DATA-API + * ================= */ + + $(function () { + $('body').on('click.carousel.data-api', '[data-slide]', function ( e ) { + var $this = $(this), href + , $target = $($this.attr('data-target') || (href = $this.attr('href')) && href.replace(/.*(?=#[^\s]+$)/, '')) //strip for ie7 + , options = !$target.data('modal') && $.extend({}, $target.data(), $this.data()) + $target.carousel(options) + e.preventDefault() + }) + }) + +}(window.jQuery);/* ============================================================= + * bootstrap-collapse.js v2.0.4 + * http://twitter.github.com/bootstrap/javascript.html#collapse + * ============================================================= + * Copyright 2012 Twitter, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================ */ + + +!function ($) { + + "use strict"; // jshint ;_; + + + /* COLLAPSE PUBLIC CLASS DEFINITION + * ================================ */ + + var Collapse = function (element, options) { + this.$element = $(element) + this.options = $.extend({}, $.fn.collapse.defaults, options) + + if (this.options.parent) { + this.$parent = $(this.options.parent) + } + + this.options.toggle && this.toggle() + } + + Collapse.prototype = { + + constructor: Collapse + + , dimension: function () { + var hasWidth = this.$element.hasClass('width') + return hasWidth ? 'width' : 'height' + } + + , show: function () { + var dimension + , scroll + , actives + , hasData + + if (this.transitioning) return + + dimension = this.dimension() + scroll = $.camelCase(['scroll', dimension].join('-')) + actives = this.$parent && this.$parent.find('> .accordion-group > .in') + + if (actives && actives.length) { + hasData = actives.data('collapse') + if (hasData && hasData.transitioning) return + actives.collapse('hide') + hasData || actives.data('collapse', null) + } + + this.$element[dimension](0) + this.transition('addClass', $.Event('show'), 'shown') + this.$element[dimension](this.$element[0][scroll]) + } + + , hide: function () { + var dimension + if (this.transitioning) return + dimension = this.dimension() + this.reset(this.$element[dimension]()) + this.transition('removeClass', $.Event('hide'), 'hidden') + this.$element[dimension](0) + } + + , reset: function (size) { + var dimension = this.dimension() + + this.$element + .removeClass('collapse') + [dimension](size || 'auto') + [0].offsetWidth + + this.$element[size !== null ? 'addClass' : 'removeClass']('collapse') + + return this + } + + , transition: function (method, startEvent, completeEvent) { + var that = this + , complete = function () { + if (startEvent.type == 'show') that.reset() + that.transitioning = 0 + that.$element.trigger(completeEvent) + } + + this.$element.trigger(startEvent) + + if (startEvent.isDefaultPrevented()) return + + this.transitioning = 1 + + this.$element[method]('in') + + $.support.transition && this.$element.hasClass('collapse') ? + this.$element.one($.support.transition.end, complete) : + complete() + } + + , toggle: function () { + this[this.$element.hasClass('in') ? 'hide' : 'show']() + } + + } + + + /* COLLAPSIBLE PLUGIN DEFINITION + * ============================== */ + + $.fn.collapse = function (option) { + return this.each(function () { + var $this = $(this) + , data = $this.data('collapse') + , options = typeof option == 'object' && option + if (!data) $this.data('collapse', (data = new Collapse(this, options))) + if (typeof option == 'string') data[option]() + }) + } + + $.fn.collapse.defaults = { + toggle: true + } + + $.fn.collapse.Constructor = Collapse + + + /* COLLAPSIBLE DATA-API + * ==================== */ + + $(function () { + $('body').on('click.collapse.data-api', '[data-toggle=collapse]', function ( e ) { + var $this = $(this), href + , target = $this.attr('data-target') + || e.preventDefault() + || (href = $this.attr('href')) && href.replace(/.*(?=#[^\s]+$)/, '') //strip for ie7 + , option = $(target).data('collapse') ? 'toggle' : $this.data() + $(target).collapse(option) + }) + }) + +}(window.jQuery);/* ============================================================ + * bootstrap-dropdown.js v2.0.4 + * http://twitter.github.com/bootstrap/javascript.html#dropdowns + * ============================================================ + * Copyright 2012 Twitter, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================ */ + + +!function ($) { + + "use strict"; // jshint ;_; + + + /* DROPDOWN CLASS DEFINITION + * ========================= */ + + var toggle = '[data-toggle="dropdown"]' + , Dropdown = function (element) { + var $el = $(element).on('click.dropdown.data-api', this.toggle) + $('html').on('click.dropdown.data-api', function () { + $el.parent().removeClass('open') + }) + } + + Dropdown.prototype = { + + constructor: Dropdown + + , toggle: function (e) { + var $this = $(this) + , $parent + , selector + , isActive + + if ($this.is('.disabled, :disabled')) return + + selector = $this.attr('data-target') + + if (!selector) { + selector = $this.attr('href') + selector = selector && selector.replace(/.*(?=#[^\s]*$)/, '') //strip for ie7 + } + + $parent = $(selector) + $parent.length || ($parent = $this.parent()) + + isActive = $parent.hasClass('open') + + clearMenus() + + if (!isActive) $parent.toggleClass('open') + + return false + } + + } + + function clearMenus() { + $(toggle).parent().removeClass('open') + } + + + /* DROPDOWN PLUGIN DEFINITION + * ========================== */ + + $.fn.dropdown = function (option) { + return this.each(function () { + var $this = $(this) + , data = $this.data('dropdown') + if (!data) $this.data('dropdown', (data = new Dropdown(this))) + if (typeof option == 'string') data[option].call($this) + }) + } + + $.fn.dropdown.Constructor = Dropdown + + + /* APPLY TO STANDARD DROPDOWN ELEMENTS + * =================================== */ + + $(function () { + $('html').on('click.dropdown.data-api', clearMenus) + $('body') + .on('click.dropdown', '.dropdown form', function (e) { e.stopPropagation() }) + .on('click.dropdown.data-api', toggle, Dropdown.prototype.toggle) + }) + +}(window.jQuery);/* ========================================================= + * bootstrap-modal.js v2.0.4 + * http://twitter.github.com/bootstrap/javascript.html#modals + * ========================================================= + * Copyright 2012 Twitter, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ========================================================= */ + + +!function ($) { + + "use strict"; // jshint ;_; + + + /* MODAL CLASS DEFINITION + * ====================== */ + + var Modal = function (content, options) { + this.options = options + this.$element = $(content) + .delegate('[data-dismiss="modal"]', 'click.dismiss.modal', $.proxy(this.hide, this)) + } + + Modal.prototype = { + + constructor: Modal + + , toggle: function () { + return this[!this.isShown ? 'show' : 'hide']() + } + + , show: function () { + var that = this + , e = $.Event('show') + + this.$element.trigger(e) + + if (this.isShown || e.isDefaultPrevented()) return + + $('body').addClass('modal-open') + + this.isShown = true + + escape.call(this) + backdrop.call(this, function () { + var transition = $.support.transition && that.$element.hasClass('fade') + + if (!that.$element.parent().length) { + that.$element.appendTo(document.body) //don't move modals dom position + } + + that.$element + .show() + + if (transition) { + that.$element[0].offsetWidth // force reflow + } + + that.$element.addClass('in') + + transition ? + that.$element.one($.support.transition.end, function () { that.$element.trigger('shown') }) : + that.$element.trigger('shown') + + }) + } + + , hide: function (e) { + e && e.preventDefault() + + var that = this + + e = $.Event('hide') + + this.$element.trigger(e) + + if (!this.isShown || e.isDefaultPrevented()) return + + this.isShown = false + + $('body').removeClass('modal-open') + + escape.call(this) + + this.$element.removeClass('in') + + $.support.transition && this.$element.hasClass('fade') ? + hideWithTransition.call(this) : + hideModal.call(this) + } + + } + + + /* MODAL PRIVATE METHODS + * ===================== */ + + function hideWithTransition() { + var that = this + , timeout = setTimeout(function () { + that.$element.off($.support.transition.end) + hideModal.call(that) + }, 500) + + this.$element.one($.support.transition.end, function () { + clearTimeout(timeout) + hideModal.call(that) + }) + } + + function hideModal(that) { + this.$element + .hide() + .trigger('hidden') + + backdrop.call(this) + } + + function backdrop(callback) { + var that = this + , animate = this.$element.hasClass('fade') ? 'fade' : '' + + if (this.isShown && this.options.backdrop) { + var doAnimate = $.support.transition && animate + + this.$backdrop = $('
') + .appendTo(document.body) + + if (this.options.backdrop != 'static') { + this.$backdrop.click($.proxy(this.hide, this)) + } + + if (doAnimate) this.$backdrop[0].offsetWidth // force reflow + + this.$backdrop.addClass('in') + + doAnimate ? + this.$backdrop.one($.support.transition.end, callback) : + callback() + + } else if (!this.isShown && this.$backdrop) { + this.$backdrop.removeClass('in') + + $.support.transition && this.$element.hasClass('fade')? + this.$backdrop.one($.support.transition.end, $.proxy(removeBackdrop, this)) : + removeBackdrop.call(this) + + } else if (callback) { + callback() + } + } + + function removeBackdrop() { + this.$backdrop.remove() + this.$backdrop = null + } + + function escape() { + var that = this + if (this.isShown && this.options.keyboard) { + $(document).on('keyup.dismiss.modal', function ( e ) { + e.which == 27 && that.hide() + }) + } else if (!this.isShown) { + $(document).off('keyup.dismiss.modal') + } + } + + + /* MODAL PLUGIN DEFINITION + * ======================= */ + + $.fn.modal = function (option) { + return this.each(function () { + var $this = $(this) + , data = $this.data('modal') + , options = $.extend({}, $.fn.modal.defaults, $this.data(), typeof option == 'object' && option) + if (!data) $this.data('modal', (data = new Modal(this, options))) + if (typeof option == 'string') data[option]() + else if (options.show) data.show() + }) + } + + $.fn.modal.defaults = { + backdrop: true + , keyboard: true + , show: true + } + + $.fn.modal.Constructor = Modal + + + /* MODAL DATA-API + * ============== */ + + $(function () { + $('body').on('click.modal.data-api', '[data-toggle="modal"]', function ( e ) { + var $this = $(this), href + , $target = $($this.attr('data-target') || (href = $this.attr('href')) && href.replace(/.*(?=#[^\s]+$)/, '')) //strip for ie7 + , option = $target.data('modal') ? 'toggle' : $.extend({}, $target.data(), $this.data()) + + e.preventDefault() + $target.modal(option) + }) + }) + +}(window.jQuery);/* =========================================================== + * bootstrap-tooltip.js v2.0.4 + * http://twitter.github.com/bootstrap/javascript.html#tooltips + * Inspired by the original jQuery.tipsy by Jason Frame + * =========================================================== + * Copyright 2012 Twitter, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ========================================================== */ + + +!function ($) { + + "use strict"; // jshint ;_; + + + /* TOOLTIP PUBLIC CLASS DEFINITION + * =============================== */ + + var Tooltip = function (element, options) { + this.init('tooltip', element, options) + } + + Tooltip.prototype = { + + constructor: Tooltip + + , init: function (type, element, options) { + var eventIn + , eventOut + + this.type = type + this.$element = $(element) + this.options = this.getOptions(options) + this.enabled = true + + if (this.options.trigger != 'manual') { + eventIn = this.options.trigger == 'hover' ? 'mouseenter' : 'focus' + eventOut = this.options.trigger == 'hover' ? 'mouseleave' : 'blur' + this.$element.on(eventIn, this.options.selector, $.proxy(this.enter, this)) + this.$element.on(eventOut, this.options.selector, $.proxy(this.leave, this)) + } + + this.options.selector ? + (this._options = $.extend({}, this.options, { trigger: 'manual', selector: '' })) : + this.fixTitle() + } + + , getOptions: function (options) { + options = $.extend({}, $.fn[this.type].defaults, options, this.$element.data()) + + if (options.delay && typeof options.delay == 'number') { + options.delay = { + show: options.delay + , hide: options.delay + } + } + + return options + } + + , enter: function (e) { + var self = $(e.currentTarget)[this.type](this._options).data(this.type) + + if (!self.options.delay || !self.options.delay.show) return self.show() + + clearTimeout(this.timeout) + self.hoverState = 'in' + this.timeout = setTimeout(function() { + if (self.hoverState == 'in') self.show() + }, self.options.delay.show) + } + + , leave: function (e) { + var self = $(e.currentTarget)[this.type](this._options).data(this.type) + + if (this.timeout) clearTimeout(this.timeout) + if (!self.options.delay || !self.options.delay.hide) return self.hide() + + self.hoverState = 'out' + this.timeout = setTimeout(function() { + if (self.hoverState == 'out') self.hide() + }, self.options.delay.hide) + } + + , show: function () { + var $tip + , inside + , pos + , actualWidth + , actualHeight + , placement + , tp + + if (this.hasContent() && this.enabled) { + $tip = this.tip() + this.setContent() + + if (this.options.animation) { + $tip.addClass('fade') + } + + placement = typeof this.options.placement == 'function' ? + this.options.placement.call(this, $tip[0], this.$element[0]) : + this.options.placement + + inside = /in/.test(placement) + + $tip + .remove() + .css({ top: 0, left: 0, display: 'block' }) + .appendTo(inside ? this.$element : document.body) + + pos = this.getPosition(inside) + + actualWidth = $tip[0].offsetWidth + actualHeight = $tip[0].offsetHeight + + switch (inside ? placement.split(' ')[1] : placement) { + case 'bottom': + tp = {top: pos.top + pos.height, left: pos.left + pos.width / 2 - actualWidth / 2} + break + case 'top': + tp = {top: pos.top - actualHeight, left: pos.left + pos.width / 2 - actualWidth / 2} + break + case 'left': + tp = {top: pos.top + pos.height / 2 - actualHeight / 2, left: pos.left - actualWidth} + break + case 'right': + tp = {top: pos.top + pos.height / 2 - actualHeight / 2, left: pos.left + pos.width} + break + } + + $tip + .css(tp) + .addClass(placement) + .addClass('in') + } + } + + , isHTML: function(text) { + // html string detection logic adapted from jQuery + return typeof text != 'string' + || ( text.charAt(0) === "<" + && text.charAt( text.length - 1 ) === ">" + && text.length >= 3 + ) || /^(?:[^<]*<[\w\W]+>[^>]*$)/.exec(text) + } + + , setContent: function () { + var $tip = this.tip() + , title = this.getTitle() + + $tip.find('.tooltip-inner')[this.isHTML(title) ? 'html' : 'text'](title) + $tip.removeClass('fade in top bottom left right') + } + + , hide: function () { + var that = this + , $tip = this.tip() + + $tip.removeClass('in') + + function removeWithAnimation() { + var timeout = setTimeout(function () { + $tip.off($.support.transition.end).remove() + }, 500) + + $tip.one($.support.transition.end, function () { + clearTimeout(timeout) + $tip.remove() + }) + } + + $.support.transition && this.$tip.hasClass('fade') ? + removeWithAnimation() : + $tip.remove() + } + + , fixTitle: function () { + var $e = this.$element + if ($e.attr('title') || typeof($e.attr('data-original-title')) != 'string') { + $e.attr('data-original-title', $e.attr('title') || '').removeAttr('title') + } + } + + , hasContent: function () { + return this.getTitle() + } + + , getPosition: function (inside) { + return $.extend({}, (inside ? {top: 0, left: 0} : this.$element.offset()), { + width: this.$element[0].offsetWidth + , height: this.$element[0].offsetHeight + }) + } + + , getTitle: function () { + var title + , $e = this.$element + , o = this.options + + title = $e.attr('data-original-title') + || (typeof o.title == 'function' ? o.title.call($e[0]) : o.title) + + return title + } + + , tip: function () { + return this.$tip = this.$tip || $(this.options.template) + } + + , validate: function () { + if (!this.$element[0].parentNode) { + this.hide() + this.$element = null + this.options = null + } + } + + , enable: function () { + this.enabled = true + } + + , disable: function () { + this.enabled = false + } + + , toggleEnabled: function () { + this.enabled = !this.enabled + } + + , toggle: function () { + this[this.tip().hasClass('in') ? 'hide' : 'show']() + } + + } + + + /* TOOLTIP PLUGIN DEFINITION + * ========================= */ + + $.fn.tooltip = function ( option ) { + return this.each(function () { + var $this = $(this) + , data = $this.data('tooltip') + , options = typeof option == 'object' && option + if (!data) $this.data('tooltip', (data = new Tooltip(this, options))) + if (typeof option == 'string') data[option]() + }) + } + + $.fn.tooltip.Constructor = Tooltip + + $.fn.tooltip.defaults = { + animation: true + , placement: 'top' + , selector: false + , template: '
' + , trigger: 'hover' + , title: '' + , delay: 0 + } + +}(window.jQuery); +/* =========================================================== + * bootstrap-popover.js v2.0.4 + * http://twitter.github.com/bootstrap/javascript.html#popovers + * =========================================================== + * Copyright 2012 Twitter, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * =========================================================== */ + + +!function ($) { + + "use strict"; // jshint ;_; + + + /* POPOVER PUBLIC CLASS DEFINITION + * =============================== */ + + var Popover = function ( element, options ) { + this.init('popover', element, options) + } + + + /* NOTE: POPOVER EXTENDS BOOTSTRAP-TOOLTIP.js + ========================================== */ + + Popover.prototype = $.extend({}, $.fn.tooltip.Constructor.prototype, { + + constructor: Popover + + , setContent: function () { + var $tip = this.tip() + , title = this.getTitle() + , content = this.getContent() + + $tip.find('.popover-title')[this.isHTML(title) ? 'html' : 'text'](title) + $tip.find('.popover-content > *')[this.isHTML(content) ? 'html' : 'text'](content) + + $tip.removeClass('fade top bottom left right in') + } + + , hasContent: function () { + return this.getTitle() || this.getContent() + } + + , getContent: function () { + var content + , $e = this.$element + , o = this.options + + content = $e.attr('data-content') + || (typeof o.content == 'function' ? o.content.call($e[0]) : o.content) + + return content + } + + , tip: function () { + if (!this.$tip) { + this.$tip = $(this.options.template) + } + return this.$tip + } + + }) + + + /* POPOVER PLUGIN DEFINITION + * ======================= */ + + $.fn.popover = function (option) { + return this.each(function () { + var $this = $(this) + , data = $this.data('popover') + , options = typeof option == 'object' && option + if (!data) $this.data('popover', (data = new Popover(this, options))) + if (typeof option == 'string') data[option]() + }) + } + + $.fn.popover.Constructor = Popover + + $.fn.popover.defaults = $.extend({} , $.fn.tooltip.defaults, { + placement: 'right' + , content: '' + , template: '

' + }) + +}(window.jQuery);/* ============================================================= + * bootstrap-scrollspy.js v2.0.4 + * http://twitter.github.com/bootstrap/javascript.html#scrollspy + * ============================================================= + * Copyright 2012 Twitter, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================== */ + + +!function ($) { + + "use strict"; // jshint ;_; + + + /* SCROLLSPY CLASS DEFINITION + * ========================== */ + + function ScrollSpy( element, options) { + var process = $.proxy(this.process, this) + , $element = $(element).is('body') ? $(window) : $(element) + , href + this.options = $.extend({}, $.fn.scrollspy.defaults, options) + this.$scrollElement = $element.on('scroll.scroll.data-api', process) + this.selector = (this.options.target + || ((href = $(element).attr('href')) && href.replace(/.*(?=#[^\s]+$)/, '')) //strip for ie7 + || '') + ' .nav li > a' + this.$body = $('body') + this.refresh() + this.process() + } + + ScrollSpy.prototype = { + + constructor: ScrollSpy + + , refresh: function () { + var self = this + , $targets + + this.offsets = $([]) + this.targets = $([]) + + $targets = this.$body + .find(this.selector) + .map(function () { + var $el = $(this) + , href = $el.data('target') || $el.attr('href') + , $href = /^#\w/.test(href) && $(href) + return ( $href + && href.length + && [[ $href.position().top, href ]] ) || null + }) + .sort(function (a, b) { return a[0] - b[0] }) + .each(function () { + self.offsets.push(this[0]) + self.targets.push(this[1]) + }) + } + + , process: function () { + var scrollTop = this.$scrollElement.scrollTop() + this.options.offset + , scrollHeight = this.$scrollElement[0].scrollHeight || this.$body[0].scrollHeight + , maxScroll = scrollHeight - this.$scrollElement.height() + , offsets = this.offsets + , targets = this.targets + , activeTarget = this.activeTarget + , i + + if (scrollTop >= maxScroll) { + return activeTarget != (i = targets.last()[0]) + && this.activate ( i ) + } + + for (i = offsets.length; i--;) { + activeTarget != targets[i] + && scrollTop >= offsets[i] + && (!offsets[i + 1] || scrollTop <= offsets[i + 1]) + && this.activate( targets[i] ) + } + } + + , activate: function (target) { + var active + , selector + + this.activeTarget = target + + $(this.selector) + .parent('.active') + .removeClass('active') + + selector = this.selector + + '[data-target="' + target + '"],' + + this.selector + '[href="' + target + '"]' + + active = $(selector) + .parent('li') + .addClass('active') + + if (active.parent('.dropdown-menu')) { + active = active.closest('li.dropdown').addClass('active') + } + + active.trigger('activate') + } + + } + + + /* SCROLLSPY PLUGIN DEFINITION + * =========================== */ + + $.fn.scrollspy = function ( option ) { + return this.each(function () { + var $this = $(this) + , data = $this.data('scrollspy') + , options = typeof option == 'object' && option + if (!data) $this.data('scrollspy', (data = new ScrollSpy(this, options))) + if (typeof option == 'string') data[option]() + }) + } + + $.fn.scrollspy.Constructor = ScrollSpy + + $.fn.scrollspy.defaults = { + offset: 10 + } + + + /* SCROLLSPY DATA-API + * ================== */ + + $(function () { + $('[data-spy="scroll"]').each(function () { + var $spy = $(this) + $spy.scrollspy($spy.data()) + }) + }) + +}(window.jQuery);/* ======================================================== + * bootstrap-tab.js v2.0.4 + * http://twitter.github.com/bootstrap/javascript.html#tabs + * ======================================================== + * Copyright 2012 Twitter, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================== */ + + +!function ($) { + + "use strict"; // jshint ;_; + + + /* TAB CLASS DEFINITION + * ==================== */ + + var Tab = function ( element ) { + this.element = $(element) + } + + Tab.prototype = { + + constructor: Tab + + , show: function () { + var $this = this.element + , $ul = $this.closest('ul:not(.dropdown-menu)') + , selector = $this.attr('data-target') + , previous + , $target + , e + + if (!selector) { + selector = $this.attr('href') + selector = selector && selector.replace(/.*(?=#[^\s]*$)/, '') //strip for ie7 + } + + if ( $this.parent('li').hasClass('active') ) return + + previous = $ul.find('.active a').last()[0] + + e = $.Event('show', { + relatedTarget: previous + }) + + $this.trigger(e) + + if (e.isDefaultPrevented()) return + + $target = $(selector) + + this.activate($this.parent('li'), $ul) + this.activate($target, $target.parent(), function () { + $this.trigger({ + type: 'shown' + , relatedTarget: previous + }) + }) + } + + , activate: function ( element, container, callback) { + var $active = container.find('> .active') + , transition = callback + && $.support.transition + && $active.hasClass('fade') + + function next() { + $active + .removeClass('active') + .find('> .dropdown-menu > .active') + .removeClass('active') + + element.addClass('active') + + if (transition) { + element[0].offsetWidth // reflow for transition + element.addClass('in') + } else { + element.removeClass('fade') + } + + if ( element.parent('.dropdown-menu') ) { + element.closest('li.dropdown').addClass('active') + } + + callback && callback() + } + + transition ? + $active.one($.support.transition.end, next) : + next() + + $active.removeClass('in') + } + } + + + /* TAB PLUGIN DEFINITION + * ===================== */ + + $.fn.tab = function ( option ) { + return this.each(function () { + var $this = $(this) + , data = $this.data('tab') + if (!data) $this.data('tab', (data = new Tab(this))) + if (typeof option == 'string') data[option]() + }) + } + + $.fn.tab.Constructor = Tab + + + /* TAB DATA-API + * ============ */ + + $(function () { + $('body').on('click.tab.data-api', '[data-toggle="tab"], [data-toggle="pill"]', function (e) { + e.preventDefault() + $(this).tab('show') + }) + }) + +}(window.jQuery);/* ============================================================= + * bootstrap-typeahead.js v2.0.4 + * http://twitter.github.com/bootstrap/javascript.html#typeahead + * ============================================================= + * Copyright 2012 Twitter, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================ */ + + +!function($){ + + "use strict"; // jshint ;_; + + + /* TYPEAHEAD PUBLIC CLASS DEFINITION + * ================================= */ + + var Typeahead = function (element, options) { + this.$element = $(element) + this.options = $.extend({}, $.fn.typeahead.defaults, options) + this.matcher = this.options.matcher || this.matcher + this.sorter = this.options.sorter || this.sorter + this.highlighter = this.options.highlighter || this.highlighter + this.updater = this.options.updater || this.updater + this.$menu = $(this.options.menu).appendTo('body') + this.source = this.options.source + this.shown = false + this.listen() + } + + Typeahead.prototype = { + + constructor: Typeahead + + , select: function () { + var val = this.$menu.find('.active').attr('data-value') + this.$element + .val(this.updater(val)) + .change() + return this.hide() + } + + , updater: function (item) { + return item + } + + , show: function () { + var pos = $.extend({}, this.$element.offset(), { + height: this.$element[0].offsetHeight + }) + + this.$menu.css({ + top: pos.top + pos.height + , left: pos.left + }) + + this.$menu.show() + this.shown = true + return this + } + + , hide: function () { + this.$menu.hide() + this.shown = false + return this + } + + , lookup: function (event) { + var that = this + , items + , q + + this.query = this.$element.val() + + if (!this.query) { + return this.shown ? this.hide() : this + } + + items = $.grep(this.source, function (item) { + return that.matcher(item) + }) + + items = this.sorter(items) + + if (!items.length) { + return this.shown ? this.hide() : this + } + + return this.render(items.slice(0, this.options.items)).show() + } + + , matcher: function (item) { + return ~item.toLowerCase().indexOf(this.query.toLowerCase()) + } + + , sorter: function (items) { + var beginswith = [] + , caseSensitive = [] + , caseInsensitive = [] + , item + + while (item = items.shift()) { + if 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(event) { + var active = this.$menu.find('.active').removeClass('active') + , prev = active.prev() + + if (!prev.length) { + prev = this.$menu.find('li').last() + } + + prev.addClass('active') + } + + , listen: function () { + this.$element + .on('blur', $.proxy(this.blur, this)) + .on('keypress', $.proxy(this.keypress, this)) + .on('keyup', $.proxy(this.keyup, this)) + + if ($.browser.webkit || $.browser.msie) { + this.$element.on('keydown', $.proxy(this.keypress, this)) + } + + this.$menu + .on('click', $.proxy(this.click, this)) + .on('mouseenter', 'li', $.proxy(this.mouseenter, this)) + } + + , keyup: function (e) { + switch(e.keyCode) { + case 40: // down arrow + case 38: // up arrow + break + + case 9: // tab + case 13: // enter + if (!this.shown) return + this.select() + break + + case 27: // escape + if (!this.shown) return + this.hide() + break + + default: + this.lookup() + } + + e.stopPropagation() + e.preventDefault() + } + + , keypress: function (e) { + if (!this.shown) return + + switch(e.keyCode) { + case 9: // tab + case 13: // enter + case 27: // escape + e.preventDefault() + break + + case 38: // up arrow + if (e.type != 'keydown') break + e.preventDefault() + this.prev() + break + + case 40: // down arrow + if (e.type != 'keydown') break + e.preventDefault() + this.next() + break + } + + e.stopPropagation() + } + + , blur: function (e) { + var that = this + setTimeout(function () { that.hide() }, 150) + } + + , click: function (e) { + e.stopPropagation() + e.preventDefault() + this.select() + } + + , mouseenter: function (e) { + this.$menu.find('.active').removeClass('active') + $(e.currentTarget).addClass('active') + } + + } + + + /* TYPEAHEAD PLUGIN DEFINITION + * =========================== */ + + $.fn.typeahead = function (option) { + return this.each(function () { + var $this = $(this) + , data = $this.data('typeahead') + , options = typeof option == 'object' && option + if (!data) $this.data('typeahead', (data = new Typeahead(this, options))) + if (typeof option == 'string') data[option]() + }) + } + + $.fn.typeahead.defaults = { + source: [] + , items: 8 + , menu: '' + , item: '
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      ',item:'
      • '},a.fn.typeahead.Constructor=b,a(function(){a("body").on("focus.typeahead.data-api",'[data-provide="typeahead"]',function(b){var c=a(this);if(c.data("typeahead"))return;b.preventDefault(),c.typeahead(c.data())})})}(window.jQuery); \ No newline at end of file diff --git a/docs/mccall81_245T.html b/docs/mccall81_245T.html new file mode 100644 index 00000000..426a35c4 --- /dev/null +++ b/docs/mccall81_245T.html @@ -0,0 +1,232 @@ + + + + +mccall81_245T. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
      +
      +
      + mkin 0.9.44.9000 +
      + +
      +
      +
      +
      + + +
      +
      + +
      + +

      + Datasets on aerobic soil metabolism of 2,4,5-T in six soils +

      + +
      +
      +

      Usage

      + 
      mccall81_245T
      + +
      +

      Description

      + +

      Time course of 2,4,5-trichlorophenoxyacetic acid, and the corresponding + 2,4,5-trichlorophenol and 2,4,5-trichloroanisole as recovered in diethylether + extracts.

      + +
      + +
      +

      Format

      + +

      A dataframe containing the following variables. +

      +
      name
      the name of the compound observed. Note that T245 is used as + an acronym for 2,4,5-T. T245 is a legitimate object name + in R, which is necessary for specifying models using + mkinmod.

      + +

      time
      a numeric vector containing sampling times in days after + treatment

      + +

      value
      a numeric vector containing concentrations in percent of applied radioactivity

      + +

      soil
      a factor containing the name of the soil

      + +

      + +

      + +
      + +
      +

      Source

      + +

      McCall P, Vrona SA, Kelley SS (1981) Fate of uniformly carbon-14 ring labeled 2,4,5-Trichlorophenoxyacetic acid and 2,4-dichlorophenoxyacetic acid. J Agric Chem 29, 100-107 + http://dx.doi.org/10.1021/jf00103a026

      + +
      + +

      Examples

      + 
        SFO_SFO_SFO <- mkinmod(T245 = list(type = "SFO", to = "phenol"),
+                         phenol = list(type = "SFO", to = "anisole"),
+                         anisole = list(type = "SFO"))
      
+Successfully compiled differential equation model from auto-generated C code.
+
        ## Not run: ------------------------------------
+#     fit.1 <- mkinfit(SFO_SFO_SFO, subset(mccall81_245T, soil == "Commerce"))
+#     summary(fit.1, data = FALSE)
+#   
+## ---------------------------------------------
+  # No covariance matrix and k_phenol_sink is really small, therefore fix it to zero
+  fit.2 <- mkinfit(SFO_SFO_SFO, subset(mccall81_245T, soil == "Commerce"),
+                 parms.ini = c(k_phenol_sink = 0),
+                 fixed_parms = "k_phenol_sink", quiet = TRUE)
+  summary(fit.2, data = FALSE)
      
+
      mkin version:    0.9.44.9000 
+R version:       3.3.1 
+Date of fit:     Sat Sep 10 05:53:00 2016 
+Date of summary: Sat Sep 10 05:53:00 2016 
+
+Equations:
+d_T245 = - k_T245_sink * T245 - k_T245_phenol * T245
+d_phenol = + k_T245_phenol * T245 - k_phenol_sink * phenol -
+           k_phenol_anisole * phenol
+d_anisole = + k_phenol_anisole * phenol - k_anisole_sink * anisole
+
+Model predictions using solution type deSolve 
+
+Fitted with method Port using 246 model solutions performed in 1.45 s
+
+Weighting: none
+
+Starting values for parameters to be optimised:
+                    value   type
+T245_0           100.9000  state
+k_T245_sink        0.1000 deparm
+k_T245_phenol      0.1001 deparm
+k_phenol_anisole   0.1002 deparm
+k_anisole_sink     0.1003 deparm
+
+Starting values for the transformed parameters actually optimised:
+                          value lower upper
+T245_0               100.900000  -Inf   Inf
+log_k_T245_sink       -2.302585  -Inf   Inf
+log_k_T245_phenol     -2.301586  -Inf   Inf
+log_k_phenol_anisole  -2.300587  -Inf   Inf
+log_k_anisole_sink    -2.299590  -Inf   Inf
+
+Fixed parameter values:
+              value   type
+phenol_0          0  state
+anisole_0         0  state
+k_phenol_sink     0 deparm
+
+Optimised, transformed parameters with symmetric confidence intervals:
+                     Estimate Std. Error  Lower    Upper
+T245_0               103.9000    2.35200 98.930 108.8000
+log_k_T245_sink       -4.1130    0.13250 -4.390  -3.8350
+log_k_T245_phenol     -3.6120    0.05002 -3.716  -3.5070
+log_k_phenol_anisole  -0.9037    0.30580 -1.544  -0.2637
+log_k_anisole_sink    -5.0090    0.11180 -5.243  -4.7750
+
+Parameter correlation:
+                       T245_0 log_k_T245_sink log_k_T245_phenol
+T245_0                1.00000         0.63761           -0.1742
+log_k_T245_sink       0.63761         1.00000           -0.3831
+log_k_T245_phenol    -0.17416        -0.38313            1.0000
+log_k_phenol_anisole -0.05948         0.08745           -0.3047
+log_k_anisole_sink   -0.16208        -0.60469            0.5227
+                     log_k_phenol_anisole log_k_anisole_sink
+T245_0                           -0.05948            -0.1621
+log_k_T245_sink                   0.08745            -0.6047
+log_k_T245_phenol                -0.30470             0.5227
+log_k_phenol_anisole              1.00000            -0.1774
+log_k_anisole_sink               -0.17744             1.0000
+
+Residual standard error: 2.706 on 19 degrees of freedom
+
+Backtransformed parameters:
+Confidence intervals for internally transformed parameters are asymmetric.
+t-test (unrealistically) based on the assumption of normal distribution
+for estimators of untransformed parameters.
+                  Estimate t value    Pr(>t)     Lower     Upper
+T245_0           1.039e+02  44.160 6.462e-21 98.930000 108.80000
+k_T245_sink      1.636e-02   7.545 1.978e-07  0.012400   0.02159
+k_T245_phenol    2.701e-02  19.990 1.607e-14  0.024320   0.02999
+k_phenol_anisole 4.051e-01   3.270 2.014e-03  0.213600   0.76820
+k_anisole_sink   6.679e-03   8.942 1.544e-08  0.005285   0.00844
+
+Chi2 error levels in percent:
+         err.min n.optim df
+All data   9.831       5 17
+T245       7.908       3  5
+phenol    99.808       1  6
+anisole    5.379       1  6
+
+Resulting formation fractions:
+                   ff
+T245_sink      0.3772
+T245_phenol    0.6228
+phenol_anisole 1.0000
+phenol_sink    0.0000
+anisole_sink   1.0000
+
+Estimated disappearance times:
+           DT50    DT90
+T245     15.982  53.091
+phenol    1.711   5.685
+anisole 103.784 344.763
+
      +
      +
      + + + + + +
      +
      + + +
      + + \ No newline at end of file diff --git a/docs/mkin_long_to_wide.html b/docs/mkin_long_to_wide.html new file mode 100644 index 00000000..d4bb7ee2 --- /dev/null +++ b/docs/mkin_long_to_wide.html @@ -0,0 +1,150 @@ + + + + +mkin_long_to_wide. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
      +
      +
      + mkin 0.9.44.9000 +
      + +
      +
      +
      +
      + + +
      +
      + +
      + +

      + Convert a dataframe from long to wide format +

      + +
      +
      +

      Usage

      + 
      mkin_long_to_wide(long_data, time = "time", outtime = "time")
      + +

      Arguments

      +
      +
      long_data
      +
      + The dataframe must contain one variable called "time" with the time values specified by the + time argument, one column called "name" with the grouping of the observed values, and + finally one column of observed values called "value". +
      +
      time
      +
      + The name of the time variable in the long input data. +
      +
      outtime
      +
      + The name of the time variable in the wide output data. +
      +
      + +
      +

      Description

      + +

      This function takes a dataframe in the long form as required by modCost + and converts it into a dataframe with one independent variable and several + dependent variables as columns.

      + +
      + +
      +

      Value

      + +

      + Dataframe in wide format. +

      + +
      + +

      Examples

      + 
      mkin_long_to_wide(FOCUS_2006_D)
      
+
         time parent    m1
+1     0  99.46  0.00
+2     0 102.04  0.00
+3     1  93.50  4.84
+4     1  92.50  5.64
+5     3  63.23 12.91
+6     3  68.99 12.96
+7     7  52.32 22.97
+8     7  55.13 24.47
+9    14  27.27 41.69
+10   14  26.64 33.21
+11   21  11.50 44.37
+12   21  11.64 46.44
+13   35   2.85 41.22
+14   35   2.91 37.95
+15   50   0.69 41.19
+16   50   0.63 40.01
+17   75   0.05 40.09
+18   75   0.06 33.85
+19  100     NA 31.04
+20  100     NA 33.13
+21  120     NA 25.15
+22  120     NA 33.31
+
      +
      +
      + + + + +

      Author

      + + Johannes Ranke + + +
      +
      + + +
      + + \ No newline at end of file diff --git a/docs/mkin_wide_to_long.html b/docs/mkin_wide_to_long.html new file mode 100644 index 00000000..73c54a28 --- /dev/null +++ b/docs/mkin_wide_to_long.html @@ -0,0 +1,129 @@ + + + + +mkin_wide_to_long. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
      +
      +
      + mkin 0.9.44.9000 +
      + +
      +
      +
      +
      + + +
      +
      + +
      + +

      + Convert a dataframe with observations over time into long format +

      + +
      +
      +

      Usage

      + 
      mkin_wide_to_long(wide_data, time = "t")
      + +

      Arguments

      +
      +
      wide_data
      +
      + The dataframe must contain one variable with the time values specified by the + time argument and usually more than one column of observed values. +
      +
      time
      +
      + The name of the time variable. +
      +
      + +
      +

      Description

      + +

      This function simply takes a dataframe with one independent variable and several + dependent variable and converts it into the long form as required by modCost.

      + +
      + +
      +

      Value

      + +

      + Dataframe in long format as needed for modCost. +

      + +
      + +

      Examples

      + 
      wide <- data.frame(t = c(1,2,3), x = c(1,4,7), y = c(3,4,5))
+mkin_wide_to_long(wide)
      
+
        name time value
+1    x    1     1
+2    x    2     4
+3    x    3     7
+4    y    1     3
+5    y    2     4
+6    y    3     5
+
      +
      +
      + + + + +

      Author

      + + Johannes Ranke + + +
      +
      + + +
      + + \ No newline at end of file diff --git a/docs/mkinds.html b/docs/mkinds.html new file mode 100644 index 00000000..48b2e904 --- /dev/null +++ b/docs/mkinds.html @@ -0,0 +1,121 @@ + + + + +mkinds. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
      +
      +
      + mkin 0.9.44.9000 +
      + +
      +
      +
      +
      + + +
      +
      + +
      + +

      A dataset class for mkin

      + +
      +
      +

      Usage

      + 
      mkinds
      + +
      +

      Format

      + +

      An R6Class generator object.

      + +
      + +
      +

      Description

      + +

      A dataset class for mkin

      + +
      + +
      +

      Fields

      + +

      + +

      +
      title
      A full title for the dataset

      + +

      sampling
      times The sampling times

      + +

      time_unit
      The time unit

      + +

      observed
      Names of the observed compounds

      + +

      unit
      The unit of the observations

      + +

      replicates
      The number of replicates

      + +

      data
      A dataframe with at least the columns name, time and value +in order to be compatible with mkinfit

      + +

      + +
      + +

      Examples

      + 
      mds <- mkinds$new("FOCUS A", FOCUS_2006_A)
      +
      +
      + + + + + +
      +
      + + +
      + + \ No newline at end of file diff --git a/docs/mkinerrmin.html b/docs/mkinerrmin.html new file mode 100644 index 00000000..58cf6d47 --- /dev/null +++ b/docs/mkinerrmin.html @@ -0,0 +1,161 @@ + + + + +mkinerrmin. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
      +
      +
      + mkin 0.9.44.9000 +
      + +
      +
      +
      +
      + + +
      +
      + +
      + +

      +Calculate the minimum error to assume in order to pass the variance test +

      + +
      +
      +

      Usage

      + 
      mkinerrmin(fit, alpha = 0.05)
      + +

      Arguments

      +
      +
      fit
      +
      + an object of class mkinfit. +
      +
      alpha
      +
      + The confidence level chosen for the chi-squared test. +
      +
      + +
      +

      Description

      + +

      This function uses optimize in order to iteratively find the +smallest relative error still resulting in passing the chi-squared test +as defined in the FOCUS kinetics report from 2006.

      + +
      + +
      +

      Value

      + +

      + A dataframe with the following components: +
      err.min
      The relative error, expressed as a fraction.

      + +

      n.optim
      The number of optimised parameters attributed to the data series.

      + +

      df
      The number of remaining degrees of freedom for the chi2 error level + calculations. Note that mean values are used for the chi2 statistic and + therefore every time point with observed values in the series only counts + one time.

      + +

      The dataframe has one row for the total dataset and one further row for + each observed state variable in the model. +

      + +
      + +
      +

      Details

      + +

      This function is used internally by summary.mkinfit.

      + +
      + +
      +

      References

      + +

      FOCUS (2006) “Guidance Document on Estimating Persistence and + Degradation Kinetics from Environmental Fate Studies on Pesticides in EU + Registration” Report of the FOCUS Work Group on Degradation Kinetics, + EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, + http://focus.jrc.ec.europa.eu/dk

      + +
      + +

      Examples

      + 
      SFO_SFO = mkinmod(parent = list(type = "SFO", to = "m1"),
+                  m1 = list(type = "SFO"),
+                  use_of_ff = "max")
      
+Successfully compiled differential equation model from auto-generated C code.
+
      
+fit_FOCUS_D = mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE)
+round(mkinerrmin(fit_FOCUS_D), 4)
      
+
               err.min n.optim df
+All data  0.0640       4 15
+parent    0.0646       2  7
+m1        0.0469       2  8
+
      
+
      fit_FOCUS_E = mkinfit(SFO_SFO, FOCUS_2006_E, quiet = TRUE)
+round(mkinerrmin(fit_FOCUS_E), 4)
      
+
               err.min n.optim df
+All data  0.1544       4 13
+parent    0.1659       2  7
+m1        0.1095       2  6
+
      +
      +
      + + + + + +
      +
      + + +
      + + \ No newline at end of file diff --git a/docs/mkinfit.html b/docs/mkinfit.html new file mode 100644 index 00000000..9264c59b --- /dev/null +++ b/docs/mkinfit.html @@ -0,0 +1,538 @@ + + + + +mkinfit. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
      +
      +
      + mkin 0.9.44.9000 +
      + +
      +
      +
      +
      + + +
      +
      + +
      + +

      + Fit a kinetic model to data with one or more state variables +

      + +
      +
      +

      Usage

      + 
      mkinfit(mkinmod, observed,
+  parms.ini = "auto",
+  state.ini = "auto",
+  fixed_parms = NULL, fixed_initials = names(mkinmod$diffs)[-1],
+  from_max_mean = FALSE,
+  solution_type = c("auto", "analytical", "eigen", "deSolve"),
+  method.ode = "lsoda",
+  use_compiled = "auto",
+  method.modFit = c("Port", "Marq", "SANN", "Nelder-Mead", "BFGS", "CG", "L-BFGS-B"),
+  maxit.modFit = "auto",
+  control.modFit = list(),
+  transform_rates = TRUE,
+  transform_fractions = TRUE,
+  plot = FALSE, quiet = FALSE, err = NULL, weight = "none",
+  scaleVar = FALSE,
+  atol = 1e-8, rtol = 1e-10, n.outtimes = 100,
+  reweight.method = NULL,
+  reweight.tol = 1e-8, reweight.max.iter = 10,
+  trace_parms = FALSE, ...)
      + +

      Arguments

      +
      +
      mkinmod
      +
      + A list of class mkinmod, containing the kinetic model to be + fitted to the data, or one of the shorthand names ("SFO", "FOMC", "DFOP", + "HS", "SFORB"). If a shorthand name is given, a parent only degradation + model is generated for the variable with the highest value in + observed. +
      +
      observed
      +
      + The observed data. It has to be in the long format as described in + modFit, i.e. the first column called "name" must contain the + name of the observed variable for each data point. The second column must + contain the times of observation, named "time". The third column must be + named "value" and contain the observed values. Optionally, a further column + can contain weights for each data point. Its name must be passed as a + further argument named err which is then passed on to + modFit. +
      +
      parms.ini
      +
      + A named vector of initial values for the parameters, including parameters + to be optimised and potentially also fixed parameters as indicated by + fixed_parms. If set to "auto", initial values for rate constants + are set to default values. Using parameter names that are not in the model + gives an error. + + It is possible to only specify a subset of the parameters that the model + needs. You can use the parameter lists "bparms.ode" from a previously + fitted model, which contains the differential equation parameters from this + model. This works nicely if the models are nested. An example is given + below. +
      +
      state.ini
      +
      + A named vector of initial values for the state variables of the model. In + case the observed variables are represented by more than one model + variable, the names will differ from the names of the observed variables + (see map component of mkinmod). The default is to set + the initial value of the first model variable to the mean of the time zero + values for the variable with the maximum observed value, and all others to 0. + If this variable has no time zero observations, its initial value is set to 100. +
      +
      fixed_parms
      +
      + The names of parameters that should not be optimised but rather kept at the + values specified in parms.ini. +
      +
      fixed_initials
      +
      + The names of model variables for which the initial state at time 0 should + be excluded from the optimisation. Defaults to all state variables except + for the first one. +
      +
      from_max_mean
      +
      + If this is set to TRUE, and the model has only one observed variable, then + data before the time of the maximum observed value (after averaging for each + sampling time) are discarded, and this time is subtracted from all + remaining time values, so the time of the maximum observed mean value is + the new time zero. +
      +
      solution_type
      +
      + If set to "eigen", the solution of the system of differential equations is + based on the spectral decomposition of the coefficient matrix in cases that + this is possible. If set to "deSolve", a numerical ode solver from package + deSolve is used. If set to "analytical", an analytical + solution of the model is used. This is only implemented for simple + degradation experiments with only one state variable, i.e. with no + metabolites. The default is "auto", which uses "analytical" if possible, + otherwise "eigen" if the model can be expressed using eigenvalues and + eigenvectors, and finally "deSolve" for the remaining models (time + dependence of degradation rates and metabolites). This argument is passed + on to the helper function mkinpredict. +
      +
      method.ode
      +
      + The solution method passed via mkinpredict to + ode in case the solution type is "deSolve". The default + "lsoda" is performant, but sometimes fails to converge. +
      +
      use_compiled
      +
      + If set to FALSE, no compiled version of the mkinmod + model is used, in the calls to mkinpredict even if + a compiled verion is present. +
      +
      method.modFit
      +
      + The optimisation method passed to modFit. + + In order to optimally deal with problems where local minima occur, the + "Port" algorithm is now used per default as it is less prone to get trapped + in local minima and depends less on starting values for parameters than + the Levenberg Marquardt variant selected by "Marq". However, "Port" needs + more iterations. + + The former default "Marq" is the Levenberg Marquardt algorithm + nls.lm from the package minpack.lm and usually needs + the least number of iterations. + + The "Pseudo" algorithm is not included because it needs finite parameter bounds + which are currently not supported. + + The "Newton" algorithm is not included because its number of iterations + can not be controlled by control.modFit and it does not appear + to provide advantages over the other algorithms. +
      +
      maxit.modFit
      +
      + Maximum number of iterations in the optimisation. If not "auto", this will + be passed to the method called by modFit, overriding + what may be specified in the next argument control.modFit. +
      +
      control.modFit
      +
      + Additional arguments passed to the optimisation method used by + modFit. +
      +
      transform_rates
      +
      + Boolean specifying if kinetic rate constants should be transformed in the + model specification used in the fitting for better compliance with the + assumption of normal distribution of the estimator. If TRUE, also + alpha and beta parameters of the FOMC model are log-transformed, as well + as k1 and k2 rate constants for the DFOP and HS models and the break point + tb of the HS model. + If FALSE, zero is used as a lower bound for the rates in the optimisation. +
      +
      transform_fractions
      +
      + Boolean specifying if formation fractions constants should be transformed in the + model specification used in the fitting for better compliance with the + assumption of normal distribution of the estimator. The default (TRUE) is + to do transformations. If TRUE, the g parameter of the DFOP and HS + models are also transformed, as they can also be seen as compositional + data. The transformation used for these transformations is the + ilr transformation. +
      +
      plot
      +
      + Should the observed values and the numerical solutions be plotted at each + stage of the optimisation? +
      +
      quiet
      +
      + Suppress printing out the current model cost after each improvement? +
      +
      err
      +
      either NULL, or the name of the column with the + error estimates, used to weigh the residuals (see details of + modCost); if NULL, then the residuals are not weighed. +
      +
      weight
      +
      + only if err=NULL: how to weight the residuals, one of "none", + "std", "mean", see details of modCost. +
      +
      scaleVar
      +
      + Will be passed to modCost. Default is not to scale Variables + according to the number of observations. +
      +
      atol
      +
      + Absolute error tolerance, passed to ode. Default is 1e-8, + lower than in lsoda. +
      +
      rtol
      +
      + Absolute error tolerance, passed to ode. Default is 1e-10, + much lower than in lsoda. +
      +
      n.outtimes
      +
      + The length of the dataseries that is produced by the model prediction + function mkinpredict. This impacts the accuracy of + the numerical solver if that is used (see solution_type argument. + The default value is 100. +
      +
      reweight.method
      +
      + The method used for iteratively reweighting residuals, also known + as iteratively reweighted least squares (IRLS). Default is NULL, + the other method implemented is called "obs", meaning that each + observed variable is assumed to have its own variance, this is + estimated from the fit and used for weighting the residuals + in each iteration until convergence of this estimate up to + reweight.tol or up to the maximum number of iterations + specified by reweight.max.iter. +
      +
      reweight.tol
      +
      + Tolerance for convergence criterion for the variance components + in IRLS fits. +
      +
      reweight.max.iter
      +
      + Maximum iterations in IRLS fits. +
      +
      trace_parms
      +
      + Should a trace of the parameter values be listed? +
      +
      ...
      +
      + Further arguments that will be passed to modFit. +
      +
      + +
      +

      Description

      + +

      This function uses the Flexible Modelling Environment package + FME to create a function calculating the model cost, i.e. the + deviation between the kinetic model and the observed data. This model cost is + then minimised using the Port algorithm nlminb, + using the specified initial or fixed parameters and starting values. + Per default, parameters in the kinetic models are internally transformed in order + to better satisfy the assumption of a normal distribution of their estimators. + In each step of the optimsation, the kinetic model is solved using the + function mkinpredict. The variance of the residuals for each + observed variable can optionally be iteratively reweighted until convergence + using the argument reweight.method = "obs".

      + +
      + +
      +

      Value

      + +

      + A list with "mkinfit" and "modFit" in the class attribute. + A summary can be obtained by summary.mkinfit. +

      + +
      + +
      +

      Note

      + +

      The implementation of iteratively reweighted least squares is inspired by the + work of the KinGUII team at Bayer Crop Science (Walter Schmitt and Zhenglei + Gao). A similar implemention can also be found in CAKE 2.0, which is the + other GUI derivative of mkin, sponsored by Syngenta.

      + +
      + +
      +

      Note

      + +

      When using the "IORE" submodel for metabolites, fitting with + "transform_rates = TRUE" (the default) often leads to failures of the + numerical ODE solver. In this situation it may help to switch off the + internal rate transformation.

      + +
      + +

      Examples

      + 
      # Use shorthand notation for parent only degradation
+fit <- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE)
+summary(fit)
      
+
      mkin version:    0.9.44.9000 
+R version:       3.3.1 
+Date of fit:     Sat Sep 10 05:53:02 2016 
+Date of summary: Sat Sep 10 05:53:02 2016 
+
+Equations:
+d_parent = - (alpha/beta) * 1/((time/beta) + 1) * parent
+
+Model predictions using solution type analytical 
+
+Fitted with method Port using 64 model solutions performed in 0.16 s
+
+Weighting: none
+
+Starting values for parameters to be optimised:
+         value   type
+parent_0  85.1  state
+alpha      1.0 deparm
+beta      10.0 deparm
+
+Starting values for the transformed parameters actually optimised:
+              value lower upper
+parent_0  85.100000  -Inf   Inf
+log_alpha  0.000000  -Inf   Inf
+log_beta   2.302585  -Inf   Inf
+
+Fixed parameter values:
+None
+
+Optimised, transformed parameters with symmetric confidence intervals:
+          Estimate Std. Error    Lower   Upper
+parent_0  85.87000     2.2460 80.38000 91.3700
+log_alpha  0.05192     0.1605 -0.34080  0.4446
+log_beta   0.65100     0.2801 -0.03452  1.3360
+
+Parameter correlation:
+          parent_0 log_alpha log_beta
+parent_0    1.0000   -0.2033  -0.3624
+log_alpha  -0.2033    1.0000   0.9547
+log_beta   -0.3624    0.9547   1.0000
+
+Residual standard error: 2.275 on 6 degrees of freedom
+
+Backtransformed parameters:
+Confidence intervals for internally transformed parameters are asymmetric.
+t-test (unrealistically) based on the assumption of normal distribution
+for estimators of untransformed parameters.
+         Estimate t value    Pr(>t)   Lower  Upper
+parent_0   85.870  38.230 1.069e-08 80.3800 91.370
+alpha       1.053   6.231 3.953e-04  0.7112  1.560
+beta        1.917   3.570 5.895e-03  0.9661  3.806
+
+Chi2 error levels in percent:
+         err.min n.optim df
+All data   6.657       3  6
+parent     6.657       3  6
+
+Estimated disappearance times:
+        DT50  DT90 DT50back
+parent 1.785 15.15     4.56
+
+Data:
+ time variable observed predicted residual
+    0   parent     85.1    85.875  -0.7749
+    1   parent     57.9    55.191   2.7091
+    3   parent     29.9    31.845  -1.9452
+    7   parent     14.6    17.012  -2.4124
+   14   parent      9.7     9.241   0.4590
+   28   parent      6.6     4.754   1.8460
+   63   parent      4.0     2.102   1.8977
+   91   parent      3.9     1.441   2.4590
+  119   parent      0.6     1.092  -0.4919
+
      
+
      
+# One parent compound, one metabolite, both single first order.
+# Use mkinsub for convenience in model formulation. Pathway to sink included per default.
+SFO_SFO <- mkinmod(
+  parent = mkinsub("SFO", "m1"),
+  m1 = mkinsub("SFO"))
      
+Successfully compiled differential equation model from auto-generated C code.
+
      # Fit the model to the FOCUS example dataset D using defaults
+print(system.time(fit <- mkinfit(SFO_SFO, FOCUS_2006_D,
+                           solution_type = "eigen", quiet = TRUE)))
      
+
         user  system elapsed 
+  1.272   1.360   1.044 
+
      
+
      coef(fit)
      
+
               parent_0 log_k_parent_sink   log_k_parent_m1     log_k_m1_sink 
+         99.59848          -3.03822          -2.98030          -5.24750 
+
      
+
      endpoints(fit)
      
+
      $ff
+parent_sink   parent_m1     m1_sink 
+   0.485524    0.514476    1.000000 
+
+$SFORB
+logical(0)
+
+$distimes
+             DT50      DT90
+parent   7.022929  23.32967
+m1     131.760712 437.69961
+
+
      
+
      ## Not run: ------------------------------------
+# # deSolve is slower when no C compiler (gcc) was available during model generation
+# print(system.time(fit.deSolve <- mkinfit(SFO_SFO, FOCUS_2006_D, 
+#                            solution_type = "deSolve")))
+# coef(fit.deSolve)
+# endpoints(fit.deSolve)
+## ---------------------------------------------
+
+# Use stepwise fitting, using optimised parameters from parent only fit, FOMC
+## Not run: ------------------------------------
+# FOMC_SFO <- mkinmod(
+#   parent = mkinsub("FOMC", "m1"),
+#   m1 = mkinsub("SFO"))
+# # Fit the model to the FOCUS example dataset D using defaults
+# fit.FOMC_SFO <- mkinfit(FOMC_SFO, FOCUS_2006_D)
+# # Use starting parameters from parent only FOMC fit
+# fit.FOMC = mkinfit("FOMC", FOCUS_2006_D, plot=TRUE)
+# fit.FOMC_SFO <- mkinfit(FOMC_SFO, FOCUS_2006_D, 
+#   parms.ini = fit.FOMC$bparms.ode, plot=TRUE)
+# 
+# # Use stepwise fitting, using optimised parameters from parent only fit, SFORB
+# SFORB_SFO <- mkinmod(
+#   parent = list(type = "SFORB", to = "m1", sink = TRUE),
+#   m1 = list(type = "SFO"))
+# # Fit the model to the FOCUS example dataset D using defaults
+# fit.SFORB_SFO <- mkinfit(SFORB_SFO, FOCUS_2006_D)
+# fit.SFORB_SFO.deSolve <- mkinfit(SFORB_SFO, FOCUS_2006_D, solution_type = "deSolve")
+# # Use starting parameters from parent only SFORB fit (not really needed in this case)
+# fit.SFORB = mkinfit("SFORB", FOCUS_2006_D)
+# fit.SFORB_SFO <- mkinfit(SFORB_SFO, FOCUS_2006_D, parms.ini = fit.SFORB$bparms.ode)
+## ---------------------------------------------
+
+## Not run: ------------------------------------
+# # Weighted fits, including IRLS
+# SFO_SFO.ff <- mkinmod(parent = mkinsub("SFO", "m1"),
+#                       m1 = mkinsub("SFO"), use_of_ff = "max")
+# f.noweight <- mkinfit(SFO_SFO.ff, FOCUS_2006_D)
+# summary(f.noweight)
+# f.irls <- mkinfit(SFO_SFO.ff, FOCUS_2006_D, reweight.method = "obs")
+# summary(f.irls)
+# f.w.mean <- mkinfit(SFO_SFO.ff, FOCUS_2006_D, weight = "mean")
+# summary(f.w.mean)
+# f.w.value <- mkinfit(SFO_SFO.ff, subset(FOCUS_2006_D, value != 0), err = "value")
+# summary(f.w.value)
+## ---------------------------------------------
+
+## Not run: ------------------------------------
+# # Manual weighting
+# dw <- FOCUS_2006_D
+# errors <- c(parent = 2, m1 = 1)
+# dw$err.man <- errors[FOCUS_2006_D$name]
+# f.w.man <- mkinfit(SFO_SFO.ff, dw, err = "err.man")
+# summary(f.w.man)
+# f.w.man.irls <- mkinfit(SFO_SFO.ff, dw, err = "err.man",
+#                        reweight.method = "obs")
+# summary(f.w.man.irls)
+## ---------------------------------------------
      +
      +
      + + +

      See also

      +

      Plotting methods plot.mkinfit and + mkinparplot.

      + +

      Fitting of several models to several datasets in a single call to + mmkin.

      + + + +

      Author

      + + Johannes Ranke + + +
      +
      + + +
      + + \ No newline at end of file diff --git a/docs/mkinmod.html b/docs/mkinmod.html new file mode 100644 index 00000000..afbb9cc2 --- /dev/null +++ b/docs/mkinmod.html @@ -0,0 +1,221 @@ + + + + +mkinmod. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
      +
      +
      + mkin 0.9.44.9000 +
      + +
      +
      +
      +
      + + +
      +
      + +
      + +

      + Function to set up a kinetic model with one or more state variables +

      + +
      +
      +

      Usage

      + 
      mkinmod(..., use_of_ff = "min", speclist = NULL, quiet = FALSE, verbose = FALSE)
      + +

      Arguments

      +
      +
      ...
      +
      + For each observed variable, a list has to be specified as an argument, containing + at least a component type, specifying the type of kinetics to use + for the variable. Currently, single first order kinetics "SFO", + indeterminate order rate equation kinetics "IORE", or + single first order with reversible binding "SFORB" are implemented for all + variables, while + "FOMC", "DFOP" and "HS" can additionally be chosen for the first + variable which is assumed to be the source compartment. + Additionally, each component of the list can include a character vector to, + specifying names of variables to which a transfer is to be assumed in the + model. + If the argument use_of_ff is set to "min" (default) and the model for + the compartment is "SFO" or "SFORB", an additional component of the list + can be "sink=FALSE" effectively fixing the flux to sink to zero. +
      +
      use_of_ff
      +
      + Specification of the use of formation fractions in the model equations and, if + applicable, the coefficient matrix. If "min", a minimum use of formation + fractions is made in order to avoid fitting the product of formation fractions + and rate constants. If "max", formation fractions are always used. +
      +
      speclist
      +
      + The specification of the observed variables and their submodel types and + pathways can be given as a single list using this argument. Default is NULL. +
      +
      quiet
      +
      + Should messages be suppressed? +
      +
      verbose
      +
      + If TRUE, passed to cfunction if applicable to give + detailed information about the C function being built. +
      +
      + +
      +

      Description

      + +

      The function usually takes several expressions, each assigning a compound name to + a list, specifying the kinetic model type and reaction or transfer to other + observed compartments. Instead of specifying several expressions, a list + of lists can be given in the speclist argument.

      + +

      For the definition of model types and their parameters, the equations given + in the FOCUS and NAFTA guidance documents are used.

      + +
      + +
      +

      Value

      + +

      + A list of class mkinmod for use with mkinfit, containing +
      diffs
      A vector of string representations of differential equations, + one for each modelling variable.

      + +

      parms
      A vector of parameter names occurring in the differential equations.

      + +

      map
      A list containing named character vectors for each observed variable, specifying + the modelling variables by which it is represented.

      + +

      use_of_ff
      The content of use_of_ff is passed on in this list component.

      + +

      coefmat
      The coefficient matrix, if the system of differential equations can be + represented by one.

      + +

      + +
      + +
      +

      Note

      + +

      The IORE submodel is not well tested (yet). When using this model for metabolites, + you may want to read the second note in the help page to + mkinfit.

      + +
      + +
      +

      References

      + +

      FOCUS (2006) “Guidance Document on Estimating Persistence and + Degradation Kinetics from Environmental Fate Studies on Pesticides in EU + Registration” Report of the FOCUS Work Group on Degradation Kinetics, + EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, + http://focus.jrc.ec.europa.eu/dk

      + +

      NAFTA Technical Working Group on Pesticides (not dated) Guidance for + Evaluating and Calculating Degradation Kinetics in Environmental + Media

      + +
      + +

      Examples

      + 
      # Specify the SFO model (this is not needed any more, as we can now mkinfit("SFO", ...)
+SFO <- mkinmod(parent = list(type = "SFO"))
+
+# One parent compound, one metabolite, both single first order
+SFO_SFO <- mkinmod(
+  parent = mkinsub("SFO", "m1"),
+  m1 = mkinsub("SFO"))
      
+Successfully compiled differential equation model from auto-generated C code.
+
      
+## Not run: ------------------------------------
+# # The above model used to be specified like this, before the advent of mkinsub()
+# SFO_SFO <- mkinmod(
+#   parent = list(type = "SFO", to = "m1"),
+#   m1 = list(type = "SFO"))
+# 
+# # Show details of creating the C function
+# SFO_SFO <- mkinmod(
+#   parent = mkinsub("SFO", "m1"),
+#   m1 = mkinsub("SFO"), verbose = TRUE)
+# 
+# # If we have several parallel metabolites 
+# # (compare tests/testthat/test_synthetic_data_for_UBA_2014.R)
+# m_synth_DFOP_par <- mkinmod(parent = mkinsub("DFOP", c("M1", "M2")),
+#                            M1 = mkinsub("SFO"),
+#                            M2 = mkinsub("SFO"),
+#                            use_of_ff = "max", quiet = TRUE)
+# 
+# fit_DFOP_par_c <- mkinfit(m_synth_DFOP_par, 
+#                           synthetic_data_for_UBA_2014[[12]]$data,
+#                           quiet = TRUE)
+## ---------------------------------------------
      +
      +
      + + + + +

      Author

      + + Johannes Ranke + + +
      +
      + + +
      + + \ No newline at end of file diff --git a/docs/mkinparplot-4.png b/docs/mkinparplot-4.png new file mode 100644 index 00000000..77eafd95 Binary files /dev/null and b/docs/mkinparplot-4.png differ diff --git a/docs/mkinparplot.html b/docs/mkinparplot.html new file mode 100644 index 00000000..3b78785e --- /dev/null +++ b/docs/mkinparplot.html @@ -0,0 +1,123 @@ + + + + +mkinparplot. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
      +
      +
      + mkin 0.9.44.9000 +
      + +
      +
      +
      +
      + + +
      +
      + +
      + +

      + Function to plot the confidence intervals obtained using + mkinfit +

      + +
      +
      +

      Usage

      + 
      mkinparplot(object)
      + +

      Arguments

      +
      +
      object
      +
      + A fit represented in an mkinfit object. +
      +
      + +
      +

      Description

      + +

      This function plots the confidence intervals for the parameters + fitted using mkinfit.

      + +
      + +
      +

      Value

      + +

      + Nothing is returned by this function, as it is called for its side effect, namely to produce a plot. +

      + +
      + +

      Examples

      + 
      model <- mkinmod(
+  T245 = list(type = "SFO", to = c("phenol"), sink = FALSE),
+  phenol = list(type = "SFO", to = c("anisole")),
+  anisole = list(type = "SFO"), use_of_ff = "max")
      
+Successfully compiled differential equation model from auto-generated C code.
+
      fit <- mkinfit(model, subset(mccall81_245T, soil == "Commerce"), quiet = TRUE)
+mkinparplot(fit)
      
+
      +
      +
      + + + + +

      Author

      + + Johannes Ranke + + +
      +
      + + +
      + + \ No newline at end of file diff --git a/docs/mkinplot.html b/docs/mkinplot.html new file mode 100644 index 00000000..a8f6632e --- /dev/null +++ b/docs/mkinplot.html @@ -0,0 +1,115 @@ + + + + +mkinplot. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
      +
      +
      + mkin 0.9.44.9000 +
      + +
      +
      +
      +
      + + +
      +
      + +
      + +

      + Plot the observed data and the fitted model of an mkinfit object +

      + +
      +
      +

      Usage

      + 
      mkinplot(fit, ...)
      + +

      Arguments

      +
      +
      fit
      +
      + an object of class mkinfit. +
      +
      ...
      +
      + further arguments passed to plot.mkinfit. +
      +
      + +
      +

      Description

      + +

      Deprecated function. It now only calls the plot method plot.mkinfit.

      + +
      + +
      +

      Value

      + +

      + The function is called for its side effect. +

      + +
      +
      +
      + + + + +

      Author

      + + Johannes Ranke + + +
      +
      + + +
      + + \ No newline at end of file diff --git a/docs/mkinpredict.html b/docs/mkinpredict.html new file mode 100644 index 00000000..438b37c0 --- /dev/null +++ b/docs/mkinpredict.html @@ -0,0 +1,345 @@ + + + + +mkinpredict. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
      +
      +
      + mkin 0.9.44.9000 +
      + +
      +
      +
      +
      + + +
      +
      + +
      + +

      + Produce predictions from a kinetic model using specific parameters +

      + +
      +
      +

      Usage

      + 
      mkinpredict(mkinmod, odeparms, odeini, outtimes, solution_type = "deSolve",
+              use_compiled = "auto", method.ode = "lsoda", atol = 1e-08, rtol = 1e-10,
+        map_output = TRUE, ...)
      + +

      Arguments

      +
      +
      mkinmod
      +
      + A kinetic model as produced by mkinmod. +
      +
      odeparms
      +
      + A numeric vector specifying the parameters used in the kinetic model, which + is generally defined as a set of ordinary differential equations. +
      +
      odeini
      +
      + A numeric vectory containing the initial values of the state variables of + the model. Note that the state variables can differ from the observed + variables, for example in the case of the SFORB model. +
      +
      outtimes
      +
      + A numeric vector specifying the time points for which model predictions + should be generated. +
      +
      solution_type
      +
      + The method that should be used for producing the predictions. This should + generally be "analytical" if there is only one observed variable, and + usually "deSolve" in the case of several observed variables. The third + possibility "eigen" is faster but not applicable to some models e.g. + using FOMC for the parent compound. +
      +
      method.ode
      +
      + The solution method passed via mkinpredict to + ode in case the solution type is "deSolve". The default + "lsoda" is performant, but sometimes fails to converge. +
      +
      use_compiled
      +
      + If set to FALSE, no compiled version of the mkinmod + model is used, even if is present. +
      +
      atol
      +
      + Absolute error tolerance, passed to ode. Default is 1e-8, + lower than in lsoda. +
      +
      rtol
      +
      + Absolute error tolerance, passed to ode. Default is 1e-10, + much lower than in lsoda. +
      +
      map_output
      +
      + Boolean to specify if the output should list values for the observed + variables (default) or for all state variables (if set to FALSE). +
      +
      ...
      +
      + Further arguments passed to the ode solver in case such a solver is used. +
      +
      + +
      +

      Description

      + +

      This function produces a time series for all the observed variables in a + kinetic model as specified by mkinmod, using a specific set of + kinetic parameters and initial values for the state variables.

      + +
      + +
      +

      Value

      + +

      + A matrix in the same format as the output of ode. +

      + +
      + +

      Examples

      + 
        SFO <- mkinmod(degradinol = list(type = "SFO"))
+  # Compare solution types
+  mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
+              solution_type = "analytical")
      
+
         time  degradinol
+1     0 100.0000000
+2     1  74.0818221
+3     2  54.8811636
+4     3  40.6569660
+5     4  30.1194212
+6     5  22.3130160
+7     6  16.5298888
+8     7  12.2456428
+9     8   9.0717953
+10    9   6.7205513
+11   10   4.9787068
+12   11   3.6883167
+13   12   2.7323722
+14   13   2.0241911
+15   14   1.4995577
+16   15   1.1108997
+17   16   0.8229747
+18   17   0.6096747
+19   18   0.4516581
+20   19   0.3345965
+21   20   0.2478752
+
      
+
        mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
+              solution_type = "deSolve")
      
+
         time  degradinol
+1     0 100.0000000
+2     1  74.0818221
+3     2  54.8811636
+4     3  40.6569660
+5     4  30.1194212
+6     5  22.3130160
+7     6  16.5298888
+8     7  12.2456428
+9     8   9.0717953
+10    9   6.7205513
+11   10   4.9787068
+12   11   3.6883167
+13   12   2.7323722
+14   13   2.0241911
+15   14   1.4995577
+16   15   1.1108996
+17   16   0.8229747
+18   17   0.6096747
+19   18   0.4516581
+20   19   0.3345965
+21   20   0.2478752
+
      
+
        mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
+              solution_type = "deSolve", use_compiled = FALSE)
      
+
         time  degradinol
+1     0 100.0000000
+2     1  74.0818221
+3     2  54.8811636
+4     3  40.6569660
+5     4  30.1194212
+6     5  22.3130160
+7     6  16.5298888
+8     7  12.2456428
+9     8   9.0717953
+10    9   6.7205513
+11   10   4.9787068
+12   11   3.6883167
+13   12   2.7323722
+14   13   2.0241911
+15   14   1.4995577
+16   15   1.1108996
+17   16   0.8229747
+18   17   0.6096747
+19   18   0.4516581
+20   19   0.3345965
+21   20   0.2478752
+
      
+
        mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
+              solution_type = "eigen")
      
+
         time  degradinol
+1     0 100.0000000
+2     1  74.0818221
+3     2  54.8811636
+4     3  40.6569660
+5     4  30.1194212
+6     5  22.3130160
+7     6  16.5298888
+8     7  12.2456428
+9     8   9.0717953
+10    9   6.7205513
+11   10   4.9787068
+12   11   3.6883167
+13   12   2.7323722
+14   13   2.0241911
+15   14   1.4995577
+16   15   1.1108997
+17   16   0.8229747
+18   17   0.6096747
+19   18   0.4516581
+20   19   0.3345965
+21   20   0.2478752
+
      
+
      
+
+  # Compare integration methods to analytical solution
+  mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
+              solution_type = "analytical")[21,]
      
+
         time degradinol
+21   20  0.2478752
+
      
+
        mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
+              method = "lsoda")[21,]
      
+
         time degradinol
+21   20  0.2478752
+
      
+
        mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
+              method = "ode45")[21,]
      
+
         time degradinol
+21   20  0.2478752
+
      
+
        mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
+              method = "rk4")[21,]
      
+
         time degradinol
+21   20  0.2480043
+
      
+
       # rk4 is not as precise here
+
+  # The number of output times used to make a lot of difference until the
+  # default for atol was adjusted
+  mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100),
+              seq(0, 20, by = 0.1))[201,]
      
+
          time degradinol
+201   20  0.2478752
+
      
+
        mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100),
+              seq(0, 20, by = 0.01))[2001,]
      
+
           time degradinol
+2001   20  0.2478752
+
      
+
      
+  # Check compiled model versions - they are faster than the eigenvalue based solutions!
+  SFO_SFO = mkinmod(parent = list(type = "SFO", to = "m1"),
+                    m1 = list(type = "SFO"))
      
+Successfully compiled differential equation model from auto-generated C code.
+
        system.time(
+    print(mkinpredict(SFO_SFO, c(k_parent_m1 = 0.05, k_parent_sink = 0.1, k_m1_sink = 0.01),
+                c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
+                solution_type = "eigen")[201,]))
      
+
          time   parent       m1
+201   20 4.978707 27.46227
+
      
+
         user  system elapsed 
+  0.008   0.020   0.005 
+
      
+
        system.time(
+    print(mkinpredict(SFO_SFO, c(k_parent_m1 = 0.05, k_parent_sink = 0.1, k_m1_sink = 0.01),
+                c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
+                solution_type = "deSolve")[201,]))
      
+
          time   parent       m1
+201   20 4.978707 27.46227
+
      
+
         user  system elapsed 
+  0.012   0.004   0.002 
+
      
+
        system.time(
+    print(mkinpredict(SFO_SFO, c(k_parent_m1 = 0.05, k_parent_sink = 0.1, k_m1_sink = 0.01),
+                c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
+                solution_type = "deSolve", use_compiled = FALSE)[201,]))
      
+
          time   parent       m1
+201   20 4.978707 27.46227
+
      
+
         user  system elapsed 
+  0.036   0.000   0.036 
+
      +
      +
      + + + + +

      Author

      + + Johannes Ranke + + +
      +
      + + +
      + + \ No newline at end of file diff --git a/docs/mkinresplot-4.png b/docs/mkinresplot-4.png new file mode 100644 index 00000000..7dbd7522 Binary files /dev/null and b/docs/mkinresplot-4.png differ diff --git a/docs/mkinresplot.html b/docs/mkinresplot.html new file mode 100644 index 00000000..09f4f7b9 --- /dev/null +++ b/docs/mkinresplot.html @@ -0,0 +1,163 @@ + + + + +mkinresplot. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
      +
      +
      + mkin 0.9.44.9000 +
      + +
      +
      +
      +
      + + +
      +
      + +
      + +

      + Function to plot residuals stored in an mkin object +

      + +
      +
      +

      Usage

      + 
      mkinresplot(object,
+    obs_vars = names(object$mkinmod$map),
+    xlim = c(0, 1.1 * max(object$data$time)),
+    xlab = "Time", ylab = "Residual",
+    maxabs = "auto", legend = TRUE, lpos = "topright", ...)
      + +

      Arguments

      +
      +
      object
      +
      + A fit represented in an mkinfit object. +
      +
      obs_vars
      +
      + A character vector of names of the observed variables for which residuals + should be plotted. Defaults to all observed variables in the model +
      +
      xlim
      +
      + plot range in x direction. +
      +
      xlab
      +
      + Label for the x axis. Defaults to "Time [days]". +
      +
      ylab
      +
      + Label for the y axis. Defaults to "Residual [% of applied radioactivity]". +
      +
      maxabs
      +
      + Maximum absolute value of the residuals. This is used for the scaling of + the y axis and defaults to "auto". +
      +
      legend
      +
      + Should a legend be plotted? Defaults to "TRUE". +
      +
      lpos
      +
      + Where should the legend be placed? Default is "topright". Will be passed on to + legend.
      +
      ...
      +
      + further arguments passed to plot. +
      +
      + +
      +

      Description

      + +

      This function plots the residuals for the specified subset of the + observed variables from an mkinfit object. A combined plot of the fitted + model and the residuals can be obtained using plot.mkinfit + using the argument show_residuals = TRUE.

      + +
      + +
      +

      Value

      + +

      + Nothing is returned by this function, as it is called for its side effect, namely to produce a plot. +

      + +
      + +

      Examples

      + 
      model <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"))
      
+Successfully compiled differential equation model from auto-generated C code.
+
      fit <- mkinfit(model, FOCUS_2006_D, quiet = TRUE)
+mkinresplot(fit, "m1")
      
+
      +
      +
      + + +

      See also

      +

      mkinplot, for a way to plot the data and the fitted lines of the + mkinfit object.

      + + + +

      Author

      + + Johannes Ranke + + +
      +
      + + +
      + + \ No newline at end of file diff --git a/docs/mkinsub.html b/docs/mkinsub.html new file mode 100644 index 00000000..5272a578 --- /dev/null +++ b/docs/mkinsub.html @@ -0,0 +1,149 @@ + + + + +mkinsub. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
      +
      +
      + mkin 0.9.44.9000 +
      + +
      +
      +
      +
      + + +
      +
      + +
      + +

      + Function to set up a kinetic submodel for one state variable +

      + +
      +
      +

      Usage

      + 
      mkinsub(submodel, to = NULL, sink = TRUE, full_name = NA)
      + +

      Arguments

      +
      +
      submodel
      +
      + Character vector of length one to specify the submodel type. See + mkinmod for the list of allowed submodel names. +
      +
      to
      +
      + Vector of the names of the state variable to which a transformation + shall be included in the model. +
      +
      sink
      +
      + Should a pathway to sink be included in the model in addition to the + pathways to other state variables? +
      +
      full_name
      +
      + An optional name to be used e.g. for plotting fits performed with the model. + You can use non-ASCII characters here, but then your R code will not be + portable, i.e. may produce unintended plot results on other + operating systems or system configurations. +
      +
      + +
      +

      Description

      + +

      This is a convenience function to set up the lists used as arguments for + mkinmod.

      + +
      + +
      +

      Value

      + +

      + A list for use with mkinmod. +

      + +
      + +

      Examples

      + 
      # One parent compound, one metabolite, both single first order.
+SFO_SFO <- mkinmod(
+  parent = list(type = "SFO", to = "m1"),
+  m1 = list(type = "SFO"))
      
+Successfully compiled differential equation model from auto-generated C code.
+
      
+# The same model using mkinsub
+SFO_SFO.2 <- mkinmod(
+  parent = mkinsub("SFO", "m1"),
+  m1 = mkinsub("SFO"))
      
+Successfully compiled differential equation model from auto-generated C code.
+
      
+# Now supplying full names
+SFO_SFO.2 <- mkinmod(
+  parent = mkinsub("SFO", "m1", full_name = "Test compound"),
+  m1 = mkinsub("SFO", full_name = "Metabolite M1"))
      
+Successfully compiled differential equation model from auto-generated C code.
      +
      +
      + + + + +

      Author

      + + Johannes Ranke + + +
      +
      + + +
      + + \ No newline at end of file diff --git a/docs/mmkin.html b/docs/mmkin.html new file mode 100644 index 00000000..5924d3d5 --- /dev/null +++ b/docs/mmkin.html @@ -0,0 +1,172 @@ + + + + +mmkin. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
      +
      +
      + mkin 0.9.44.9000 +
      + +
      +
      +
      +
      + + +
      +
      + +
      + +

      + Fit one or more kinetic models with one or more state variables to one or more datasets +

      + +
      +
      +

      Usage

      + 
      mmkin(models, datasets,
+      cores = round(detectCores()/2), cluster = NULL, ...)
      + +

      Arguments

      +
      +
      models
      +
      + Either a character vector of shorthand names ("SFO", "FOMC", "DFOP", + "HS", "SFORB"), or an optionally named list of mkinmod + objects. +
      +
      datasets
      +
      + An optionally named list of datasets suitable as observed data for + mkinfit. +
      +
      cores
      +
      + The number of cores to be used for multicore processing. This is only + used when the cluster argument is NULL. +
      +
      cluster
      +
      + A cluster as returned by makeCluster to be used for parallel + execution. +
      +
      ...
      +
      + Further arguments that will be passed to mkinfit. +
      +
      + +
      +

      Description

      + +

      This function calls mkinfit on all combinations of models and datasets + specified in its first two arguments.

      + +
      + +
      +

      Value

      + +

      + A matrix of mkinfit objects that can be indexed using the model + and dataset names as row and column indices. +

      + +
      + +

      Examples

      + 
      ## Not run: ------------------------------------
+# m_synth_SFO_lin <- mkinmod(parent = mkinsub("SFO", "M1"),
+#                            M1 = mkinsub("SFO", "M2"),
+#                            M2 = mkinsub("SFO"), use_of_ff = "max")
+# 
+# m_synth_FOMC_lin <- mkinmod(parent = mkinsub("FOMC", "M1"),
+#                             M1 = mkinsub("SFO", "M2"),
+#                             M2 = mkinsub("SFO"), use_of_ff = "max")
+# 
+# models <- list(SFO_lin = m_synth_SFO_lin, FOMC_lin = m_synth_FOMC_lin)
+# datasets <- lapply(synthetic_data_for_UBA_2014[1:3], function(x) x$data)
+# names(datasets) <- paste("Dataset", 1:3)
+# 
+# time_default <- system.time(fits.0 <- mmkin(models, datasets))
+# time_1 <- system.time(fits.1 <- mmkin(models, datasets, cores = 1))
+# 
+# time_default
+# time_1
+# 
+# endpoints(fits[["SFO_lin", 2]])
+# 
+# # Plot.mkinfit handles rows or columns of mmkin result objects
+# plot(fits.0[1, ])
+# plot(fits.0[1, ], obs_var = c("M1", "M2"))
+# plot(fits.0[, 1])
+# # Use double brackets to extract a single mkinfit object, which will be plotted
+# # by plot.mkinfit
+# plot(fits.0[[1, 1]], sep_obs = TRUE, show_residuals = TRUE, show_errmin = TRUE)
+# # Plotting with mmkin (single brackets, extracting an mmkin object) does not
+# # allow to plot the observed variables separately
+# plot(fits.0[1, 1])
+## ---------------------------------------------
      +
      +
      + + +

      See also

      +

      [.mmkin for subsetting, plot.mmkin for plotting.

      + + + +

      Author

      + + Johannes Ranke + + +
      +
      + + +
      + + \ No newline at end of file diff --git a/docs/plot.mkinfit-10.png b/docs/plot.mkinfit-10.png new file mode 100644 index 00000000..09bb687c Binary files /dev/null and b/docs/plot.mkinfit-10.png differ diff --git a/docs/plot.mkinfit-4.png b/docs/plot.mkinfit-4.png new file mode 100644 index 00000000..1e90d789 Binary files /dev/null and b/docs/plot.mkinfit-4.png differ diff --git a/docs/plot.mkinfit-6.png b/docs/plot.mkinfit-6.png new file mode 100644 index 00000000..1dfbbe4c Binary files /dev/null and b/docs/plot.mkinfit-6.png differ diff --git a/docs/plot.mkinfit-8.png b/docs/plot.mkinfit-8.png new file mode 100644 index 00000000..e4ab674a Binary files /dev/null and b/docs/plot.mkinfit-8.png differ diff --git a/docs/plot.mkinfit.html b/docs/plot.mkinfit.html new file mode 100644 index 00000000..76ae3c4d --- /dev/null +++ b/docs/plot.mkinfit.html @@ -0,0 +1,238 @@ + + + + +plot.mkinfit. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
      +
      +
      + mkin 0.9.44.9000 +
      + +
      +
      +
      +
      + + +
      +
      + +
      + +

      + Plot the observed data and the fitted model of an mkinfit object +

      + +
      +
      +

      Usage

      + 
      # S3 method for mkinfit
+plot(x, fit = x,
+  obs_vars = names(fit$mkinmod$map),
+  xlab = "Time", ylab = "Observed",
+  xlim = range(fit$data$time),
+  ylim = "default",
+  col_obs = 1:length(obs_vars), pch_obs = col_obs,
+  lty_obs = rep(1, length(obs_vars)),
+  add = FALSE, legend = !add,
+  show_residuals = FALSE, maxabs = "auto",
+  sep_obs = FALSE, rel.height.middle = 0.9,
+  lpos = "topright", inset = c(0.05, 0.05),
+  show_errmin = FALSE, errmin_digits = 3, ...)
+plot_sep(fit, sep_obs = TRUE,  show_residuals = TRUE, show_errmin = TRUE, ...)
      + +

      Arguments

      +
      +
      x
      +
      + Alias for fit introduced for compatibility with the generic S3 method. +
      +
      fit
      +
      + An object of class mkinfit. +
      +
      obs_vars
      +
      + A character vector of names of the observed variables for which the + data and the model should be plotted. Defauls to all observed variables + in the model. +
      +
      xlab
      +
      + Label for the x axis. +
      +
      ylab
      +
      + Label for the y axis. +
      +
      xlim
      +
      + Plot range in x direction. +
      +
      ylim
      +
      + Plot range in y direction. +
      +
      col_obs
      +
      + Colors used for plotting the observed data and the corresponding model prediction lines. +
      +
      pch_obs
      +
      + Symbols to be used for plotting the data. +
      +
      lty_obs
      +
      + Line types to be used for the model predictions. +
      +
      add
      +
      + Should the plot be added to an existing plot? +
      +
      legend
      +
      + Should a legend be added to the plot? +
      +
      show_residuals
      +
      + Should residuals be shown? If only one plot of the fits is shown, the + residual plot is in the lower third of the plot? Otherwise, i.e. if + "sep_obs" is given, the residual plots will be located to the right of + the plots of the fitted curves. +
      +
      maxabs
      +
      + Maximum absolute value of the residuals. This is used for the scaling of + the y axis and defaults to "auto". +
      +
      sep_obs
      +
      + Should the observed variables be shown in separate subplots? If yes, residual plots + requested by "show_residuals" will be shown next to, not below the plot of the fits. +
      +
      rel.height.middle
      +
      + The relative height of the middle plot, if more than two rows of plots are shown. +
      +
      lpos
      +
      + Position(s) of the legend(s). Passed to legend as the first argument. + If not length one, this should be of the same length as the obs_var argument. +
      +
      inset
      +
      + Passed to legend if applicable. +
      +
      show_errmin
      +
      + Should the FOCUS chi2 error value be shown in the upper margin of the plot? +
      +
      errmin_digits
      +
      + The number of significant digits for rounding the FOCUS chi2 error percentage. +
      +
      ...
      +
      + Further arguments passed to plot. +
      +
      + +
      +

      Description

      + +

      Solves the differential equations with the optimised and fixed parameters + from a previous successful call to mkinfit and plots + the observed data together with the solution of the fitted model.

      + +
      + +
      +

      Value

      + +

      + The function is called for its side effect. +

      + +
      + +

      Examples

      + 
      # One parent compound, one metabolite, both single first order, path from
+# parent to sink included, use Levenberg-Marquardt for speed
+SFO_SFO <- mkinmod(parent = mkinsub("SFO", "m1", full = "Parent"),
+                   m1 = mkinsub("SFO", full = "Metabolite M1" ))
      
+Successfully compiled differential equation model from auto-generated C code.
+
      fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE, method.modFit = "Marq")
+plot(fit)
      
+
      
+
      plot(fit, show_residuals = TRUE)
      
+
      
+
      
+# Show the observed variables separately
+plot(fit, sep_obs = TRUE, lpos = c("topright", "bottomright"))
      
+
      
+
      
+# Show the observed variables separately, with residuals
+plot(fit, sep_obs = TRUE, show_residuals = TRUE, lpos = c("topright", "bottomright"),
+     show_errmin = TRUE)
      
+
      
+
      
+# The same can be obtained with less typing, using the convenience function plot_sep
+plot_sep(fit, lpos = c("topright", "bottomright"))
      +
      +
      + + + + +

      Author

      + + Johannes Ranke + + +
      +
      + + +
      + + \ No newline at end of file diff --git a/docs/plot.mmkin-2.png b/docs/plot.mmkin-2.png new file mode 100644 index 00000000..a26e413f Binary files /dev/null and b/docs/plot.mmkin-2.png differ diff --git a/docs/plot.mmkin-4.png b/docs/plot.mmkin-4.png new file mode 100644 index 00000000..f23ed172 Binary files /dev/null and b/docs/plot.mmkin-4.png differ diff --git a/docs/plot.mmkin-6.png b/docs/plot.mmkin-6.png new file mode 100644 index 00000000..dc24d92f Binary files /dev/null and b/docs/plot.mmkin-6.png differ diff --git a/docs/plot.mmkin.html b/docs/plot.mmkin.html new file mode 100644 index 00000000..24b0d60c --- /dev/null +++ b/docs/plot.mmkin.html @@ -0,0 +1,158 @@ + + + + +plot.mmkin. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
      +
      +
      + mkin 0.9.44.9000 +
      + +
      +
      +
      +
      + + +
      +
      + +
      + +

      + Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object +

      + +
      +
      +

      Usage

      + 
      # S3 method for mmkin
+plot(x, main = "auto", legends = 1, errmin_var = "All data", errmin_digits = 3,
+              cex = 0.7, rel.height.middle = 0.9, ...)
      + +

      Arguments

      +
      +
      x
      +
      + An object of class mmkin, with either one row or one column. +
      +
      main
      +
      + The main title placed on the outer margin of the plot. +
      +
      legends
      +
      + An index for the fits for which legends should be shown. +
      +
      errmin_var
      +
      + The variable for which the FOCUS chi2 error value should be shown. +
      +
      errmin_digits
      +
      + The number of significant digits for rounding the FOCUS chi2 error percentage. +
      +
      cex
      +
      + Passed to the plot functions and mtext. +
      +
      rel.height.middle
      +
      + The relative height of the middle plot, if more than two rows of plots are shown. +
      +
      ...
      +
      + Further arguments passed to plot.mkinfit and mkinresplot. +
      +
      + +
      +

      Description

      + +

      When x is a row selected from an mmkin object ([.mmkin), the same model + fitted for at least one dataset is shown. When it is a column, the fit of at least one model + to the same dataset is shown.

      + +
      + +
      +

      Value

      + +

      + The function is called for its side effect. +

      + +
      + +

      Examples

      + 
        # Only use one core not to offend CRAN checks, use Levenberg-Marquardt for speed
+  fits <- mmkin(c("FOMC", "HS"), list("FOCUS B" = FOCUS_2006_B, "FOCUS C" = FOCUS_2006_C),
+                cores = 1, quiet = TRUE, method.modFit = "Marq")
+  plot(fits[, "FOCUS C"])
      
+
      
+
        plot(fits["FOMC", ])
      
+
      
+
      
+  # We can also plot a single fit, if we like the way mmkin works, but then the plot 
+  # height should be smaller than the plot width (this is not possible for the html pages
+  # generated by staticdocs, as far as I know).
+  plot(fits["FOMC", "FOCUS C"]) # same as plot(fits[1, 2])
      
+
      +
      +
      + + + + +

      Author

      + + Johannes Ranke + + +
      +
      + + +
      + + \ No newline at end of file diff --git a/docs/print.mkinds.html b/docs/print.mkinds.html new file mode 100644 index 00000000..308744c1 --- /dev/null +++ b/docs/print.mkinds.html @@ -0,0 +1,101 @@ + + + + +print.mkinds. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
      +
      +
      + mkin 0.9.44.9000 +
      + +
      +
      +
      +
      + + +
      +
      + +
      + +

      + Print mkinds objects +

      + +
      +
      +

      Usage

      + 
      # S3 method for mkinds
+print(x, ...)
      + +

      Arguments

      +
      +
      x
      +
      + An mkinds object. +
      +
      ...
      +
      + Not used. +
      +
      + +
      +

      Description

      + +

      Print mkinds objects.

      + +
      +
      +
      + + + + + +
      +
      + + +
      + + \ No newline at end of file diff --git a/docs/print.mkinmod.html b/docs/print.mkinmod.html new file mode 100644 index 00000000..4ad7737b --- /dev/null +++ b/docs/print.mkinmod.html @@ -0,0 +1,101 @@ + + + + +print.mkinmod. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
      +
      +
      + mkin 0.9.44.9000 +
      + +
      +
      +
      +
      + + +
      +
      + +
      + +

      + Print mkinmod objects +

      + +
      +
      +

      Usage

      + 
      # S3 method for mkinmod
+print(x, ...)
      + +

      Arguments

      +
      +
      x
      +
      + An mkinmod object. +
      +
      ...
      +
      + Not used. +
      +
      + +
      +

      Description

      + +

      Print mkinmod objects in a way that the user finds his way to get to its components.

      + +
      +
      +
      + + + + + +
      +
      + + +
      + + \ No newline at end of file diff --git a/docs/reference.html b/docs/reference.html new file mode 100644 index 00000000..df1bd9a8 --- /dev/null +++ b/docs/reference.html @@ -0,0 +1,288 @@ + + + + +Function reference. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
      +
      +
      + mkin 0.9.44.9000 +
      + +
      +
      +
      +
      + + +
      +
      + +
      + +
      +
      +

      Function reference

      + +

      Main functions

      +

      Essential functionality

      + + +
        + +
      • + mkinmod
        + Function to set up a kinetic model with one or more state variables +
        • + +
      • + mkinfit
        + Fit a kinetic model to data with one or more state variables +
        • + +
      • + mmkin
        + Fit one or more kinetic models with one or more state variables to one or more datasets +
        • + +
      +

      Show results

      +

      Functions working on mkinfit objects

      + + +
        + +
      • + plot.mkinfit(plot_sep)
        + Plot the observed data and the fitted model of an mkinfit object +
        • + +
      • + summary.mkinfit(print.summary.mkinfit)
        + Summary method for class "mkinfit" +
        • + +
      • + mkinresplot
        + Function to plot residuals stored in an mkin object +
        • + +
      • + mkinparplot
        + Function to plot the confidence intervals obtained using + mkinfit +
        • + +
      • + endpoints
        +Function to calculate endpoints for further use from kinetic models fitted with mkinfit +
        • + +
      • + mkinerrmin
        +Calculate the minimum error to assume in order to pass the variance test +
        • + +
      +

      Work with mmkin objects

      +

      Functions working with aggregated results

      + + +
        + +
      • + [.mmkin
        Subsetting method for mmkin objects
        • + +
      • + plot.mmkin
        + Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object +
        • + +
      +

      Datasets and known results

      + + + +

      Helper functions

      + + +
        + +
      • + mkin_wide_to_long
        + Convert a dataframe with observations over time into long format +
        • + +
      • + mkin_long_to_wide
        + Convert a dataframe from long to wide format +
        • + +
      • + mkinsub
        + Function to set up a kinetic submodel for one state variable +
        • + +
      • + mkinds
        A dataset class for mkin
        • + +
      • + print.mkinds
        + Print mkinds objects +
        • + +
      • + print.mkinmod
        + Print mkinmod objects +
        • + +
      • + mkinpredict
        + Produce predictions from a kinetic model using specific parameters +
        • + +
      • + transform_odeparms(backtransform_odeparms)
        + Functions to transform and backtransform kinetic parameters for fitting +
        • + +
      • + ilr(invilr)
        + Function to perform isometric log-ratio transformation +
        • + +
      • + geometric_mean
        Calculate the geometric mean
        • + +
      +

      Analytical solutions

      +

      Parent only model solutions

      + + + +

      Deprecated functions

      +

      Functions that have been superseeded

      + + +
        + +
      • + mkinplot
        + Plot the observed data and the fitted model of an mkinfit object +
        • + +
      +

      Other

      + + +
        + +
      • + add_err
        + Add normally distributed errors to simulated kinetic degradation data +
        • + +
      +
      +
      + + +
      + + \ No newline at end of file diff --git a/docs/schaefer07_complex_case.html b/docs/schaefer07_complex_case.html new file mode 100644 index 00000000..23ed3a26 --- /dev/null +++ b/docs/schaefer07_complex_case.html @@ -0,0 +1,134 @@ + + + + +schaefer07_complex_case. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
      +
      +
      + mkin 0.9.44.9000 +
      + +
      +
      +
      +
      + + +
      +
      + +
      + +

      + Metabolism data set used for checking the software quality of KinGUI +

      + +
      +
      +

      Usage

      + 
      data(schaefer07_complex_case)
      + +
      +

      Description

      + +

      This dataset was used for a comparison of KinGUI and ModelMaker to check the + software quality of KinGUI in the original publication (Schäfer et al., 2007). + The results from the fitting are also included.

      + +
      + +
      +

      Format

      + +

      The data set is a data frame with 8 observations on the following 6 variables. +

      +
      time
      a numeric vector

      + +

      parent
      a numeric vector

      + +

      A1
      a numeric vector

      + +

      B1
      a numeric vector

      + +

      C1
      a numeric vector

      + +

      A2
      a numeric vector

      + +

      + +

      The results are a data frame with 14 results for different parameter values

      + +
      + +
      +

      References

      + +

      Schäfer D, Mikolasch B, Rainbird P and Harvey B (2007). KinGUI: a new kinetic + software tool for evaluations according to FOCUS degradation kinetics. In: Del + Re AAM, Capri E, Fragoulis G and Trevisan M (Eds.). Proceedings of the XIII + Symposium Pesticide Chemistry, Piacenza, 2007, p. 916-923.

      + +
      + +

      Examples

      + 
      data <- mkin_wide_to_long(schaefer07_complex_case, time = "time")
+model <- mkinmod(
+  parent = list(type = "SFO", to = c("A1", "B1", "C1"), sink = FALSE),
+  A1 = list(type = "SFO", to = "A2"),
+  B1 = list(type = "SFO"),
+  C1 = list(type = "SFO"),
+  A2 = list(type = "SFO"), use_of_ff = "max")
      
+Successfully compiled differential equation model from auto-generated C code.
+
      ## Not run: mkinfit(model, data, plot=TRUE)
      +
      +
      + + + + + +
      +
      + + +
      + + \ No newline at end of file diff --git a/docs/summary.mkinfit.html b/docs/summary.mkinfit.html new file mode 100644 index 00000000..574c5c07 --- /dev/null +++ b/docs/summary.mkinfit.html @@ -0,0 +1,259 @@ + + + + +summary.mkinfit. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
      +
      +
      + mkin 0.9.44.9000 +
      + +
      +
      +
      +
      + + +
      +
      + +
      + +

      + Summary method for class "mkinfit" +

      + +
      +
      +

      Usage

      + 
      # S3 method for mkinfit
+summary(object, data = TRUE, distimes = TRUE, alpha = 0.05, ...)
+# S3 method for summary.mkinfit
+print(x, digits = max(3, getOption("digits") - 3), ...)
      + +

      Arguments

      +
      +
      object
      +
      + an object of class mkinfit. +
      +
      x
      +
      + an object of class summary.mkinfit. +
      +
      data
      +
      + logical, indicating whether the data should be included in the summary. +
      +
      distimes
      +
      + logical, indicating whether DT50 and DT90 values should be included. +
      +
      alpha
      +
      + error level for confidence interval estimation from t distribution +
      +
      digits
      +
      + Number of digits to use for printing +
      +
      ...
      +
      + optional arguments passed to methods like print. +
      +
      + +
      +

      Description

      + +

      Lists model equations, the summary as returned by summary.modFit, + the chi2 error levels calculated according to FOCUS guidance (2006) as far + as defined therein, and optionally the data, consisting of observed, predicted + and residual values.

      + +
      + +
      +

      Value

      + +

      + The summary function returns a list derived from + summary.modFit, with components, among others +
      version, Rversion
      The mkin and R versions used

      + +

      date.fit, date.summary
      The dates where the fit and the summary were produced

      + +

      use_of_ff
      Was maximum or minimum use made of formation fractions

      + +

      residuals, residualVariance, sigma, modVariance, df
      As in summary.modFit

      + +

      cov.unscaled, cov.scaled, info, niter, stopmess, par
      As in summary.modFit

      + +

      bpar
      Optimised and backtransformed parameters

      + +

      diffs
      The differential equations used in the model

      + +

      data
      The data (see Description above).

      + +

      start
      The starting values and bounds, if applicable, for optimised parameters.

      + +

      fixed
      The values of fixed parameters.

      + +

      errmin
      The chi2 error levels for each observed variable.

      + +

      bparms.ode
      All backtransformed ODE parameters, for use as starting parameters for + related models.

      + +

      ff
      The estimated formation fractions derived from the fitted model.

      + +

      distimes
      The DT50 and DT90 values for each observed variable.

      + +

      SFORB
      If applicable, eigenvalues of SFORB components of the model.

      + +

      The print method is called for its side effect, i.e. printing the summary. +

      + +
      + +
      +

      References

      + +

      FOCUS (2006) “Guidance Document on Estimating Persistence and + Degradation Kinetics from Environmental Fate Studies on Pesticides in EU + Registration” Report of the FOCUS Work Group on Degradation Kinetics, + EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, + http://focus.jrc.ec.europa.eu/dk

      + +
      + +

      Examples

      + 
        summary(mkinfit(mkinmod(parent = list(type = "SFO")), FOCUS_2006_A, quiet = TRUE))
      
+
      mkin version:    0.9.44.9000 
+R version:       3.3.1 
+Date of fit:     Sat Sep 10 05:53:12 2016 
+Date of summary: Sat Sep 10 05:53:12 2016 
+
+Equations:
+d_parent = - k_parent_sink * parent
+
+Model predictions using solution type analytical 
+
+Fitted with method Port using 35 model solutions performed in 0.09 s
+
+Weighting: none
+
+Starting values for parameters to be optimised:
+               value   type
+parent_0      101.24  state
+k_parent_sink   0.10 deparm
+
+Starting values for the transformed parameters actually optimised:
+                       value lower upper
+parent_0          101.240000  -Inf   Inf
+log_k_parent_sink  -2.302585  -Inf   Inf
+
+Fixed parameter values:
+None
+
+Optimised, transformed parameters with symmetric confidence intervals:
+                  Estimate Std. Error  Lower   Upper
+parent_0           109.200     4.3910 98.410 119.900
+log_k_parent_sink   -3.291     0.1152 -3.573  -3.009
+
+Parameter correlation:
+                  parent_0 log_k_parent_sink
+parent_0             1.000             0.575
+log_k_parent_sink    0.575             1.000
+
+Residual standard error: 6.08 on 6 degrees of freedom
+
+Backtransformed parameters:
+Confidence intervals for internally transformed parameters are asymmetric.
+t-test (unrealistically) based on the assumption of normal distribution
+for estimators of untransformed parameters.
+               Estimate t value    Pr(>t)    Lower     Upper
+parent_0      109.20000  24.860 1.394e-07 98.41000 119.90000
+k_parent_sink   0.03722   8.679 6.457e-05  0.02807   0.04934
+
+Chi2 error levels in percent:
+         err.min n.optim df
+All data   8.385       2  6
+parent     8.385       2  6
+
+Resulting formation fractions:
+            ff
+parent_sink  1
+
+Estimated disappearance times:
+        DT50  DT90
+parent 18.62 61.87
+
+Data:
+ time variable observed predicted residual
+    0   parent   101.24   109.153  -7.9132
+    3   parent    99.27    97.622   1.6484
+    7   parent    90.11    84.119   5.9913
+   14   parent    72.19    64.826   7.3641
+   30   parent    29.71    35.738  -6.0283
+   62   parent     5.98    10.862  -4.8818
+   90   parent     1.54     3.831  -2.2911
+  118   parent     0.39     1.351  -0.9613
+
      +
      +
      + + + + +

      Author

      + + Johannes Ranke + + +
      +
      + + +
      + + \ No newline at end of file diff --git a/docs/synthetic_data_for_UBA.html b/docs/synthetic_data_for_UBA.html new file mode 100644 index 00000000..1a29a7b2 --- /dev/null +++ b/docs/synthetic_data_for_UBA.html @@ -0,0 +1,156 @@ + + + + +synthetic_data_for_UBA_2014. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
      +
      +
      + mkin 0.9.44.9000 +
      + +
      +
      +
      +
      + + +
      +
      + +
      + +

      + Synthetic datasets for one parent compound with two metabolites +

      + +
      +
      +

      Usage

      + 
      synthetic_data_for_UBA_2014
      + +
      +

      Description

      + +

      The 12 datasets were generated using four different models and three different + variance components. The four models are either the SFO or the DFOP model with either + two sequential or two parallel metabolites.

      + +

      Variance component 'a' is based on a normal distribution with standard deviation of 3, + Variance component 'b' is also based on a normal distribution, but with a standard deviation of 7. + Variance component 'c' is based on the error model from Rocke and Lorenzato (1995), with the + minimum standard deviation (for small y values) of 0.5, and a proportionality constant of 0.07 + for the increase of the standard deviation with y.

      + +

      Initial concentrations for metabolites and all values where adding the variance component resulted + in a value below the assumed limit of detection of 0.1 were set to NA.

      + +

      As an example, the first dataset has the title SFO_lin_a and is based on the SFO model + with two sequential metabolites (linear pathway), with added variance component 'a'.

      + +

      Compare also the code in the example section to see the degradation models.

      + +
      + +
      +

      Format

      + +

      A list containing datasets in the form internally used by the 'gmkin' package. + The list has twelfe components. Each of the components is one dataset that has, + among others, the following components +

      +
      title
      The name of the dataset, e.g. SFO_lin_a

      + +

      data
      A data frame with the data in the form expected by mkinfit

      + +

      + +

      + +
      + +
      +

      Source

      + +

      Ranke (2014) Prüfung und Validierung von Modellierungssoftware als Alternative + zu ModelMaker 4.0, Umweltbundesamt Projektnummer 27452

      + +

      Rocke, David M. und Lorenzato, Stefan (1995) A two-component model for + measurement error in analytical chemistry. Technometrics 37(2), 176-184.

      + +
      + +

      Examples

      + 
      ## Not run: ------------------------------------
+# m_synth_SFO_lin <- mkinmod(parent = list(type = "SFO", to = "M1"),
+#                            M1 = list(type = "SFO", to = "M2"),
+#                            M2 = list(type = "SFO"), use_of_ff = "max")
+# 
+# 
+# m_synth_SFO_par <- mkinmod(parent = list(type = "SFO", to = c("M1", "M2"),
+#                                          sink = FALSE),
+#                            M1 = list(type = "SFO"),
+#                            M2 = list(type = "SFO"), use_of_ff = "max")
+# 
+# m_synth_DFOP_lin <- mkinmod(parent = list(type = "DFOP", to = "M1"),
+#                             M1 = list(type = "SFO", to = "M2"),
+#                             M2 = list(type = "SFO"), use_of_ff = "max")
+# 
+# m_synth_DFOP_par <- mkinmod(parent = list(type = "DFOP", to = c("M1", "M2"),
+#                                           sink = FALSE),
+#                             M1 = list(type = "SFO"),
+#                             M2 = list(type = "SFO"), use_of_ff = "max")
+# 
+# mkinfit(m_synth_SFO_lin, synthetic_data_for_UBA_2014[[1]]$data)
+## ---------------------------------------------
      +
      +
      + + + + + +
      +
      + + +
      + + \ No newline at end of file diff --git a/docs/transform_odeparms.html b/docs/transform_odeparms.html new file mode 100644 index 00000000..6f48b1fa --- /dev/null +++ b/docs/transform_odeparms.html @@ -0,0 +1,281 @@ + + + + +transform_odeparms. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
      +
      +
      + mkin 0.9.44.9000 +
      + +
      +
      +
      +
      + + +
      +
      + +
      + +

      + Functions to transform and backtransform kinetic parameters for fitting +

      + +
      +
      +

      Usage

      + 
      transform_odeparms(parms, mkinmod,
+                   transform_rates = TRUE, transform_fractions = TRUE)
+backtransform_odeparms(transparms, mkinmod,
+                       transform_rates = TRUE, transform_fractions = TRUE)
      + +

      Arguments

      +
      +
      parms
      +
      + Parameters of kinetic models as used in the differential equations. +
      +
      transparms
      +
      + Transformed parameters of kinetic models as used in the fitting procedure. +
      +
      mkinmod
      +
      + The kinetic model of class mkinmod, containing the names of + the model variables that are needed for grouping the formation fractions + before ilr transformation, the parameter names and + the information if the pathway to sink is included in the model. +
      +
      transform_rates
      +
      + Boolean specifying if kinetic rate constants should be transformed in the + model specification used in the fitting for better compliance with the + assumption of normal distribution of the estimator. If TRUE, also + alpha and beta parameters of the FOMC model are log-transformed, as well + as k1 and k2 rate constants for the DFOP and HS models and the break point tb + of the HS model. +
      +
      transform_fractions
      +
      + Boolean specifying if formation fractions constants should be transformed in the + model specification used in the fitting for better compliance with the + assumption of normal distribution of the estimator. The default (TRUE) is + to do transformations. The g parameter of the DFOP and HS models are also + transformed, as they can also be seen as compositional data. The + transformation used for these transformations is the ilr + transformation. +
      +
      + +
      +

      Description

      + +

      The transformations are intended to map parameters that should only take + on restricted values to the full scale of real numbers. For kinetic rate + constants and other paramters that can only take on positive values, a + simple log transformation is used. For compositional parameters, such as + the formations fractions that should always sum up to 1 and can not be + negative, the ilr transformation is used.

      + +

      The transformation of sets of formation fractions is fragile, as it supposes + the same ordering of the components in forward and backward transformation. + This is no problem for the internal use in mkinfit.

      + +
      + +
      +

      Value

      + +

      + A vector of transformed or backtransformed parameters with the same names + as the original parameters. +

      + +
      + +

      Examples

      + 
      SFO_SFO <- mkinmod(
+  parent = list(type = "SFO", to = "m1", sink = TRUE),
+  m1 = list(type = "SFO"))
      
+Successfully compiled differential equation model from auto-generated C code.
+
      # Fit the model to the FOCUS example dataset D using defaults
+fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE)
+summary(fit, data=FALSE) # See transformed and backtransformed parameters
      
+
      mkin version:    0.9.44.9000 
+R version:       3.3.1 
+Date of fit:     Sat Sep 10 05:53:13 2016 
+Date of summary: Sat Sep 10 05:53:13 2016 
+
+Equations:
+d_parent = - k_parent_sink * parent - k_parent_m1 * parent
+d_m1 = + k_parent_m1 * parent - k_m1_sink * m1
+
+Model predictions using solution type deSolve 
+
+Fitted with method Port using 153 model solutions performed in 0.634 s
+
+Weighting: none
+
+Starting values for parameters to be optimised:
+                 value   type
+parent_0      100.7500  state
+k_parent_sink   0.1000 deparm
+k_parent_m1     0.1001 deparm
+k_m1_sink       0.1002 deparm
+
+Starting values for the transformed parameters actually optimised:
+                       value lower upper
+parent_0          100.750000  -Inf   Inf
+log_k_parent_sink  -2.302585  -Inf   Inf
+log_k_parent_m1    -2.301586  -Inf   Inf
+log_k_m1_sink      -2.300587  -Inf   Inf
+
+Fixed parameter values:
+     value  type
+m1_0     0 state
+
+Optimised, transformed parameters with symmetric confidence intervals:
+                  Estimate Std. Error  Lower   Upper
+parent_0            99.600    1.61400 96.330 102.900
+log_k_parent_sink   -3.038    0.07826 -3.197  -2.879
+log_k_parent_m1     -2.980    0.04124 -3.064  -2.897
+log_k_m1_sink       -5.248    0.13610 -5.523  -4.972
+
+Parameter correlation:
+                  parent_0 log_k_parent_sink log_k_parent_m1 log_k_m1_sink
+parent_0           1.00000            0.6075        -0.06625       -0.1701
+log_k_parent_sink  0.60752            1.0000        -0.08740       -0.6253
+log_k_parent_m1   -0.06625           -0.0874         1.00000        0.4716
+log_k_m1_sink     -0.17006           -0.6253         0.47163        1.0000
+
+Residual standard error: 3.211 on 36 degrees of freedom
+
+Backtransformed parameters:
+Confidence intervals for internally transformed parameters are asymmetric.
+t-test (unrealistically) based on the assumption of normal distribution
+for estimators of untransformed parameters.
+               Estimate t value    Pr(>t)     Lower     Upper
+parent_0      99.600000  61.720 2.024e-38 96.330000 1.029e+02
+k_parent_sink  0.047920  12.780 3.050e-15  0.040890 5.616e-02
+k_parent_m1    0.050780  24.250 3.407e-24  0.046700 5.521e-02
+k_m1_sink      0.005261   7.349 5.758e-09  0.003992 6.933e-03
+
+Chi2 error levels in percent:
+         err.min n.optim df
+All data   6.398       4 15
+parent     6.827       3  6
+m1         4.490       1  9
+
+Resulting formation fractions:
+                ff
+parent_sink 0.4855
+parent_m1   0.5145
+m1_sink     1.0000
+
+Estimated disappearance times:
+          DT50   DT90
+parent   7.023  23.33
+m1     131.761 437.70
+
      
+
      
+## Not run: ------------------------------------
+# fit.2 <- mkinfit(SFO_SFO, FOCUS_2006_D, transform_rates = FALSE)
+# summary(fit.2, data=FALSE) 
+## ---------------------------------------------
+
+initials <- fit$start$value
+names(initials) <- rownames(fit$start)
+transformed <- fit$start_transformed$value
+names(transformed) <- rownames(fit$start_transformed)
+transform_odeparms(initials, SFO_SFO)
      
+
               parent_0 log_k_parent_sink   log_k_parent_m1     log_k_m1_sink 
+       100.750000         -2.302585         -2.301586         -2.300587 
+
      
+
      backtransform_odeparms(transformed, SFO_SFO)
      
+
           parent_0 k_parent_sink   k_parent_m1     k_m1_sink 
+     100.7500        0.1000        0.1001        0.1002 
+
      
+
      
+## Not run: ------------------------------------
+# # The case of formation fractions
+# SFO_SFO.ff <- mkinmod(
+#   parent = list(type = "SFO", to = "m1", sink = TRUE),
+#   m1 = list(type = "SFO"),
+#   use_of_ff = "max")
+# 
+# fit.ff <- mkinfit(SFO_SFO.ff, FOCUS_2006_D)
+# summary(fit.ff, data = FALSE)
+# initials <- c("f_parent_to_m1" = 0.5)
+# transformed <- transform_odeparms(initials, SFO_SFO.ff)
+# backtransform_odeparms(transformed, SFO_SFO.ff)
+# 
+# # And without sink
+# SFO_SFO.ff.2 <- mkinmod(
+#   parent = list(type = "SFO", to = "m1", sink = FALSE),
+#   m1 = list(type = "SFO"),
+#   use_of_ff = "max")
+# 
+# 
+# fit.ff.2 <- mkinfit(SFO_SFO.ff.2, FOCUS_2006_D)
+# summary(fit.ff.2, data = FALSE)
+## ---------------------------------------------
      +
      +
      + + + + +

      Author

      + + Johannes Ranke + + +
      +
      + + +
      + + \ No newline at end of file diff --git a/docs/vignettes/FOCUS_D.html b/docs/vignettes/FOCUS_D.html new file mode 100644 index 00000000..94d4f4a3 --- /dev/null +++ b/docs/vignettes/FOCUS_D.html @@ -0,0 +1,332 @@ + + + + + + + + + + + + + + + +Example evaluation of FOCUS Example Dataset D + + + + + + + + + + + + + + + + + + + + + + + + + + +
      + + + + + + + + + + + + + + + +

      This is just a very simple vignette showing how to fit a degradation model for a parent compound with one transformation product using mkin. After loading the library we look a the data. We have observed concentrations in the column named value at the times specified in column time for the two observed variables named parent and m1.

      +
      library("mkin")
+print(FOCUS_2006_D)
      +
      ##      name time  value
+## 1  parent    0  99.46
+## 2  parent    0 102.04
+## 3  parent    1  93.50
+## 4  parent    1  92.50
+## 5  parent    3  63.23
+## 6  parent    3  68.99
+## 7  parent    7  52.32
+## 8  parent    7  55.13
+## 9  parent   14  27.27
+## 10 parent   14  26.64
+## 11 parent   21  11.50
+## 12 parent   21  11.64
+## 13 parent   35   2.85
+## 14 parent   35   2.91
+## 15 parent   50   0.69
+## 16 parent   50   0.63
+## 17 parent   75   0.05
+## 18 parent   75   0.06
+## 19 parent  100     NA
+## 20 parent  100     NA
+## 21 parent  120     NA
+## 22 parent  120     NA
+## 23     m1    0   0.00
+## 24     m1    0   0.00
+## 25     m1    1   4.84
+## 26     m1    1   5.64
+## 27     m1    3  12.91
+## 28     m1    3  12.96
+## 29     m1    7  22.97
+## 30     m1    7  24.47
+## 31     m1   14  41.69
+## 32     m1   14  33.21
+## 33     m1   21  44.37
+## 34     m1   21  46.44
+## 35     m1   35  41.22
+## 36     m1   35  37.95
+## 37     m1   50  41.19
+## 38     m1   50  40.01
+## 39     m1   75  40.09
+## 40     m1   75  33.85
+## 41     m1  100  31.04
+## 42     m1  100  33.13
+## 43     m1  120  25.15
+## 44     m1  120  33.31
      +

      Next we specify the degradation model: The parent compound degrades with simple first-order kinetics (SFO) to one metabolite named m1, which also degrades with SFO kinetics.

      +

      The call to mkinmod returns a degradation model. The differential equations represented in R code can be found in the character vector $diffs of the mkinmod object. If a C compiler (gcc) is installed and functional, the differential equation model will be compiled from auto-generated C code.

      +
      SFO_SFO <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"))
      +
      ## Successfully compiled differential equation model from auto-generated C code.
      +
      print(SFO_SFO$diffs)
      +
      ##                                                       parent 
+## "d_parent = - k_parent_sink * parent - k_parent_m1 * parent" 
+##                                                           m1 
+##             "d_m1 = + k_parent_m1 * parent - k_m1_sink * m1"
      +

      We do the fitting without progress report (quiet = TRUE).

      +
      fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE)
      +

      A plot of the fit including a residual plot for both observed variables is obtained using the plot method for mkinfit objects.

      +
      plot(fit, show_residuals = TRUE)
      +

      +

      Confidence intervals for the parameter estimates are obtained using the mkinparplot function.

      +
      mkinparplot(fit)
      +

      +

      A comprehensive report of the results is obtained using the summary method for mkinfit objects.

      +
      summary(fit)
      +
      ## mkin version:    0.9.44.9000 
+## R version:       3.3.1 
+## Date of fit:     Sat Sep 10 05:54:26 2016 
+## Date of summary: Sat Sep 10 05:54:26 2016 
+## 
+## Equations:
+## d_parent = - k_parent_sink * parent - k_parent_m1 * parent
+## d_m1 = + k_parent_m1 * parent - k_m1_sink * m1
+## 
+## Model predictions using solution type deSolve 
+## 
+## Fitted with method Port using 153 model solutions performed in 0.65 s
+## 
+## Weighting: none
+## 
+## Starting values for parameters to be optimised:
+##                  value   type
+## parent_0      100.7500  state
+## k_parent_sink   0.1000 deparm
+## k_parent_m1     0.1001 deparm
+## k_m1_sink       0.1002 deparm
+## 
+## Starting values for the transformed parameters actually optimised:
+##                        value lower upper
+## parent_0          100.750000  -Inf   Inf
+## log_k_parent_sink  -2.302585  -Inf   Inf
+## log_k_parent_m1    -2.301586  -Inf   Inf
+## log_k_m1_sink      -2.300587  -Inf   Inf
+## 
+## Fixed parameter values:
+##      value  type
+## m1_0     0 state
+## 
+## Optimised, transformed parameters with symmetric confidence intervals:
+##                   Estimate Std. Error  Lower   Upper
+## parent_0            99.600    1.61400 96.330 102.900
+## log_k_parent_sink   -3.038    0.07826 -3.197  -2.879
+## log_k_parent_m1     -2.980    0.04124 -3.064  -2.897
+## log_k_m1_sink       -5.248    0.13610 -5.523  -4.972
+## 
+## Parameter correlation:
+##                   parent_0 log_k_parent_sink log_k_parent_m1 log_k_m1_sink
+## parent_0           1.00000            0.6075        -0.06625       -0.1701
+## log_k_parent_sink  0.60752            1.0000        -0.08740       -0.6253
+## log_k_parent_m1   -0.06625           -0.0874         1.00000        0.4716
+## log_k_m1_sink     -0.17006           -0.6253         0.47163        1.0000
+## 
+## Residual standard error: 3.211 on 36 degrees of freedom
+## 
+## Backtransformed parameters:
+## Confidence intervals for internally transformed parameters are asymmetric.
+## t-test (unrealistically) based on the assumption of normal distribution
+## for estimators of untransformed parameters.
+##                Estimate t value    Pr(>t)     Lower     Upper
+## parent_0      99.600000  61.720 2.024e-38 96.330000 1.029e+02
+## k_parent_sink  0.047920  12.780 3.050e-15  0.040890 5.616e-02
+## k_parent_m1    0.050780  24.250 3.407e-24  0.046700 5.521e-02
+## k_m1_sink      0.005261   7.349 5.758e-09  0.003992 6.933e-03
+## 
+## Chi2 error levels in percent:
+##          err.min n.optim df
+## All data   6.398       4 15
+## parent     6.827       3  6
+## m1         4.490       1  9
+## 
+## Resulting formation fractions:
+##                 ff
+## parent_sink 0.4855
+## parent_m1   0.5145
+## m1_sink     1.0000
+## 
+## Estimated disappearance times:
+##           DT50   DT90
+## parent   7.023  23.33
+## m1     131.761 437.70
+## 
+## Data:
+##  time variable observed predicted   residual
+##     0   parent    99.46 9.960e+01 -1.385e-01
+##     0   parent   102.04 9.960e+01  2.442e+00
+##     1   parent    93.50 9.024e+01  3.262e+00
+##     1   parent    92.50 9.024e+01  2.262e+00
+##     3   parent    63.23 7.407e+01 -1.084e+01
+##     3   parent    68.99 7.407e+01 -5.083e+00
+##     7   parent    52.32 4.991e+01  2.408e+00
+##     7   parent    55.13 4.991e+01  5.218e+00
+##    14   parent    27.27 2.501e+01  2.257e+00
+##    14   parent    26.64 2.501e+01  1.627e+00
+##    21   parent    11.50 1.253e+01 -1.035e+00
+##    21   parent    11.64 1.253e+01 -8.946e-01
+##    35   parent     2.85 3.148e+00 -2.979e-01
+##    35   parent     2.91 3.148e+00 -2.379e-01
+##    50   parent     0.69 7.162e-01 -2.624e-02
+##    50   parent     0.63 7.162e-01 -8.624e-02
+##    75   parent     0.05 6.074e-02 -1.074e-02
+##    75   parent     0.06 6.074e-02 -7.382e-04
+##   100   parent       NA 5.151e-03         NA
+##   100   parent       NA 5.151e-03         NA
+##   120   parent       NA 7.155e-04         NA
+##   120   parent       NA 7.155e-04         NA
+##     0       m1     0.00 0.000e+00  0.000e+00
+##     0       m1     0.00 0.000e+00  0.000e+00
+##     1       m1     4.84 4.803e+00  3.704e-02
+##     1       m1     5.64 4.803e+00  8.370e-01
+##     3       m1    12.91 1.302e+01 -1.140e-01
+##     3       m1    12.96 1.302e+01 -6.400e-02
+##     7       m1    22.97 2.504e+01 -2.075e+00
+##     7       m1    24.47 2.504e+01 -5.748e-01
+##    14       m1    41.69 3.669e+01  5.000e+00
+##    14       m1    33.21 3.669e+01 -3.480e+00
+##    21       m1    44.37 4.165e+01  2.717e+00
+##    21       m1    46.44 4.165e+01  4.787e+00
+##    35       m1    41.22 4.331e+01 -2.093e+00
+##    35       m1    37.95 4.331e+01 -5.363e+00
+##    50       m1    41.19 4.122e+01 -2.831e-02
+##    50       m1    40.01 4.122e+01 -1.208e+00
+##    75       m1    40.09 3.645e+01  3.643e+00
+##    75       m1    33.85 3.645e+01 -2.597e+00
+##   100       m1    31.04 3.198e+01 -9.416e-01
+##   100       m1    33.13 3.198e+01  1.148e+00
+##   120       m1    25.15 2.879e+01 -3.640e+00
+##   120       m1    33.31 2.879e+01  4.520e+00
      + + + + +
      + + + + + + diff --git a/docs/vignettes/FOCUS_L.html b/docs/vignettes/FOCUS_L.html new file mode 100644 index 00000000..26076d0a --- /dev/null +++ b/docs/vignettes/FOCUS_L.html @@ -0,0 +1,830 @@ + + + + + + + + + + + + + + + +Example evaluation of FOCUS Laboratory Data L1 to L3 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      + + + + + + + + + + + + + + + +
      +
      +
      +
      +
      + +
      + + + + + + + +
      +

      Laboratory Data L1

      +

      The following code defines example dataset L1 from the FOCUS kinetics report, p. 284:

      +
      library("mkin", quietly = TRUE)
+FOCUS_2006_L1 = data.frame(
+  t = rep(c(0, 1, 2, 3, 5, 7, 14, 21, 30), each = 2),
+  parent = c(88.3, 91.4, 85.6, 84.5, 78.9, 77.6, 
+             72.0, 71.9, 50.3, 59.4, 47.0, 45.1,
+             27.7, 27.3, 10.0, 10.4, 2.9, 4.0))
+FOCUS_2006_L1_mkin <- mkin_wide_to_long(FOCUS_2006_L1)
      +

      Here we use the assumptions of simple first order (SFO), the case of declining rate constant over time (FOMC) and the case of two different phases of the kinetics (DFOP). For a more detailed discussion of the models, please see the FOCUS kinetics report.

      +

      Since mkin version 0.9-32 (July 2014), we can use shorthand notation like "SFO" for parent only degradation models. The following two lines fit the model and produce the summary report of the model fit. This covers the numerical analysis given in the FOCUS report.

      +
      m.L1.SFO <- mkinfit("SFO", FOCUS_2006_L1_mkin, quiet = TRUE)
+summary(m.L1.SFO)
      +
      ## mkin version:    0.9.44.9000 
+## R version:       3.3.1 
+## Date of fit:     Sat Sep 10 05:54:26 2016 
+## Date of summary: Sat Sep 10 05:54:26 2016 
+## 
+## Equations:
+## d_parent = - k_parent_sink * parent
+## 
+## Model predictions using solution type analytical 
+## 
+## Fitted with method Port using 37 model solutions performed in 0.087 s
+## 
+## Weighting: none
+## 
+## Starting values for parameters to be optimised:
+##               value   type
+## parent_0      89.85  state
+## k_parent_sink  0.10 deparm
+## 
+## Starting values for the transformed parameters actually optimised:
+##                       value lower upper
+## parent_0          89.850000  -Inf   Inf
+## log_k_parent_sink -2.302585  -Inf   Inf
+## 
+## Fixed parameter values:
+## None
+## 
+## Optimised, transformed parameters with symmetric confidence intervals:
+##                   Estimate Std. Error  Lower  Upper
+## parent_0            92.470    1.36800 89.570 95.370
+## log_k_parent_sink   -2.347    0.04057 -2.433 -2.261
+## 
+## Parameter correlation:
+##                   parent_0 log_k_parent_sink
+## parent_0            1.0000            0.6248
+## log_k_parent_sink   0.6248            1.0000
+## 
+## Residual standard error: 2.948 on 16 degrees of freedom
+## 
+## Backtransformed parameters:
+## Confidence intervals for internally transformed parameters are asymmetric.
+## t-test (unrealistically) based on the assumption of normal distribution
+## for estimators of untransformed parameters.
+##               Estimate t value    Pr(>t)    Lower   Upper
+## parent_0      92.47000   67.58 2.170e-21 89.57000 95.3700
+## k_parent_sink  0.09561   24.65 1.867e-14  0.08773  0.1042
+## 
+## Chi2 error levels in percent:
+##          err.min n.optim df
+## All data   3.424       2  7
+## parent     3.424       2  7
+## 
+## Resulting formation fractions:
+##             ff
+## parent_sink  1
+## 
+## Estimated disappearance times:
+##         DT50  DT90
+## parent 7.249 24.08
+## 
+## Data:
+##  time variable observed predicted residual
+##     0   parent     88.3    92.471  -4.1710
+##     0   parent     91.4    92.471  -1.0710
+##     1   parent     85.6    84.039   1.5610
+##     1   parent     84.5    84.039   0.4610
+##     2   parent     78.9    76.376   2.5241
+##     2   parent     77.6    76.376   1.2241
+##     3   parent     72.0    69.412   2.5884
+##     3   parent     71.9    69.412   2.4884
+##     5   parent     50.3    57.330  -7.0301
+##     5   parent     59.4    57.330   2.0699
+##     7   parent     47.0    47.352  -0.3515
+##     7   parent     45.1    47.352  -2.2515
+##    14   parent     27.7    24.247   3.4528
+##    14   parent     27.3    24.247   3.0528
+##    21   parent     10.0    12.416  -2.4163
+##    21   parent     10.4    12.416  -2.0163
+##    30   parent      2.9     5.251  -2.3513
+##    30   parent      4.0     5.251  -1.2513
      +

      A plot of the fit is obtained with the plot function for mkinfit objects.

      +
      plot(m.L1.SFO, show_errmin = TRUE, main = "FOCUS L1 - SFO")
      +

      +

      The residual plot can be easily obtained by

      +
      mkinresplot(m.L1.SFO, ylab = "Observed", xlab = "Time")
      +

      +

      For comparison, the FOMC model is fitted as well, and the χ2 error level is checked.

      +
      m.L1.FOMC <- mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet=TRUE)
      +
      ## Warning in mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet = TRUE): Optimisation by method Port did not converge.
+## Convergence code is 1
      +
      plot(m.L1.FOMC, show_errmin = TRUE, main = "FOCUS L1 - FOMC")
      +

      +
      summary(m.L1.FOMC, data = FALSE)
      +
      ## mkin version:    0.9.44.9000 
+## R version:       3.3.1 
+## Date of fit:     Sat Sep 10 05:54:27 2016 
+## Date of summary: Sat Sep 10 05:54:27 2016 
+## 
+## 
+## Warning: Optimisation by method Port did not converge.
+## Convergence code is 1 
+## 
+## 
+## Equations:
+## d_parent = - (alpha/beta) * 1/((time/beta) + 1) * parent
+## 
+## Model predictions using solution type analytical 
+## 
+## Fitted with method Port using 188 model solutions performed in 0.444 s
+## 
+## Weighting: none
+## 
+## Starting values for parameters to be optimised:
+##          value   type
+## parent_0 89.85  state
+## alpha     1.00 deparm
+## beta     10.00 deparm
+## 
+## Starting values for the transformed parameters actually optimised:
+##               value lower upper
+## parent_0  89.850000  -Inf   Inf
+## log_alpha  0.000000  -Inf   Inf
+## log_beta   2.302585  -Inf   Inf
+## 
+## Fixed parameter values:
+## None
+## 
+## Optimised, transformed parameters with symmetric confidence intervals:
+##           Estimate Std. Error  Lower Upper
+## parent_0     92.47      1.422  89.44 95.50
+## log_alpha    15.43     15.080 -16.71 47.58
+## log_beta     17.78     15.090 -14.37 49.93
+## 
+## Parameter correlation:
+##           parent_0 log_alpha log_beta
+## parent_0    1.0000    0.1129   0.1112
+## log_alpha   0.1129    1.0000   1.0000
+## log_beta    0.1112    1.0000   1.0000
+## 
+## Residual standard error: 3.045 on 15 degrees of freedom
+## 
+## Backtransformed parameters:
+## Confidence intervals for internally transformed parameters are asymmetric.
+## t-test (unrealistically) based on the assumption of normal distribution
+## for estimators of untransformed parameters.
+##           Estimate t value    Pr(>t)     Lower     Upper
+## parent_0 9.247e+01  65.150 4.044e-20 8.944e+01 9.550e+01
+## alpha    5.044e+06   1.271 1.115e-01 5.510e-08 4.618e+20
+## beta     5.276e+07   1.259 1.137e-01 5.732e-07 4.857e+21
+## 
+## Chi2 error levels in percent:
+##          err.min n.optim df
+## All data   3.619       3  6
+## parent     3.619       3  6
+## 
+## Estimated disappearance times:
+##        DT50  DT90 DT50back
+## parent 7.25 24.08     7.25
      +

      We get a warning that the default optimisation algorithm Port did not converge, which is an indication that the model is overparameterised, i.e. contains too many parameters that are ill-defined as a consequence.

      +

      And in fact, due to the higher number of parameters, and the lower number of degrees of freedom of the fit, the χ2 error level is actually higher for the FOMC model (3.6%) than for the SFO model (3.4%). Additionally, the parameters log_alpha and log_beta internally fitted in the model have excessive confidence intervals, that span more than 25 orders of magnitude (!) when backtransformed to the scale of alpha and beta. Also, the t-test for significant difference from zero does not indicate such a significant difference, with p-values greater than 0.1, and finally, the parameter correlation of log_alpha and log_beta is 1.000, clearly indicating that the model is overparameterised.

      +

      The χ2 error levels reported in Appendix 3 and Appendix 7 to the FOCUS kinetics report are rounded to integer percentages and partly deviate by one percentage point from the results calculated by mkin. The reason for this is not known. However, mkin gives the same χ2 error levels as the kinfit package and the calculation routines of the kinfit package have been extensively compared to the results obtained by the KinGUI software, as documented in the kinfit package vignette. KinGUI was the first widely used standard package in this field. Also, the calculation of χ2 error levels was compared with KinGUII, CAKE and DegKin manager in a project sponsored by the German Umweltbundesamt (Ranke).

      +
      +
      +

      Laboratory Data L2

      +

      The following code defines example dataset L2 from the FOCUS kinetics report, p. 287:

      +
      FOCUS_2006_L2 = data.frame(
+  t = rep(c(0, 1, 3, 7, 14, 28), each = 2),
+  parent = c(96.1, 91.8, 41.4, 38.7,
+             19.3, 22.3, 4.6, 4.6,
+             2.6, 1.2, 0.3, 0.6))
+FOCUS_2006_L2_mkin <- mkin_wide_to_long(FOCUS_2006_L2)
      +
      +

      SFO fit for L2

      +

      Again, the SFO model is fitted and the result is plotted. The residual plot can be obtained simply by adding the argument show_residuals to the plot command.

      +
      m.L2.SFO <- mkinfit("SFO", FOCUS_2006_L2_mkin, quiet=TRUE)
+plot(m.L2.SFO, show_residuals = TRUE, show_errmin = TRUE, 
+     main = "FOCUS L2 - SFO")
      +

      +

      The χ2 error level of 14% suggests that the model does not fit very well. This is also obvious from the plots of the fit, in which we have included the residual plot.

      +

      In the FOCUS kinetics report, it is stated that there is no apparent systematic error observed from the residual plot up to the measured DT90 (approximately at day 5), and there is an underestimation beyond that point.

      +

      We may add that it is difficult to judge the random nature of the residuals just from the three samplings at days 0, 1 and 3. Also, it is not clear a priori why a consistent underestimation after the approximate DT90 should be irrelevant. However, this can be rationalised by the fact that the FOCUS fate models generally only implement SFO kinetics.

      +
      +
      +

      FOMC fit for L2

      +

      For comparison, the FOMC model is fitted as well, and the χ2 error level is checked.

      +
      m.L2.FOMC <- mkinfit("FOMC", FOCUS_2006_L2_mkin, quiet = TRUE)
+plot(m.L2.FOMC, show_residuals = TRUE,
+     main = "FOCUS L2 - FOMC")
      +

      +
      summary(m.L2.FOMC, data = FALSE)
      +
      ## mkin version:    0.9.44.9000 
+## R version:       3.3.1 
+## Date of fit:     Sat Sep 10 05:54:28 2016 
+## Date of summary: Sat Sep 10 05:54:28 2016 
+## 
+## Equations:
+## d_parent = - (alpha/beta) * 1/((time/beta) + 1) * parent
+## 
+## Model predictions using solution type analytical 
+## 
+## Fitted with method Port using 81 model solutions performed in 0.198 s
+## 
+## Weighting: none
+## 
+## Starting values for parameters to be optimised:
+##          value   type
+## parent_0 93.95  state
+## alpha     1.00 deparm
+## beta     10.00 deparm
+## 
+## Starting values for the transformed parameters actually optimised:
+##               value lower upper
+## parent_0  93.950000  -Inf   Inf
+## log_alpha  0.000000  -Inf   Inf
+## log_beta   2.302585  -Inf   Inf
+## 
+## Fixed parameter values:
+## None
+## 
+## Optimised, transformed parameters with symmetric confidence intervals:
+##           Estimate Std. Error   Lower   Upper
+## parent_0   93.7700     1.8560 89.5700 97.9700
+## log_alpha   0.3180     0.1867 -0.1044  0.7405
+## log_beta    0.2102     0.2943 -0.4555  0.8759
+## 
+## Parameter correlation:
+##           parent_0 log_alpha log_beta
+## parent_0   1.00000  -0.09553  -0.1863
+## log_alpha -0.09553   1.00000   0.9757
+## log_beta  -0.18628   0.97567   1.0000
+## 
+## Residual standard error: 2.628 on 9 degrees of freedom
+## 
+## Backtransformed parameters:
+## Confidence intervals for internally transformed parameters are asymmetric.
+## t-test (unrealistically) based on the assumption of normal distribution
+## for estimators of untransformed parameters.
+##          Estimate t value    Pr(>t)   Lower  Upper
+## parent_0   93.770  50.510 1.173e-12 89.5700 97.970
+## alpha       1.374   5.355 2.296e-04  0.9009  2.097
+## beta        1.234   3.398 3.949e-03  0.6341  2.401
+## 
+## Chi2 error levels in percent:
+##          err.min n.optim df
+## All data   6.205       3  3
+## parent     6.205       3  3
+## 
+## Estimated disappearance times:
+##          DT50  DT90 DT50back
+## parent 0.8092 5.356    1.612
      +

      The error level at which the χ2 test passes is much lower in this case. Therefore, the FOMC model provides a better description of the data, as less experimental error has to be assumed in order to explain the data.

      +
      +
      +

      DFOP fit for L2

      +

      Fitting the four parameter DFOP model further reduces the χ2 error level.

      +
      m.L2.DFOP <- mkinfit("DFOP", FOCUS_2006_L2_mkin, quiet = TRUE)
+plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,
+     main = "FOCUS L2 - DFOP")
      +

      +
      summary(m.L2.DFOP, data = FALSE)
      +
      ## mkin version:    0.9.44.9000 
+## R version:       3.3.1 
+## Date of fit:     Sat Sep 10 05:54:29 2016 
+## Date of summary: Sat Sep 10 05:54:29 2016 
+## 
+## Equations:
+## d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
+##            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 *
+##            time))) * parent
+## 
+## Model predictions using solution type analytical 
+## 
+## Fitted with method Port using 336 model solutions performed in 0.825 s
+## 
+## Weighting: none
+## 
+## Starting values for parameters to be optimised:
+##          value   type
+## parent_0 93.95  state
+## k1        0.10 deparm
+## k2        0.01 deparm
+## g         0.50 deparm
+## 
+## Starting values for the transformed parameters actually optimised:
+##              value lower upper
+## parent_0 93.950000  -Inf   Inf
+## log_k1   -2.302585  -Inf   Inf
+## log_k2   -4.605170  -Inf   Inf
+## g_ilr     0.000000  -Inf   Inf
+## 
+## Fixed parameter values:
+## None
+## 
+## Optimised, transformed parameters with symmetric confidence intervals:
+##          Estimate Std. Error Lower Upper
+## parent_0  93.9500         NA    NA    NA
+## log_k1     3.1370         NA    NA    NA
+## log_k2    -1.0880         NA    NA    NA
+## g_ilr     -0.2821         NA    NA    NA
+## 
+## Parameter correlation:
      +
      ## Warning in print.summary.mkinfit(x): Could not estimate covariance matrix; singular system:
      +
      ## Could not estimate covariance matrix; singular system:
+## 
+## Residual standard error: 1.732 on 8 degrees of freedom
+## 
+## Backtransformed parameters:
+## Confidence intervals for internally transformed parameters are asymmetric.
+## t-test (unrealistically) based on the assumption of normal distribution
+## for estimators of untransformed parameters.
+##          Estimate t value Pr(>t) Lower Upper
+## parent_0  93.9500      NA     NA    NA    NA
+## k1        23.0400      NA     NA    NA    NA
+## k2         0.3369      NA     NA    NA    NA
+## g          0.4016      NA     NA    NA    NA
+## 
+## Chi2 error levels in percent:
+##          err.min n.optim df
+## All data    2.53       4  2
+## parent      2.53       4  2
+## 
+## Estimated disappearance times:
+##          DT50  DT90 DT50_k1 DT50_k2
+## parent 0.5335 5.311 0.03009   2.058
      +

      Here, the DFOP model is clearly the best-fit model for dataset L2 based on the chi^2 error level criterion. However, the failure to calculate the covariance matrix indicates that the parameter estimates correlate excessively. Therefore, the FOMC model may be preferred for this dataset.

      +
      +
      +
      +

      Laboratory Data L3

      +

      The following code defines example dataset L3 from the FOCUS kinetics report, p. 290.

      +
      FOCUS_2006_L3 = data.frame(
+  t = c(0, 3, 7, 14, 30, 60, 91, 120),
+  parent = c(97.8, 60, 51, 43, 35, 22, 15, 12))
+FOCUS_2006_L3_mkin <- mkin_wide_to_long(FOCUS_2006_L3)
      +
      +

      Use mmkin to fit multiple models

      +

      As of mkin version 0.9-39 (June 2015), we can fit several models to one or more datasets in one call to the function mmkin. The datasets have to be passed in a list, in this case a named list holding only the L3 dataset prepared above.

      +
      # Only use one core here, not to offend the CRAN checks
+mm.L3 <- mmkin(c("SFO", "FOMC", "DFOP"), cores = 1,
+               list("FOCUS L3" = FOCUS_2006_L3_mkin), quiet = TRUE)
+plot(mm.L3)
      +

      +

      The χ2 error level of 21% as well as the plot suggest that the SFO model does not fit very well. The FOMC model performs better, with an error level at which the χ2 test passes of 7%. Fitting the four parameter DFOP model further reduces the χ2 error level considerably.

      +
      +
      +

      Accessing elements of mmkin objects

      +

      The objects returned by mmkin are arranged like a matrix, with models as a row index and datasets as a column index.

      +

      We can extract the summary and plot for e.g. the DFOP fit, using square brackets for indexing which will result in the use of the summary and plot functions working on mkinfit objects.

      +
      summary(mm.L3[["DFOP", 1]])
      +
      ## mkin version:    0.9.44.9000 
+## R version:       3.3.1 
+## Date of fit:     Sat Sep 10 05:54:30 2016 
+## Date of summary: Sat Sep 10 05:54:30 2016 
+## 
+## Equations:
+## d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
+##            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 *
+##            time))) * parent
+## 
+## Model predictions using solution type analytical 
+## 
+## Fitted with method Port using 137 model solutions performed in 0.319 s
+## 
+## Weighting: none
+## 
+## Starting values for parameters to be optimised:
+##          value   type
+## parent_0 97.80  state
+## k1        0.10 deparm
+## k2        0.01 deparm
+## g         0.50 deparm
+## 
+## Starting values for the transformed parameters actually optimised:
+##              value lower upper
+## parent_0 97.800000  -Inf   Inf
+## log_k1   -2.302585  -Inf   Inf
+## log_k2   -4.605170  -Inf   Inf
+## g_ilr     0.000000  -Inf   Inf
+## 
+## Fixed parameter values:
+## None
+## 
+## Optimised, transformed parameters with symmetric confidence intervals:
+##          Estimate Std. Error   Lower     Upper
+## parent_0  97.7500    1.43800 93.7500 101.70000
+## log_k1    -0.6612    0.13340 -1.0310  -0.29100
+## log_k2    -4.2860    0.05902 -4.4500  -4.12200
+## g_ilr     -0.1229    0.05121 -0.2651   0.01925
+## 
+## Parameter correlation:
+##          parent_0  log_k1   log_k2   g_ilr
+## parent_0  1.00000  0.1640  0.01315  0.4253
+## log_k1    0.16400  1.0000  0.46478 -0.5526
+## log_k2    0.01315  0.4648  1.00000 -0.6631
+## g_ilr     0.42526 -0.5526 -0.66310  1.0000
+## 
+## Residual standard error: 1.439 on 4 degrees of freedom
+## 
+## Backtransformed parameters:
+## Confidence intervals for internally transformed parameters are asymmetric.
+## t-test (unrealistically) based on the assumption of normal distribution
+## for estimators of untransformed parameters.
+##          Estimate t value    Pr(>t)    Lower     Upper
+## parent_0 97.75000  67.970 1.404e-07 93.75000 101.70000
+## k1        0.51620   7.499 8.460e-04  0.35650   0.74750
+## k2        0.01376  16.940 3.557e-05  0.01168   0.01621
+## g         0.45660  25.410 7.121e-06  0.40730   0.50680
+## 
+## Chi2 error levels in percent:
+##          err.min n.optim df
+## All data   2.225       4  4
+## parent     2.225       4  4
+## 
+## Estimated disappearance times:
+##         DT50 DT90 DT50_k1 DT50_k2
+## parent 7.464  123   1.343   50.37
+## 
+## Data:
+##  time variable observed predicted residual
+##     0   parent     97.8     97.75  0.05396
+##     3   parent     60.0     60.45 -0.44933
+##     7   parent     51.0     49.44  1.56338
+##    14   parent     43.0     43.84 -0.83632
+##    30   parent     35.0     35.15 -0.14707
+##    60   parent     22.0     23.26 -1.25919
+##    91   parent     15.0     15.18 -0.18181
+##   120   parent     12.0     10.19  1.81395
      +
      plot(mm.L3[["DFOP", 1]], show_errmin = TRUE)
      +

      +

      Here, a look to the model plot, the confidence intervals of the parameters and the correlation matrix suggest that the parameter estimates are reliable, and the DFOP model can be used as the best-fit model based on the χ2 error level criterion for laboratory data L3.

      +

      This is also an example where the standard t-test for the parameter g_ilr is misleading, as it tests for a significant difference from zero. In this case, zero appears to be the correct value for this parameter, and the confidence interval for the backtransformed parameter g is quite narrow.

      +
      +
      +
      +

      Laboratory Data L4

      +

      The following code defines example dataset L4 from the FOCUS kinetics report, p. 293:

      +
      FOCUS_2006_L4 = data.frame(
+  t = c(0, 3, 7, 14, 30, 60, 91, 120),
+  parent = c(96.6, 96.3, 94.3, 88.8, 74.9, 59.9, 53.5, 49.0))
+FOCUS_2006_L4_mkin <- mkin_wide_to_long(FOCUS_2006_L4)
      +

      Fits of the SFO and FOMC models, plots and summaries are produced below:

      +
      # Only use one core here, not to offend the CRAN checks
+mm.L4 <- mmkin(c("SFO", "FOMC"), cores = 1,
+               list("FOCUS L4" = FOCUS_2006_L4_mkin), 
+               quiet = TRUE)
+plot(mm.L4)
      +

      +

      The χ2 error level of 3.3% as well as the plot suggest that the SFO model fits very well. The error level at which the χ2 test passes is slightly lower for the FOMC model. However, the difference appears negligible.

      +
      summary(mm.L4[["SFO", 1]], data = FALSE)
      +
      ## mkin version:    0.9.44.9000 
+## R version:       3.3.1 
+## Date of fit:     Sat Sep 10 05:54:30 2016 
+## Date of summary: Sat Sep 10 05:54:31 2016 
+## 
+## Equations:
+## d_parent = - k_parent_sink * parent
+## 
+## Model predictions using solution type analytical 
+## 
+## Fitted with method Port using 46 model solutions performed in 0.107 s
+## 
+## Weighting: none
+## 
+## Starting values for parameters to be optimised:
+##               value   type
+## parent_0       96.6  state
+## k_parent_sink   0.1 deparm
+## 
+## Starting values for the transformed parameters actually optimised:
+##                       value lower upper
+## parent_0          96.600000  -Inf   Inf
+## log_k_parent_sink -2.302585  -Inf   Inf
+## 
+## Fixed parameter values:
+## None
+## 
+## Optimised, transformed parameters with symmetric confidence intervals:
+##                   Estimate Std. Error  Lower   Upper
+## parent_0             96.44    1.94900 91.670 101.200
+## log_k_parent_sink    -5.03    0.07999 -5.225  -4.834
+## 
+## Parameter correlation:
+##                   parent_0 log_k_parent_sink
+## parent_0            1.0000            0.5865
+## log_k_parent_sink   0.5865            1.0000
+## 
+## Residual standard error: 3.651 on 6 degrees of freedom
+## 
+## Backtransformed parameters:
+## Confidence intervals for internally transformed parameters are asymmetric.
+## t-test (unrealistically) based on the assumption of normal distribution
+## for estimators of untransformed parameters.
+##                Estimate t value    Pr(>t)     Lower     Upper
+## parent_0      96.440000   49.49 2.283e-09 91.670000 1.012e+02
+## k_parent_sink  0.006541   12.50 8.008e-06  0.005378 7.955e-03
+## 
+## Chi2 error levels in percent:
+##          err.min n.optim df
+## All data   3.287       2  6
+## parent     3.287       2  6
+## 
+## Resulting formation fractions:
+##             ff
+## parent_sink  1
+## 
+## Estimated disappearance times:
+##        DT50 DT90
+## parent  106  352
      +
      summary(mm.L4[["FOMC", 1]], data = FALSE)
      +
      ## mkin version:    0.9.44.9000 
+## R version:       3.3.1 
+## Date of fit:     Sat Sep 10 05:54:31 2016 
+## Date of summary: Sat Sep 10 05:54:31 2016 
+## 
+## Equations:
+## d_parent = - (alpha/beta) * 1/((time/beta) + 1) * parent
+## 
+## Model predictions using solution type analytical 
+## 
+## Fitted with method Port using 66 model solutions performed in 0.149 s
+## 
+## Weighting: none
+## 
+## Starting values for parameters to be optimised:
+##          value   type
+## parent_0  96.6  state
+## alpha      1.0 deparm
+## beta      10.0 deparm
+## 
+## Starting values for the transformed parameters actually optimised:
+##               value lower upper
+## parent_0  96.600000  -Inf   Inf
+## log_alpha  0.000000  -Inf   Inf
+## log_beta   2.302585  -Inf   Inf
+## 
+## Fixed parameter values:
+## None
+## 
+## Optimised, transformed parameters with symmetric confidence intervals:
+##           Estimate Std. Error  Lower    Upper
+## parent_0   99.1400     1.6800 94.820 103.5000
+## log_alpha  -0.3506     0.3725 -1.308   0.6068
+## log_beta    4.1740     0.5635  2.726   5.6230
+## 
+## Parameter correlation:
+##           parent_0 log_alpha log_beta
+## parent_0    1.0000   -0.5365  -0.6083
+## log_alpha  -0.5365    1.0000   0.9913
+## log_beta   -0.6083    0.9913   1.0000
+## 
+## Residual standard error: 2.315 on 5 degrees of freedom
+## 
+## Backtransformed parameters:
+## Confidence intervals for internally transformed parameters are asymmetric.
+## t-test (unrealistically) based on the assumption of normal distribution
+## for estimators of untransformed parameters.
+##          Estimate t value    Pr(>t)   Lower   Upper
+## parent_0  99.1400  59.020 1.322e-08 94.8200 103.500
+## alpha      0.7042   2.685 2.178e-02  0.2703   1.835
+## beta      64.9800   1.775 6.807e-02 15.2600 276.600
+## 
+## Chi2 error levels in percent:
+##          err.min n.optim df
+## All data   2.029       3  5
+## parent     2.029       3  5
+## 
+## Estimated disappearance times:
+##         DT50 DT90 DT50back
+## parent 108.9 1644    494.9
      +
      +
      +

      References

      +

      Ranke, Johannes. Prüfung und Validierung von Modellierungssoftware als Alternative zu ModelMaker 4.0. Umweltbundesamt Projektnummer 27452.

      +
      + + + +
      +
      + +
      + + + + + + diff --git a/docs/vignettes/FOCUS_Z.pdf b/docs/vignettes/FOCUS_Z.pdf new file mode 100644 index 00000000..06acf802 Binary files /dev/null and b/docs/vignettes/FOCUS_Z.pdf differ diff --git a/docs/vignettes/compiled_models.html b/docs/vignettes/compiled_models.html new file mode 100644 index 00000000..97416dc7 --- /dev/null +++ b/docs/vignettes/compiled_models.html @@ -0,0 +1,327 @@ + + + + + + + + + + + + + + + +Performance benefit by using compiled model definitions in mkin + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      + + + + + + + + + + + + + + + +
      +
      +
      +
      +
      + +
      + + + + + + + +
      +

      Benchmark for a model that can also be solved with Eigenvalues

      +

      This evaluation is taken from the example section of mkinfit. When using an mkin version equal to or greater than 0.9-36 and a C compiler (gcc) is available, you will see a message that the model is being compiled from autogenerated C code when defining a model using mkinmod. The mkinmod() function checks for presence of the gcc compiler using

      +
      Sys.which("gcc")
      +
      ##            gcc 
+## "/usr/bin/gcc"
      +

      First, we build a simple degradation model for a parent compound with one metabolite.

      +
      library("mkin")
+SFO_SFO <- mkinmod(
+  parent = mkinsub("SFO", "m1"),
+  m1 = mkinsub("SFO"))
      +
      ## Successfully compiled differential equation model from auto-generated C code.
      +

      We can compare the performance of the Eigenvalue based solution against the compiled version and the R implementation of the differential equations using the microbenchmark package.

      +
      library("microbenchmark")
+library("ggplot2")
+mb.1 <- microbenchmark(
+  "deSolve, not compiled" = mkinfit(SFO_SFO, FOCUS_2006_D, 
+                                    solution_type = "deSolve", 
+                                    use_compiled = FALSE, quiet = TRUE),
+  "Eigenvalue based" = mkinfit(SFO_SFO, FOCUS_2006_D, 
+                               solution_type = "eigen", quiet = TRUE),
+  "deSolve, compiled" = mkinfit(SFO_SFO, FOCUS_2006_D, 
+                                solution_type = "deSolve", quiet = TRUE),
+  times = 3, control = list(warmup = 0))
      +
      ## Warning in microbenchmark(`deSolve, not compiled` = mkinfit(SFO_SFO,
+## FOCUS_2006_D, : Could not measure overhead. Your clock might lack
+## precision.
      +
      smb.1 <- summary(mb.1)
+print(mb.1)
      +
      ## Unit: milliseconds
+##                   expr       min        lq      mean    median        uq
+##  deSolve, not compiled 6438.0110 6444.1220 6460.7839 6450.2330 6472.1703
+##       Eigenvalue based  885.8356  892.8422  908.1567  899.8489  919.3173
+##      deSolve, compiled  717.8236  722.6525  738.3923  727.4815  748.6767
+##        max neval cld
+##  6494.1075     3   c
+##   938.7858     3  b 
+##   769.8719     3 a
      +
      autoplot(mb.1)
      +

      +

      We see that using the compiled model is by a factor of 8.9 faster than using the R version with the default ode solver, and it is even faster than the Eigenvalue based solution implemented in R which does not need iterative solution of the ODEs:

      +
      rownames(smb.1) <- smb.1$expr
+smb.1["median"]/smb.1["deSolve, compiled", "median"]
      +
      ##                         median
+## deSolve, not compiled 8.866525
+## Eigenvalue based      1.236937
+## deSolve, compiled     1.000000
      +
      +
      +

      Benchmark for a model that can not be solved with Eigenvalues

      +

      This evaluation is also taken from the example section of mkinfit.

      +
      FOMC_SFO <- mkinmod(
+  parent = mkinsub("FOMC", "m1"),
+  m1 = mkinsub( "SFO"))
      +
      ## Successfully compiled differential equation model from auto-generated C code.
      +
      mb.2 <- microbenchmark(
+  "deSolve, not compiled" = mkinfit(FOMC_SFO, FOCUS_2006_D, 
+                                    use_compiled = FALSE, quiet = TRUE),
+  "deSolve, compiled" = mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE),
+  times = 3, control = list(warmup = 0))
      +
      ## Warning in microbenchmark(`deSolve, not compiled` = mkinfit(FOMC_SFO,
+## FOCUS_2006_D, : Could not measure overhead. Your clock might lack
+## precision.
      +
      smb.2 <- summary(mb.2)
+print(mb.2)
      +
      ## Unit: seconds
+##                   expr       min        lq      mean   median        uq
+##  deSolve, not compiled 13.446530 13.475187 13.565657 13.50384 13.625221
+##      deSolve, compiled  1.334123  1.348677  1.356064  1.36323  1.367035
+##        max neval cld
+##  13.746597     3   b
+##   1.370839     3  a
      +
      smb.2["median"]/smb.2["deSolve, compiled", "median"]
      +
      ##   median
+## 1     NA
+## 2     NA
      +
      autoplot(mb.2)
      +

      +

      Here we get a performance benefit of a factor of 9.9 using the version of the differential equation model compiled from C code!

      +

      This vignette was built with mkin 0.9.44.9000 on

      +
      ## R version 3.3.1 (2016-06-21)
+## Platform: x86_64-pc-linux-gnu (64-bit)
+## Running under: Debian GNU/Linux 8 (jessie)
      +
      ## CPU model: Intel(R) Core(TM) i7-4710MQ CPU @ 2.50GHz
      +
      + + + +
      +
      + +
      + + + + + + diff --git a/docs/vignettes/mkin.html b/docs/vignettes/mkin.html new file mode 100644 index 00000000..0540ffd0 --- /dev/null +++ b/docs/vignettes/mkin.html @@ -0,0 +1,349 @@ + + + + + + + + + + + + + + + +mkin - Kinetic evaluation of chemical degradation data + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      + + + + + + + + + + + + + + + + + +
      +
      +
      +
      +
      + +
      + + + + + + + +

      Wissenschaftlicher Berater, Kronacher Str. 8, 79639 Grenzach-Wyhlen, Germany
      Privatdozent at the University of Bremen

      +
      +

      Abstract

      +

      In the regulatory evaluation of chemical substances like plant protection products (pesticides), biocides and other chemicals, degradation data play an important role. For the evaluation of pesticide degradation experiments, detailed guidance has been developed, based on nonlinear optimisation. The R add-on package mkin (Ranke 2016) implements fitting some of the models recommended in this guidance from within R and calculates some statistical measures for data series within one or more compartments, for parent and metabolites.

      +
      require(mkin)
+m_SFO_SFO_SFO <- mkinmod(parent = mkinsub("SFO", "M1"),
+                         M1 = mkinsub("SFO", "M2"),
+                         M2 = mkinsub("SFO"), 
+                         use_of_ff = "max", quiet = TRUE)
+
+sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
+
+d_SFO_SFO_SFO <- mkinpredict(m_SFO_SFO_SFO,
+  c(k_parent = 0.03, 
+    f_parent_to_M1 = 0.5, k_M1 = log(2)/100, 
+    f_M1_to_M2 = 0.9, k_M2 = log(2)/50),
+  c(parent = 100, M1 = 0, M2 = 0),
+  sampling_times)
+
+d_SFO_SFO_SFO_err <- add_err(d_SFO_SFO_SFO, function(x) 3, n = 1, seed = 123456789 )
+
+f_SFO_SFO_SFO <- mkinfit(m_SFO_SFO_SFO, d_SFO_SFO_SFO_err[[1]], quiet = TRUE)
+
+plot_sep(f_SFO_SFO_SFO, lpos = c("topright", "bottomright", "bottomright"))
      +

      +
      +
      +

      Background

      +

      Many approaches are possible regarding the evaluation of chemical degradation data.

      +

      The now deprecated kinfit package (Ranke 2015) in R (R Development Core Team 2016) implements the approach recommended in the kinetics report provided by the FOrum for Co-ordination of pesticide fate models and their USe (FOCUS Work Group on Degradation Kinetics 2006; 2014) for simple data series for one parent compound in one compartment.

      +

      The mkin package (Ranke 2016) extends this approach to data series with transformation products, commonly termed metabolites, and to more than one compartment. It is also possible to include back reactions, so equilibrium reactions and equilibrium partitioning can be specified, although this oftentimes leads to an overparameterisation of the model.

      +

      When the first mkin code was published in 2010, the most commonly used tools for fitting more complex kinetic degradation models to experimental data were KinGUI (Schäfer et al. 2007), a MATLAB based tool with a graphical user interface that was specifically tailored to the task and included some output as proposed by the FOCUS Kinetics Workgroup, and ModelMaker, a general purpose compartment based tool providing infrastructure for fitting dynamic simulation models based on differential equations to data.

      +

      The code was first uploaded to the BerliOS platform. When this was taken down, the version control history was imported into the R-Forge site, where the code is still mirrored today (see e.g. the initial commit on 11 May 2010).

      +

      At that time, the R package FME (Flexible Modelling Environment) (Soetaert and Petzoldt 2010) was already available, and provided a good basis for developing a package specifically tailored to the task. The remaining challenge was to make it as easy as possible for the users (including the author of this vignette) to specify the system of differential equations and to include the output requested by the FOCUS guidance, such as the relative standard deviation that has to be assumed for the residuals, such that the \(\chi^2\) goodness-of-fit test as defined by the FOCUS kinetics workgroup would pass using an significance level \(\alpha\) of 0.05.

      +

      Also, mkin introduced using analytical solutions for parent only kinetics for improved optimization speed. Later, Eigenvalue based solutions were introduced to mkin for the case of linear differential equations (i.e. where the FOMC or DFOP models were not used for the parent compound), greatly improving the optimization speed for these cases.

      +

      The possibility to specify back-reactions and a biphasic model (SFORB) for metabolites were present in mkin from the very beginning.

      +
      +

      Derived software tools

      +

      Soon after the publication of mkin, two derived tools were published, namely KinGUII (available from Bayer Crop Science) and CAKE (commissioned to Tessella by Syngenta), which added a graphical user interface (GUI), and added fitting by iteratively reweighted least squares (IRLS) and characterisation of likely parameter distributions by Markov Chain Monte Carlo (MCMC) sampling.

      +

      CAKE focuses on a smooth use experience, sacrificing some flexibility in the model definition, originally allowing only two primary metabolites in parallel. The current version 3.2 of CAKE release in March 2016 uses a basic scheme for up to six metabolites in a flexible arrangement, but does not support back-reactions (non-instantaneous equilibria) or biphasic kinetics for metabolites.

      +

      KinGUI offers an even more flexible widget for specifying complex kinetic models. Back-reactions (non-instanteneous equilibria) were supported early on, but until 2014, only simple first-order models could be specified for transformation products. Starting with KinGUII version 2.1, biphasic modelling of metabolites was also available in KinGUII.

      +

      A further graphical user interface (GUI) that has recently been brought to a decent degree of maturity is the browser based GUI named gmkin. Please see its documentation page and manual for further information.

      +
      +
      +

      Recent developments

      +

      Currently (June 2016), the main features available in mkin which are not present in KinGUII or CAKE, are the speed increase by using compiled code when a compiler is present, parallel model fitting on multicore machines using the mmkin function, and the estimation of parameter confidence intervals based on transformed parameters. These are explained in more detail below.

      +
      +
      +
      +

      Internal parameter transformations

      +

      For rate constants, the log transformation is used, as proposed by Bates and Watts (1988, 77, 149). Approximate intervals are constructed for the transformed rate constants (compare Bates and Watts 1988, 135), i.e. for their logarithms. Confidence intervals for the rate constants are then obtained using the appropriate backtransformation using the exponential function.

      +

      In the first version of mkin allowing for specifying models using formation fractions, a home-made reparameterisation was used in order to ensure that the sum of formation fractions would not exceed unity.

      +

      This method is still used in the current version of KinGUII (v2.1 from April 2014), with a modification that allows for fixing the pathway to sink to zero. CAKE uses penalties in the objective function in order to enforce this constraint.

      +

      In 2012, an alternative reparameterisation of the formation fractions was proposed together with René Lehmann (Ranke and Lehmann 2012), based on isometric logratio transformation (ILR). The aim was to improve the validity of the linear approximation of the objective function during the parameter estimation procedure as well as in the subsequent calculation of parameter confidence intervals.

      +
      +

      Confidence intervals based on transformed parameters

      +

      In the first attempt at providing improved parameter confidence intervals introduced to mkin in 2013, confidence intervals obtained from FME on the transformed parameters were simply all backtransformed one by one to yield asymetric confidence intervals for the backtransformed parameters.

      +

      However, while there is a 1:1 relation between the rate constants in the model and the transformed parameters fitted in the model, the parameters obtained by the isometric logratio transformation are calculated from the set of formation fractions that quantify the paths to each of the compounds formed from a specific parent compound, and no such 1:1 relation exists.

      +

      Therefore, parameter confidence intervals for formation fractions obtained with this method only appear valid for the case of a single transformation product, where only one formation fraction is to be estimated, directly corresponding to one component of the ilr transformed parameter.

      +

      The confidence intervals obtained by backtransformation for the cases where a 1:1 relation between transformed and original parameter exist are considered by the author of this vignette to be more accurate than those obtained using a re-estimation of the Hessian matrix after backtransformation, as implemented in the FME package.

      +
      +
      +

      Parameter t-test based on untransformed parameters

      +

      The standard output of many nonlinear regression software packages includes the results from a test for significant difference from zero for all parameters. Such a test is also recommended to check the validity of rate constants in the FOCUS guidance (FOCUS Work Group on Degradation Kinetics 2014, 96ff).

      +

      It has been argued that the precondition for this test, i.e. normal distribution of the estimator for the parameters, is not fulfilled in the case of nonlinear regression (Ranke and Lehmann 2015). However, this test is commonly used by industry, consultants and national authorities in order to decide on the reliability of parameter estimates, based on the FOCUS guidance mentioned above. Therefore, the results of this one-sided t-test are included in the summary output from mkin.

      +

      As it is not reasonable to test for significant difference of the transformed parameters (e.g. \(log(k)\)) from zero, the t-test is calculated based on the model definition before parameter transformation, i.e. in a similar way as in packages that do not apply such an internal parameter transformation. A note is included in the mkin output, pointing to the fact that the t-test is based on the unjustified assumption of normal distribution of the parameter estimators.

      +
      +
      +
      +

      References

      + +
      +

      Bates, D., and D. Watts. 1988. Nonlinear Regression and Its Applications. Wiley-Interscience.

      +

      FOCUS Work Group on Degradation Kinetics. 2006. Guidance Document on Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in EU Registration. Report of the FOCUS Work Group on Degradation Kinetics. http://focus.jrc.ec.europa.eu/dk.

      +

      ———. 2014. Generic Guidance for Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in EU Registration. 1.1. http://focus.jrc.ec.europa.eu/dk.

      +

      R Development Core Team. 2016. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. http://www.R-project.org.

      +

      Ranke, J. 2015. `kinfit`: Routines for Fitting Simple Kinetic Models to Chemical Degradation Data. http://CRAN.R-project.org/package=kinfit.

      +

      ———. 2016. `mkin`: Kinetic Evaluation of Chemical Degradation Data. http://CRAN.R-project.org/package=mkin.

      +

      Ranke, J., and R. Lehmann. 2012. “Parameter Reliability in Kinetic Evaluation of Environmental Metabolism Data - Assessment and the Influence of Model Specification.” In SETAC World 20-24 May. Berlin.

      +

      ———. 2015. “To T-Test or Not to T-Test, That Is the Question.” In XV Symposium on Pesticide Chemistry 2-4 September 2015. Piacenza. http://chem.uft.uni-bremen.de/ranke/posters/piacenza_2015.pdf.

      +

      Schäfer, D., B. Mikolasch, P. Rainbird, and B. Harvey. 2007. “KinGUI: a New Kinetic Software Tool for Evaluations According to FOCUS Degradation Kinetics.” In Proceedings of the XIII Symposium Pesticide Chemistry, edited by Del Re A. A. M., Capri E., Fragoulis G., and Trevisan M., 916–23. Piacenza.

      +

      Soetaert, Karline, and Thomas Petzoldt. 2010. “Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME.” Journal of Statistical Software 33 (3): 1–28. http://www.jstatsoft.org/v33/i03/.

      +
      +
      + + + +
      +
      + +
      + + + + + + + + diff --git a/inst/web/Extract.mmkin.html b/inst/web/Extract.mmkin.html index e4225ffd..6e32f5a9 100644 --- a/inst/web/Extract.mmkin.html +++ b/inst/web/Extract.mmkin.html @@ -183,7 +183,7 @@ $calls $time user system elapsed - 0.260 0.000 0.259 + 0.252 0.000 0.252 $mkinmod <mkinmod> model generated with @@ -369,7 +369,7 @@ function (P) } return(mC) } -<environment: 0x37c6c70> +<environment: 0x33d3c70> $cost_notrans function (P) @@ -391,7 +391,7 @@ function (P) scaleVar = scaleVar) return(mC) } -<environment: 0x37c6c70> +<environment: 0x33d3c70> $hessian_notrans parent_0 alpha beta @@ -457,7 +457,7 @@ $bparms.state 99.66619 $date -[1] "Sat Sep 10 04:12:07 2016" +[1] "Sat Sep 10 05:19:30 2016" attr(,"class") [1] "mkinfit" "modFit" @@ -544,7 +544,7 @@ $calls $time user system elapsed - 0.068 0.000 0.069 + 0.068 0.000 0.067 $mkinmod <mkinmod> model generated with @@ -731,7 +731,7 @@ function (P) } return(mC) } -<environment: 0x4cb0ed0> +<environment: 0x48bdf40> $cost_notrans function (P) @@ -753,7 +753,7 @@ function (P) scaleVar = scaleVar) return(mC) } -<environment: 0x4cb0ed0> +<environment: 0x48bdf40> $hessian_notrans parent_0 k_parent_sink @@ -816,7 +816,7 @@ $bparms.state 99.17407 $date -[1] "Sat Sep 10 04:12:07 2016" +[1] "Sat Sep 10 05:19:30 2016" attr(,"class") [1] "mkinfit" "modFit" @@ -896,7 +896,7 @@ $calls $time user system elapsed - 0.068 0.000 0.069 + 0.068 0.000 0.067 $mkinmod <mkinmod> model generated with @@ -1083,7 +1083,7 @@ function (P) } return(mC) } -<environment: 0x4cb0ed0> +<environment: 0x48bdf40> $cost_notrans function (P) @@ -1105,7 +1105,7 @@ function (P) scaleVar = scaleVar) return(mC) } -<environment: 0x4cb0ed0> +<environment: 0x48bdf40> $hessian_notrans parent_0 k_parent_sink @@ -1168,7 +1168,7 @@ $bparms.state 99.17407 $date -[1] "Sat Sep 10 04:12:07 2016" +[1] "Sat Sep 10 05:19:30 2016" attr(,"class") [1] "mkinfit" "modFit" diff --git a/inst/web/mccall81_245T.html b/inst/web/mccall81_245T.html index 9972571a..66a8fce2 100644 --- a/inst/web/mccall81_245T.html +++ b/inst/web/mccall81_245T.html @@ -114,8 +114,8 @@
      mkin version: 0.9.44.9000 R version: 3.3.1 -Date of fit: Sat Sep 10 04:12:13 2016 -Date of summary: Sat Sep 10 04:12:13 2016 +Date of fit: Sat Sep 10 05:19:35 2016 +Date of summary: Sat Sep 10 05:19:35 2016 Equations: d_T245 = - k_T245_sink * T245 - k_T245_phenol * T245 @@ -125,7 +125,7 @@ d_anisole = + k_phenol_anisole * phenol - k_anisole_sink * anisole Model predictions using solution type deSolve -Fitted with method Port using 246 model solutions performed in 1.456 s +Fitted with method Port using 246 model solutions performed in 1.434 s Weighting: none diff --git a/inst/web/mkinfit.html b/inst/web/mkinfit.html index 910a43ec..899f2cc7 100644 --- a/inst/web/mkinfit.html +++ b/inst/web/mkinfit.html @@ -325,15 +325,15 @@ summary(fit)
      mkin version: 0.9.44.9000 R version: 3.3.1 -Date of fit: Sat Sep 10 04:12:15 2016 -Date of summary: Sat Sep 10 04:12:15 2016 +Date of fit: Sat Sep 10 05:19:38 2016 +Date of summary: Sat Sep 10 05:19:38 2016 Equations: d_parent = - (alpha/beta) * 1/((time/beta) + 1) * parent Model predictions using solution type analytical -Fitted with method Port using 64 model solutions performed in 0.156 s +Fitted with method Port using 64 model solutions performed in 0.148 s Weighting: none @@ -409,7 +409,7 @@ print(system.time(fit <- mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "eigen", quiet = TRUE)))
      user system elapsed - 1.228 1.216 0.923 + 1.236 1.188 0.911
      coef(fit)
      diff --git a/inst/web/mkinpredict.html b/inst/web/mkinpredict.html index fbf06007..d8edeb23 100644 --- a/inst/web/mkinpredict.html +++ b/inst/web/mkinpredict.html @@ -304,7 +304,7 @@ 201 20 4.978707 27.46227
      user system elapsed - 0.024 0.004 0.005 + 0.004 0.028 0.005
      system.time( print(mkinpredict(SFO_SFO, c(k_parent_m1 = 0.05, k_parent_sink = 0.1, k_m1_sink = 0.01), @@ -315,7 +315,7 @@ 201 20 4.978707 27.46227
      user system elapsed - 0.000 0.020 0.002 + 0.016 0.004 0.003
      system.time( print(mkinpredict(SFO_SFO, c(k_parent_m1 = 0.05, k_parent_sink = 0.1, k_m1_sink = 0.01), @@ -326,7 +326,7 @@ 201 20 4.978707 27.46227
      user system elapsed - 0.036 0.000 0.036 + 0.032 0.000 0.035
      diff --git a/inst/web/summary.mkinfit.html b/inst/web/summary.mkinfit.html index 936350df..53cf5f97 100644 --- a/inst/web/summary.mkinfit.html +++ b/inst/web/summary.mkinfit.html @@ -159,8 +159,8 @@
      mkin version: 0.9.44.9000 R version: 3.3.1 -Date of fit: Sat Sep 10 04:12:23 2016 -Date of summary: Sat Sep 10 04:12:23 2016 +Date of fit: Sat Sep 10 05:19:45 2016 +Date of summary: Sat Sep 10 05:19:45 2016 Equations: d_parent = - k_parent_sink * parent diff --git a/inst/web/transform_odeparms.html b/inst/web/transform_odeparms.html index 43c6cdc2..d016bef5 100644 --- a/inst/web/transform_odeparms.html +++ b/inst/web/transform_odeparms.html @@ -135,8 +135,8 @@ summary(fit, data=FALSE) # See transformed and backtransformed parameters
      mkin version: 0.9.44.9000 R version: 3.3.1 -Date of fit: Sat Sep 10 04:12:24 2016 -Date of summary: Sat Sep 10 04:12:24 2016 +Date of fit: Sat Sep 10 05:19:46 2016 +Date of summary: Sat Sep 10 05:19:46 2016 Equations: d_parent = - k_parent_sink * parent - k_parent_m1 * parent @@ -144,7 +144,7 @@ d_m1 = + k_parent_m1 * parent - k_m1_sink * m1 Model predictions using solution type deSolve -Fitted with method Port using 153 model solutions performed in 0.682 s +Fitted with method Port using 153 model solutions performed in 0.681 s Weighting: none diff --git a/inst/web/vignettes/FOCUS_D.html b/inst/web/vignettes/FOCUS_D.html index d02b1b61..9e19315d 100644 --- a/inst/web/vignettes/FOCUS_D.html +++ b/inst/web/vignettes/FOCUS_D.html @@ -192,8 +192,8 @@ print(FOCUS_2006_D) 
      summary(fit)
      ## mkin version:    0.9.44.9000 
 ## R version:       3.3.1 
-## Date of fit:     Sat Sep 10 04:13:37 2016 
-## Date of summary: Sat Sep 10 04:13:38 2016 
+## Date of fit:     Sat Sep 10 05:20:59 2016 
+## Date of summary: Sat Sep 10 05:20:59 2016 
 ## 
 ## Equations:
 ## d_parent = - k_parent_sink * parent - k_parent_m1 * parent
@@ -201,7 +201,7 @@ print(FOCUS_2006_D)
      ## ## Model predictions using solution type deSolve ## -## Fitted with method Port using 153 model solutions performed in 0.643 s +## Fitted with method Port using 153 model solutions performed in 0.641 s ## ## Weighting: none ## diff --git a/inst/web/vignettes/FOCUS_L.html b/inst/web/vignettes/FOCUS_L.html index 10d1eb01..8223a3f4 100644 --- a/inst/web/vignettes/FOCUS_L.html +++ b/inst/web/vignettes/FOCUS_L.html @@ -236,15 +236,15 @@ FOCUS_2006_L1_mkin <- mkin_wide_to_long(FOCUS_2006_L1) summary(m.L1.SFO) 
      ## mkin version:    0.9.44.9000 
 ## R version:       3.3.1 
-## Date of fit:     Sat Sep 10 04:13:38 2016 
-## Date of summary: Sat Sep 10 04:13:38 2016 
+## Date of fit:     Sat Sep 10 05:20:59 2016 
+## Date of summary: Sat Sep 10 05:20:59 2016 
 ## 
 ## Equations:
 ## d_parent = - k_parent_sink * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 37 model solutions performed in 0.086 s
+## Fitted with method Port using 37 model solutions performed in 0.09 s
 ## 
 ## Weighting: none
 ## 
@@ -329,8 +329,8 @@ summary(m.L1.SFO)
      summary(m.L1.FOMC, data = FALSE)
      ## mkin version:    0.9.44.9000 
 ## R version:       3.3.1 
-## Date of fit:     Sat Sep 10 04:13:39 2016 
-## Date of summary: Sat Sep 10 04:13:39 2016 
+## Date of fit:     Sat Sep 10 05:21:00 2016 
+## Date of summary: Sat Sep 10 05:21:00 2016 
 ## 
 ## 
 ## Warning: Optimisation by method Port did not converge.
@@ -342,7 +342,7 @@ summary(m.L1.SFO)
      ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 188 model solutions performed in 0.443 s +## Fitted with method Port using 188 model solutions performed in 0.441 s ## ## Weighting: none ## @@ -426,8 +426,8 @@ plot(m.L2.FOMC, show_residuals = TRUE, 
      summary(m.L2.FOMC, data = FALSE)
      ## mkin version:    0.9.44.9000 
 ## R version:       3.3.1 
-## Date of fit:     Sat Sep 10 04:13:40 2016 
-## Date of summary: Sat Sep 10 04:13:40 2016 
+## Date of fit:     Sat Sep 10 05:21:01 2016 
+## Date of summary: Sat Sep 10 05:21:01 2016 
 ## 
 ## Equations:
 ## d_parent = - (alpha/beta) * 1/((time/beta) + 1) * parent
@@ -496,8 +496,8 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,
 
      summary(m.L2.DFOP, data = FALSE)
      
 
      ## mkin version:    0.9.44.9000 
 ## R version:       3.3.1 
-## Date of fit:     Sat Sep 10 04:13:41 2016 
-## Date of summary: Sat Sep 10 04:13:41 2016 
+## Date of fit:     Sat Sep 10 05:21:02 2016 
+## Date of summary: Sat Sep 10 05:21:02 2016 
 ## 
 ## Equations:
 ## d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -506,7 +506,7 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 336 model solutions performed in 0.797 s
+## Fitted with method Port using 336 model solutions performed in 0.899 s
 ## 
 ## Weighting: none
 ## 
@@ -585,8 +585,8 @@ plot(mm.L3)
      
 
      summary(mm.L3[["DFOP", 1]])
      
 
      ## mkin version:    0.9.44.9000 
 ## R version:       3.3.1 
-## Date of fit:     Sat Sep 10 04:13:42 2016 
-## Date of summary: Sat Sep 10 04:13:42 2016 
+## Date of fit:     Sat Sep 10 05:21:03 2016 
+## Date of summary: Sat Sep 10 05:21:03 2016 
 ## 
 ## Equations:
 ## d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -595,7 +595,7 @@ plot(mm.L3)
      
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 137 model solutions performed in 0.326 s
+## Fitted with method Port using 137 model solutions performed in 0.403 s
 ## 
 ## Weighting: none
 ## 
@@ -685,15 +685,15 @@ plot(mm.L4)
      summary(mm.L4[["SFO", 1]], data = FALSE)
      ## mkin version:    0.9.44.9000 
 ## R version:       3.3.1 
-## Date of fit:     Sat Sep 10 04:13:42 2016 
-## Date of summary: Sat Sep 10 04:13:42 2016 
+## Date of fit:     Sat Sep 10 05:21:04 2016 
+## Date of summary: Sat Sep 10 05:21:04 2016 
 ## 
 ## Equations:
 ## d_parent = - k_parent_sink * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 46 model solutions performed in 0.106 s
+## Fitted with method Port using 46 model solutions performed in 0.109 s
 ## 
 ## Weighting: none
 ## 
@@ -745,15 +745,15 @@ plot(mm.L4)
      summary(mm.L4[["FOMC", 1]], data = FALSE)
      ## mkin version:    0.9.44.9000 
 ## R version:       3.3.1 
-## Date of fit:     Sat Sep 10 04:13:42 2016 
-## Date of summary: Sat Sep 10 04:13:43 2016 
+## Date of fit:     Sat Sep 10 05:21:04 2016 
+## Date of summary: Sat Sep 10 05:21:04 2016 
 ## 
 ## Equations:
 ## d_parent = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 66 model solutions performed in 0.15 s
+## Fitted with method Port using 66 model solutions performed in 0.152 s
 ## 
 ## Weighting: none
 ## 
diff --git a/inst/web/vignettes/FOCUS_Z.pdf b/inst/web/vignettes/FOCUS_Z.pdf
index 27b9951d..85e50e65 100644
Binary files a/inst/web/vignettes/FOCUS_Z.pdf and b/inst/web/vignettes/FOCUS_Z.pdf differ
diff --git a/inst/web/vignettes/compiled_models.html b/inst/web/vignettes/compiled_models.html
index 004a808c..212b1abb 100644
--- a/inst/web/vignettes/compiled_models.html
+++ b/inst/web/vignettes/compiled_models.html
@@ -251,21 +251,21 @@ mb.1 <- microbenchmark(
 print(mb.1)
      ## Unit: milliseconds
 ##                   expr       min        lq      mean    median        uq
-##  deSolve, not compiled 6507.8296 6549.5160 6597.4319 6591.2024 6642.2330
-##       Eigenvalue based  890.5249  917.6589  928.4907  944.7928  947.4735
-##      deSolve, compiled  735.4908  742.0363  749.3996  748.5817  756.3540
+##  deSolve, not compiled 6410.2240 6437.0229 6461.3866 6463.8218 6486.9680
+##       Eigenvalue based  887.5697  915.3026  929.6279  943.0355  950.6570
+##      deSolve, compiled  737.4060  745.6645  749.1956  753.9229  755.0903
 ##        max neval cld
-##  6693.2636     3   c
-##   950.1543     3  b 
-##   764.1264     3 a
      +## 6510.1142 3 c +## 958.2786 3 b +## 756.2578 3 a 
      autoplot(mb.1)
      -

      -

      We see that using the compiled model is by a factor of 8.8 faster than using the R version with the default ode solver, and it is even faster than the Eigenvalue based solution implemented in R which does not need iterative solution of the ODEs:

      +

      +

      We see that using the compiled model is by a factor of 8.6 faster than using the R version with the default ode solver, and it is even faster than the Eigenvalue based solution implemented in R which does not need iterative solution of the ODEs:

      rownames(smb.1) <- smb.1$expr
 smb.1["median"]/smb.1["deSolve, compiled", "median"]
      ##                         median
-## deSolve, not compiled 8.804921
-## Eigenvalue based      1.262110
+## deSolve, not compiled 8.573584
+## Eigenvalue based      1.250838
 ## deSolve, compiled     1.000000
      @@ -286,18 +286,18 @@ smb.1["median"]/smb.1["deSolve, compiled", "median" 
      smb.2 <- summary(mb.2)
 print(mb.2)
      ## Unit: seconds
-##                   expr       min       lq      mean    median        uq
-##  deSolve, not compiled 13.741831 13.74759 13.815509 13.753350 13.852348
-##      deSolve, compiled  1.358402  1.35862  1.368666  1.358838  1.373798
+##                   expr       min        lq      mean    median       uq
+##  deSolve, not compiled 13.370040 13.424534 13.501075 13.479027 13.56659
+##      deSolve, compiled  1.336599  1.336707  1.339399  1.336815  1.34080
 ##        max neval cld
-##  13.951345     3   b
-##   1.388759     3  a
      +## 13.654158 3 b +## 1.344784 3 a 
      smb.2["median"]/smb.2["deSolve, compiled", "median"]
      ##   median
 ## 1     NA
 ## 2     NA
      autoplot(mb.2)
      -

      +

      Here we get a performance benefit of a factor of 10.1 using the version of the differential equation model compiled from C code!

      This vignette was built with mkin 0.9.44.9000 on

      ## R version 3.3.1 (2016-06-21)
diff --git a/vignettes/FOCUS_D.html b/vignettes/FOCUS_D.html
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-Example evaluation of FOCUS Example Dataset D
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      This is just a very simple vignette showing how to fit a degradation model for a parent compound with one transformation product using mkin. After loading the library we look a the data. We have observed concentrations in the column named value at the times specified in column time for the two observed variables named parent and m1.
      
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      library("mkin")
-print(FOCUS_2006_D)
      
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      ##      name time  value
-## 1  parent    0  99.46
-## 2  parent    0 102.04
-## 3  parent    1  93.50
-## 4  parent    1  92.50
-## 5  parent    3  63.23
-## 6  parent    3  68.99
-## 7  parent    7  52.32
-## 8  parent    7  55.13
-## 9  parent   14  27.27
-## 10 parent   14  26.64
-## 11 parent   21  11.50
-## 12 parent   21  11.64
-## 13 parent   35   2.85
-## 14 parent   35   2.91
-## 15 parent   50   0.69
-## 16 parent   50   0.63
-## 17 parent   75   0.05
-## 18 parent   75   0.06
-## 19 parent  100     NA
-## 20 parent  100     NA
-## 21 parent  120     NA
-## 22 parent  120     NA
-## 23     m1    0   0.00
-## 24     m1    0   0.00
-## 25     m1    1   4.84
-## 26     m1    1   5.64
-## 27     m1    3  12.91
-## 28     m1    3  12.96
-## 29     m1    7  22.97
-## 30     m1    7  24.47
-## 31     m1   14  41.69
-## 32     m1   14  33.21
-## 33     m1   21  44.37
-## 34     m1   21  46.44
-## 35     m1   35  41.22
-## 36     m1   35  37.95
-## 37     m1   50  41.19
-## 38     m1   50  40.01
-## 39     m1   75  40.09
-## 40     m1   75  33.85
-## 41     m1  100  31.04
-## 42     m1  100  33.13
-## 43     m1  120  25.15
-## 44     m1  120  33.31
      
-
      Next we specify the degradation model: The parent compound degrades with simple first-order kinetics (SFO) to one metabolite named m1, which also degrades with SFO kinetics.
      
-
      The call to mkinmod returns a degradation model. The differential equations represented in R code can be found in the character vector $diffs of the mkinmod object. If a C compiler (gcc) is installed and functional, the differential equation model will be compiled from auto-generated C code.
      
-
      SFO_SFO <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"))
      
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      ## Successfully compiled differential equation model from auto-generated C code.
      
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      print(SFO_SFO$diffs)
      
-
      ##                                                       parent 
-## "d_parent = - k_parent_sink * parent - k_parent_m1 * parent" 
-##                                                           m1 
-##             "d_m1 = + k_parent_m1 * parent - k_m1_sink * m1"
      
-
      We do the fitting without progress report (quiet = TRUE).
      
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      fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE)
      
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      A plot of the fit including a residual plot for both observed variables is obtained using the plot method for mkinfit objects.
      
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      plot(fit, show_residuals = TRUE)
      
-
      
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      Confidence intervals for the parameter estimates are obtained using the mkinparplot function.
      
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      mkinparplot(fit)
      
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      A comprehensive report of the results is obtained using the summary method for mkinfit objects.
      
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      summary(fit)
      
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      ## mkin version:    0.9.44.9000 
-## R version:       3.3.1 
-## Date of fit:     Sat Sep 10 04:13:37 2016 
-## Date of summary: Sat Sep 10 04:13:38 2016 
-## 
-## Equations:
-## d_parent = - k_parent_sink * parent - k_parent_m1 * parent
-## d_m1 = + k_parent_m1 * parent - k_m1_sink * m1
-## 
-## Model predictions using solution type deSolve 
-## 
-## Fitted with method Port using 153 model solutions performed in 0.643 s
-## 
-## Weighting: none
-## 
-## Starting values for parameters to be optimised:
-##                  value   type
-## parent_0      100.7500  state
-## k_parent_sink   0.1000 deparm
-## k_parent_m1     0.1001 deparm
-## k_m1_sink       0.1002 deparm
-## 
-## Starting values for the transformed parameters actually optimised:
-##                        value lower upper
-## parent_0          100.750000  -Inf   Inf
-## log_k_parent_sink  -2.302585  -Inf   Inf
-## log_k_parent_m1    -2.301586  -Inf   Inf
-## log_k_m1_sink      -2.300587  -Inf   Inf
-## 
-## Fixed parameter values:
-##      value  type
-## m1_0     0 state
-## 
-## Optimised, transformed parameters with symmetric confidence intervals:
-##                   Estimate Std. Error  Lower   Upper
-## parent_0            99.600    1.61400 96.330 102.900
-## log_k_parent_sink   -3.038    0.07826 -3.197  -2.879
-## log_k_parent_m1     -2.980    0.04124 -3.064  -2.897
-## log_k_m1_sink       -5.248    0.13610 -5.523  -4.972
-## 
-## Parameter correlation:
-##                   parent_0 log_k_parent_sink log_k_parent_m1 log_k_m1_sink
-## parent_0           1.00000            0.6075        -0.06625       -0.1701
-## log_k_parent_sink  0.60752            1.0000        -0.08740       -0.6253
-## log_k_parent_m1   -0.06625           -0.0874         1.00000        0.4716
-## log_k_m1_sink     -0.17006           -0.6253         0.47163        1.0000
-## 
-## Residual standard error: 3.211 on 36 degrees of freedom
-## 
-## Backtransformed parameters:
-## Confidence intervals for internally transformed parameters are asymmetric.
-## t-test (unrealistically) based on the assumption of normal distribution
-## for estimators of untransformed parameters.
-##                Estimate t value    Pr(>t)     Lower     Upper
-## parent_0      99.600000  61.720 2.024e-38 96.330000 1.029e+02
-## k_parent_sink  0.047920  12.780 3.050e-15  0.040890 5.616e-02
-## k_parent_m1    0.050780  24.250 3.407e-24  0.046700 5.521e-02
-## k_m1_sink      0.005261   7.349 5.758e-09  0.003992 6.933e-03
-## 
-## Chi2 error levels in percent:
-##          err.min n.optim df
-## All data   6.398       4 15
-## parent     6.827       3  6
-## m1         4.490       1  9
-## 
-## Resulting formation fractions:
-##                 ff
-## parent_sink 0.4855
-## parent_m1   0.5145
-## m1_sink     1.0000
-## 
-## Estimated disappearance times:
-##           DT50   DT90
-## parent   7.023  23.33
-## m1     131.761 437.70
-## 
-## Data:
-##  time variable observed predicted   residual
-##     0   parent    99.46 9.960e+01 -1.385e-01
-##     0   parent   102.04 9.960e+01  2.442e+00
-##     1   parent    93.50 9.024e+01  3.262e+00
-##     1   parent    92.50 9.024e+01  2.262e+00
-##     3   parent    63.23 7.407e+01 -1.084e+01
-##     3   parent    68.99 7.407e+01 -5.083e+00
-##     7   parent    52.32 4.991e+01  2.408e+00
-##     7   parent    55.13 4.991e+01  5.218e+00
-##    14   parent    27.27 2.501e+01  2.257e+00
-##    14   parent    26.64 2.501e+01  1.627e+00
-##    21   parent    11.50 1.253e+01 -1.035e+00
-##    21   parent    11.64 1.253e+01 -8.946e-01
-##    35   parent     2.85 3.148e+00 -2.979e-01
-##    35   parent     2.91 3.148e+00 -2.379e-01
-##    50   parent     0.69 7.162e-01 -2.624e-02
-##    50   parent     0.63 7.162e-01 -8.624e-02
-##    75   parent     0.05 6.074e-02 -1.074e-02
-##    75   parent     0.06 6.074e-02 -7.382e-04
-##   100   parent       NA 5.151e-03         NA
-##   100   parent       NA 5.151e-03         NA
-##   120   parent       NA 7.155e-04         NA
-##   120   parent       NA 7.155e-04         NA
-##     0       m1     0.00 0.000e+00  0.000e+00
-##     0       m1     0.00 0.000e+00  0.000e+00
-##     1       m1     4.84 4.803e+00  3.704e-02
-##     1       m1     5.64 4.803e+00  8.370e-01
-##     3       m1    12.91 1.302e+01 -1.140e-01
-##     3       m1    12.96 1.302e+01 -6.400e-02
-##     7       m1    22.97 2.504e+01 -2.075e+00
-##     7       m1    24.47 2.504e+01 -5.748e-01
-##    14       m1    41.69 3.669e+01  5.000e+00
-##    14       m1    33.21 3.669e+01 -3.480e+00
-##    21       m1    44.37 4.165e+01  2.717e+00
-##    21       m1    46.44 4.165e+01  4.787e+00
-##    35       m1    41.22 4.331e+01 -2.093e+00
-##    35       m1    37.95 4.331e+01 -5.363e+00
-##    50       m1    41.19 4.122e+01 -2.831e-02
-##    50       m1    40.01 4.122e+01 -1.208e+00
-##    75       m1    40.09 3.645e+01  3.643e+00
-##    75       m1    33.85 3.645e+01 -2.597e+00
-##   100       m1    31.04 3.198e+01 -9.416e-01
-##   100       m1    33.13 3.198e+01  1.148e+00
-##   120       m1    25.15 2.879e+01 -3.640e+00
-##   120       m1    33.31 2.879e+01  4.520e+00
      
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      Laboratory Data L1
      
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      The following code defines example dataset L1 from the FOCUS kinetics report, p. 284:
      
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      library("mkin", quietly = TRUE)
-FOCUS_2006_L1 = data.frame(
-  t = rep(c(0, 1, 2, 3, 5, 7, 14, 21, 30), each = 2),
-  parent = c(88.3, 91.4, 85.6, 84.5, 78.9, 77.6, 
-             72.0, 71.9, 50.3, 59.4, 47.0, 45.1,
-             27.7, 27.3, 10.0, 10.4, 2.9, 4.0))
-FOCUS_2006_L1_mkin <- mkin_wide_to_long(FOCUS_2006_L1)
      
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      Here we use the assumptions of simple first order (SFO), the case of declining rate constant over time (FOMC) and the case of two different phases of the kinetics (DFOP). For a more detailed discussion of the models, please see the FOCUS kinetics report.
      
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      Since mkin version 0.9-32 (July 2014), we can use shorthand notation like "SFO" for parent only degradation models. The following two lines fit the model and produce the summary report of the model fit. This covers the numerical analysis given in the FOCUS report.
      
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      m.L1.SFO <- mkinfit("SFO", FOCUS_2006_L1_mkin, quiet = TRUE)
-summary(m.L1.SFO)
      
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      ## mkin version:    0.9.44.9000 
-## R version:       3.3.1 
-## Date of fit:     Sat Sep 10 04:13:38 2016 
-## Date of summary: Sat Sep 10 04:13:38 2016 
-## 
-## Equations:
-## d_parent = - k_parent_sink * parent
-## 
-## Model predictions using solution type analytical 
-## 
-## Fitted with method Port using 37 model solutions performed in 0.086 s
-## 
-## Weighting: none
-## 
-## Starting values for parameters to be optimised:
-##               value   type
-## parent_0      89.85  state
-## k_parent_sink  0.10 deparm
-## 
-## Starting values for the transformed parameters actually optimised:
-##                       value lower upper
-## parent_0          89.850000  -Inf   Inf
-## log_k_parent_sink -2.302585  -Inf   Inf
-## 
-## Fixed parameter values:
-## None
-## 
-## Optimised, transformed parameters with symmetric confidence intervals:
-##                   Estimate Std. Error  Lower  Upper
-## parent_0            92.470    1.36800 89.570 95.370
-## log_k_parent_sink   -2.347    0.04057 -2.433 -2.261
-## 
-## Parameter correlation:
-##                   parent_0 log_k_parent_sink
-## parent_0            1.0000            0.6248
-## log_k_parent_sink   0.6248            1.0000
-## 
-## Residual standard error: 2.948 on 16 degrees of freedom
-## 
-## Backtransformed parameters:
-## Confidence intervals for internally transformed parameters are asymmetric.
-## t-test (unrealistically) based on the assumption of normal distribution
-## for estimators of untransformed parameters.
-##               Estimate t value    Pr(>t)    Lower   Upper
-## parent_0      92.47000   67.58 2.170e-21 89.57000 95.3700
-## k_parent_sink  0.09561   24.65 1.867e-14  0.08773  0.1042
-## 
-## Chi2 error levels in percent:
-##          err.min n.optim df
-## All data   3.424       2  7
-## parent     3.424       2  7
-## 
-## Resulting formation fractions:
-##             ff
-## parent_sink  1
-## 
-## Estimated disappearance times:
-##         DT50  DT90
-## parent 7.249 24.08
-## 
-## Data:
-##  time variable observed predicted residual
-##     0   parent     88.3    92.471  -4.1710
-##     0   parent     91.4    92.471  -1.0710
-##     1   parent     85.6    84.039   1.5610
-##     1   parent     84.5    84.039   0.4610
-##     2   parent     78.9    76.376   2.5241
-##     2   parent     77.6    76.376   1.2241
-##     3   parent     72.0    69.412   2.5884
-##     3   parent     71.9    69.412   2.4884
-##     5   parent     50.3    57.330  -7.0301
-##     5   parent     59.4    57.330   2.0699
-##     7   parent     47.0    47.352  -0.3515
-##     7   parent     45.1    47.352  -2.2515
-##    14   parent     27.7    24.247   3.4528
-##    14   parent     27.3    24.247   3.0528
-##    21   parent     10.0    12.416  -2.4163
-##    21   parent     10.4    12.416  -2.0163
-##    30   parent      2.9     5.251  -2.3513
-##    30   parent      4.0     5.251  -1.2513
      
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      plot(m.L1.SFO, show_errmin = TRUE, main = "FOCUS L1 - SFO")
      
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      m.L1.FOMC <- mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet=TRUE)
      
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      ## Warning in mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet = TRUE): Optimisation by method Port did not converge.
-## Convergence code is 1
      
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      plot(m.L1.FOMC, show_errmin = TRUE, main = "FOCUS L1 - FOMC")
      
-
      
-
      summary(m.L1.FOMC, data = FALSE)
      
-
      ## mkin version:    0.9.44.9000 
-## R version:       3.3.1 
-## Date of fit:     Sat Sep 10 04:13:39 2016 
-## Date of summary: Sat Sep 10 04:13:39 2016 
-## 
-## 
-## Warning: Optimisation by method Port did not converge.
-## Convergence code is 1 
-## 
-## 
-## Equations:
-## d_parent = - (alpha/beta) * 1/((time/beta) + 1) * parent
-## 
-## Model predictions using solution type analytical 
-## 
-## Fitted with method Port using 188 model solutions performed in 0.443 s
-## 
-## Weighting: none
-## 
-## Starting values for parameters to be optimised:
-##          value   type
-## parent_0 89.85  state
-## alpha     1.00 deparm
-## beta     10.00 deparm
-## 
-## Starting values for the transformed parameters actually optimised:
-##               value lower upper
-## parent_0  89.850000  -Inf   Inf
-## log_alpha  0.000000  -Inf   Inf
-## log_beta   2.302585  -Inf   Inf
-## 
-## Fixed parameter values:
-## None
-## 
-## Optimised, transformed parameters with symmetric confidence intervals:
-##           Estimate Std. Error  Lower Upper
-## parent_0     92.47      1.422  89.44 95.50
-## log_alpha    15.43     15.080 -16.71 47.58
-## log_beta     17.78     15.090 -14.37 49.93
-## 
-## Parameter correlation:
-##           parent_0 log_alpha log_beta
-## parent_0    1.0000    0.1129   0.1112
-## log_alpha   0.1129    1.0000   1.0000
-## log_beta    0.1112    1.0000   1.0000
-## 
-## Residual standard error: 3.045 on 15 degrees of freedom
-## 
-## Backtransformed parameters:
-## Confidence intervals for internally transformed parameters are asymmetric.
-## t-test (unrealistically) based on the assumption of normal distribution
-## for estimators of untransformed parameters.
-##           Estimate t value    Pr(>t)     Lower     Upper
-## parent_0 9.247e+01  65.150 4.044e-20 8.944e+01 9.550e+01
-## alpha    5.044e+06   1.271 1.115e-01 5.510e-08 4.618e+20
-## beta     5.276e+07   1.259 1.137e-01 5.732e-07 4.857e+21
-## 
-## Chi2 error levels in percent:
-##          err.min n.optim df
-## All data   3.619       3  6
-## parent     3.619       3  6
-## 
-## Estimated disappearance times:
-##        DT50  DT90 DT50back
-## parent 7.25 24.08     7.25
      
-
      We get a warning that the default optimisation algorithm Port did not converge, which is an indication that the model is overparameterised, i.e. contains too many parameters that are ill-defined as a consequence.
      
-
      And in fact, due to the higher number of parameters, and the lower number of degrees of freedom of the fit, the χ2 error level is actually higher for the FOMC model (3.6%) than for the SFO model (3.4%). Additionally, the parameters log_alpha and log_beta internally fitted in the model have excessive confidence intervals, that span more than 25 orders of magnitude (!) when backtransformed to the scale of alpha and beta. Also, the t-test for significant difference from zero does not indicate such a significant difference, with p-values greater than 0.1, and finally, the parameter correlation of log_alpha and log_beta is 1.000, clearly indicating that the model is overparameterised.
      
-
      The χ2 error levels reported in Appendix 3 and Appendix 7 to the FOCUS kinetics report are rounded to integer percentages and partly deviate by one percentage point from the results calculated by mkin. The reason for this is not known. However, mkin gives the same χ2 error levels as the kinfit package and the calculation routines of the kinfit package have been extensively compared to the results obtained by the KinGUI software, as documented in the kinfit package vignette. KinGUI was the first widely used standard package in this field. Also, the calculation of χ2 error levels was compared with KinGUII, CAKE and DegKin manager in a project sponsored by the German Umweltbundesamt (Ranke).
      
-
      
-
      
-
      Laboratory Data L2
      
-
      The following code defines example dataset L2 from the FOCUS kinetics report, p. 287:
      
-
      FOCUS_2006_L2 = data.frame(
-  t = rep(c(0, 1, 3, 7, 14, 28), each = 2),
-  parent = c(96.1, 91.8, 41.4, 38.7,
-             19.3, 22.3, 4.6, 4.6,
-             2.6, 1.2, 0.3, 0.6))
-FOCUS_2006_L2_mkin <- mkin_wide_to_long(FOCUS_2006_L2)
      
-
      
-
      SFO fit for L2
      
-
      Again, the SFO model is fitted and the result is plotted. The residual plot can be obtained simply by adding the argument show_residuals to the plot command.
      
-
      m.L2.SFO <- mkinfit("SFO", FOCUS_2006_L2_mkin, quiet=TRUE)
-plot(m.L2.SFO, show_residuals = TRUE, show_errmin = TRUE, 
-     main = "FOCUS L2 - SFO")
      
-
      
-
      The χ2 error level of 14% suggests that the model does not fit very well. This is also obvious from the plots of the fit, in which we have included the residual plot.
      
-
      In the FOCUS kinetics report, it is stated that there is no apparent systematic error observed from the residual plot up to the measured DT90 (approximately at day 5), and there is an underestimation beyond that point.
      
-
      We may add that it is difficult to judge the random nature of the residuals just from the three samplings at days 0, 1 and 3. Also, it is not clear a priori why a consistent underestimation after the approximate DT90 should be irrelevant. However, this can be rationalised by the fact that the FOCUS fate models generally only implement SFO kinetics.
      
-
      
-
      
-
      FOMC fit for L2
      
-
      For comparison, the FOMC model is fitted as well, and the χ2 error level is checked.
      
-
      m.L2.FOMC <- mkinfit("FOMC", FOCUS_2006_L2_mkin, quiet = TRUE)
-plot(m.L2.FOMC, show_residuals = TRUE,
-     main = "FOCUS L2 - FOMC")
      
-
      
-
      summary(m.L2.FOMC, data = FALSE)
      
-
      ## mkin version:    0.9.44.9000 
-## R version:       3.3.1 
-## Date of fit:     Sat Sep 10 04:13:40 2016 
-## Date of summary: Sat Sep 10 04:13:40 2016 
-## 
-## Equations:
-## d_parent = - (alpha/beta) * 1/((time/beta) + 1) * parent
-## 
-## Model predictions using solution type analytical 
-## 
-## Fitted with method Port using 81 model solutions performed in 0.191 s
-## 
-## Weighting: none
-## 
-## Starting values for parameters to be optimised:
-##          value   type
-## parent_0 93.95  state
-## alpha     1.00 deparm
-## beta     10.00 deparm
-## 
-## Starting values for the transformed parameters actually optimised:
-##               value lower upper
-## parent_0  93.950000  -Inf   Inf
-## log_alpha  0.000000  -Inf   Inf
-## log_beta   2.302585  -Inf   Inf
-## 
-## Fixed parameter values:
-## None
-## 
-## Optimised, transformed parameters with symmetric confidence intervals:
-##           Estimate Std. Error   Lower   Upper
-## parent_0   93.7700     1.8560 89.5700 97.9700
-## log_alpha   0.3180     0.1867 -0.1044  0.7405
-## log_beta    0.2102     0.2943 -0.4555  0.8759
-## 
-## Parameter correlation:
-##           parent_0 log_alpha log_beta
-## parent_0   1.00000  -0.09553  -0.1863
-## log_alpha -0.09553   1.00000   0.9757
-## log_beta  -0.18628   0.97567   1.0000
-## 
-## Residual standard error: 2.628 on 9 degrees of freedom
-## 
-## Backtransformed parameters:
-## Confidence intervals for internally transformed parameters are asymmetric.
-## t-test (unrealistically) based on the assumption of normal distribution
-## for estimators of untransformed parameters.
-##          Estimate t value    Pr(>t)   Lower  Upper
-## parent_0   93.770  50.510 1.173e-12 89.5700 97.970
-## alpha       1.374   5.355 2.296e-04  0.9009  2.097
-## beta        1.234   3.398 3.949e-03  0.6341  2.401
-## 
-## Chi2 error levels in percent:
-##          err.min n.optim df
-## All data   6.205       3  3
-## parent     6.205       3  3
-## 
-## Estimated disappearance times:
-##          DT50  DT90 DT50back
-## parent 0.8092 5.356    1.612
      
-
      The error level at which the χ2 test passes is much lower in this case. Therefore, the FOMC model provides a better description of the data, as less experimental error has to be assumed in order to explain the data.
      
-
      
-
      
-
      DFOP fit for L2
      
-
      Fitting the four parameter DFOP model further reduces the χ2 error level.
      
-
      m.L2.DFOP <- mkinfit("DFOP", FOCUS_2006_L2_mkin, quiet = TRUE)
-plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,
-     main = "FOCUS L2 - DFOP")
      
-
      
-
      summary(m.L2.DFOP, data = FALSE)
      
-
      ## mkin version:    0.9.44.9000 
-## R version:       3.3.1 
-## Date of fit:     Sat Sep 10 04:13:41 2016 
-## Date of summary: Sat Sep 10 04:13:41 2016 
-## 
-## Equations:
-## d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
-##            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 *
-##            time))) * parent
-## 
-## Model predictions using solution type analytical 
-## 
-## Fitted with method Port using 336 model solutions performed in 0.797 s
-## 
-## Weighting: none
-## 
-## Starting values for parameters to be optimised:
-##          value   type
-## parent_0 93.95  state
-## k1        0.10 deparm
-## k2        0.01 deparm
-## g         0.50 deparm
-## 
-## Starting values for the transformed parameters actually optimised:
-##              value lower upper
-## parent_0 93.950000  -Inf   Inf
-## log_k1   -2.302585  -Inf   Inf
-## log_k2   -4.605170  -Inf   Inf
-## g_ilr     0.000000  -Inf   Inf
-## 
-## Fixed parameter values:
-## None
-## 
-## Optimised, transformed parameters with symmetric confidence intervals:
-##          Estimate Std. Error Lower Upper
-## parent_0  93.9500         NA    NA    NA
-## log_k1     3.1370         NA    NA    NA
-## log_k2    -1.0880         NA    NA    NA
-## g_ilr     -0.2821         NA    NA    NA
-## 
-## Parameter correlation:
      
-
      ## Warning in print.summary.mkinfit(x): Could not estimate covariance matrix; singular system:
      
-
      ## Could not estimate covariance matrix; singular system:
-## 
-## Residual standard error: 1.732 on 8 degrees of freedom
-## 
-## Backtransformed parameters:
-## Confidence intervals for internally transformed parameters are asymmetric.
-## t-test (unrealistically) based on the assumption of normal distribution
-## for estimators of untransformed parameters.
-##          Estimate t value Pr(>t) Lower Upper
-## parent_0  93.9500      NA     NA    NA    NA
-## k1        23.0400      NA     NA    NA    NA
-## k2         0.3369      NA     NA    NA    NA
-## g          0.4016      NA     NA    NA    NA
-## 
-## Chi2 error levels in percent:
-##          err.min n.optim df
-## All data    2.53       4  2
-## parent      2.53       4  2
-## 
-## Estimated disappearance times:
-##          DT50  DT90 DT50_k1 DT50_k2
-## parent 0.5335 5.311 0.03009   2.058
      
-
      Here, the DFOP model is clearly the best-fit model for dataset L2 based on the chi^2 error level criterion. However, the failure to calculate the covariance matrix indicates that the parameter estimates correlate excessively. Therefore, the FOMC model may be preferred for this dataset.
      
-
      
-
      
-
      
-
      Laboratory Data L3
      
-
      The following code defines example dataset L3 from the FOCUS kinetics report, p. 290.
      
-
      FOCUS_2006_L3 = data.frame(
-  t = c(0, 3, 7, 14, 30, 60, 91, 120),
-  parent = c(97.8, 60, 51, 43, 35, 22, 15, 12))
-FOCUS_2006_L3_mkin <- mkin_wide_to_long(FOCUS_2006_L3)
      
-
      
-
      Use mmkin to fit multiple models
      
-
      As of mkin version 0.9-39 (June 2015), we can fit several models to one or more datasets in one call to the function mmkin. The datasets have to be passed in a list, in this case a named list holding only the L3 dataset prepared above.
      
-
      # Only use one core here, not to offend the CRAN checks
-mm.L3 <- mmkin(c("SFO", "FOMC", "DFOP"), cores = 1,
-               list("FOCUS L3" = FOCUS_2006_L3_mkin), quiet = TRUE)
-plot(mm.L3)
      
-
      
-
      The χ2 error level of 21% as well as the plot suggest that the SFO model does not fit very well. The FOMC model performs better, with an error level at which the χ2 test passes of 7%. Fitting the four parameter DFOP model further reduces the χ2 error level considerably.
      
-
      
-
      
-
      Accessing elements of mmkin objects
      
-
      The objects returned by mmkin are arranged like a matrix, with models as a row index and datasets as a column index.
      
-
      We can extract the summary and plot for e.g. the DFOP fit, using square brackets for indexing which will result in the use of the summary and plot functions working on mkinfit objects.
      
-
      summary(mm.L3[["DFOP", 1]])
      
-
      ## mkin version:    0.9.44.9000 
-## R version:       3.3.1 
-## Date of fit:     Sat Sep 10 04:13:42 2016 
-## Date of summary: Sat Sep 10 04:13:42 2016 
-## 
-## Equations:
-## d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
-##            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 *
-##            time))) * parent
-## 
-## Model predictions using solution type analytical 
-## 
-## Fitted with method Port using 137 model solutions performed in 0.326 s
-## 
-## Weighting: none
-## 
-## Starting values for parameters to be optimised:
-##          value   type
-## parent_0 97.80  state
-## k1        0.10 deparm
-## k2        0.01 deparm
-## g         0.50 deparm
-## 
-## Starting values for the transformed parameters actually optimised:
-##              value lower upper
-## parent_0 97.800000  -Inf   Inf
-## log_k1   -2.302585  -Inf   Inf
-## log_k2   -4.605170  -Inf   Inf
-## g_ilr     0.000000  -Inf   Inf
-## 
-## Fixed parameter values:
-## None
-## 
-## Optimised, transformed parameters with symmetric confidence intervals:
-##          Estimate Std. Error   Lower     Upper
-## parent_0  97.7500    1.43800 93.7500 101.70000
-## log_k1    -0.6612    0.13340 -1.0310  -0.29100
-## log_k2    -4.2860    0.05902 -4.4500  -4.12200
-## g_ilr     -0.1229    0.05121 -0.2651   0.01925
-## 
-## Parameter correlation:
-##          parent_0  log_k1   log_k2   g_ilr
-## parent_0  1.00000  0.1640  0.01315  0.4253
-## log_k1    0.16400  1.0000  0.46478 -0.5526
-## log_k2    0.01315  0.4648  1.00000 -0.6631
-## g_ilr     0.42526 -0.5526 -0.66310  1.0000
-## 
-## Residual standard error: 1.439 on 4 degrees of freedom
-## 
-## Backtransformed parameters:
-## Confidence intervals for internally transformed parameters are asymmetric.
-## t-test (unrealistically) based on the assumption of normal distribution
-## for estimators of untransformed parameters.
-##          Estimate t value    Pr(>t)    Lower     Upper
-## parent_0 97.75000  67.970 1.404e-07 93.75000 101.70000
-## k1        0.51620   7.499 8.460e-04  0.35650   0.74750
-## k2        0.01376  16.940 3.557e-05  0.01168   0.01621
-## g         0.45660  25.410 7.121e-06  0.40730   0.50680
-## 
-## Chi2 error levels in percent:
-##          err.min n.optim df
-## All data   2.225       4  4
-## parent     2.225       4  4
-## 
-## Estimated disappearance times:
-##         DT50 DT90 DT50_k1 DT50_k2
-## parent 7.464  123   1.343   50.37
-## 
-## Data:
-##  time variable observed predicted residual
-##     0   parent     97.8     97.75  0.05396
-##     3   parent     60.0     60.45 -0.44933
-##     7   parent     51.0     49.44  1.56338
-##    14   parent     43.0     43.84 -0.83632
-##    30   parent     35.0     35.15 -0.14707
-##    60   parent     22.0     23.26 -1.25919
-##    91   parent     15.0     15.18 -0.18181
-##   120   parent     12.0     10.19  1.81395
      
-
      plot(mm.L3[["DFOP", 1]], show_errmin = TRUE)
      
-
      
-
      Here, a look to the model plot, the confidence intervals of the parameters and the correlation matrix suggest that the parameter estimates are reliable, and the DFOP model can be used as the best-fit model based on the χ2 error level criterion for laboratory data L3.
      
-
      This is also an example where the standard t-test for the parameter g_ilr is misleading, as it tests for a significant difference from zero. In this case, zero appears to be the correct value for this parameter, and the confidence interval for the backtransformed parameter g is quite narrow.
      
-
      
-
      
-
      
-
      Laboratory Data L4
      
-
      The following code defines example dataset L4 from the FOCUS kinetics report, p. 293:
      
-
      FOCUS_2006_L4 = data.frame(
-  t = c(0, 3, 7, 14, 30, 60, 91, 120),
-  parent = c(96.6, 96.3, 94.3, 88.8, 74.9, 59.9, 53.5, 49.0))
-FOCUS_2006_L4_mkin <- mkin_wide_to_long(FOCUS_2006_L4)
      
-
      Fits of the SFO and FOMC models, plots and summaries are produced below:
      
-
      # Only use one core here, not to offend the CRAN checks
-mm.L4 <- mmkin(c("SFO", "FOMC"), cores = 1,
-               list("FOCUS L4" = FOCUS_2006_L4_mkin), 
-               quiet = TRUE)
-plot(mm.L4)
      
-
      
-
      The χ2 error level of 3.3% as well as the plot suggest that the SFO model fits very well. The error level at which the χ2 test passes is slightly lower for the FOMC model. However, the difference appears negligible.
      
-
      summary(mm.L4[["SFO", 1]], data = FALSE)
      
-
      ## mkin version:    0.9.44.9000 
-## R version:       3.3.1 
-## Date of fit:     Sat Sep 10 04:13:42 2016 
-## Date of summary: Sat Sep 10 04:13:42 2016 
-## 
-## Equations:
-## d_parent = - k_parent_sink * parent
-## 
-## Model predictions using solution type analytical 
-## 
-## Fitted with method Port using 46 model solutions performed in 0.106 s
-## 
-## Weighting: none
-## 
-## Starting values for parameters to be optimised:
-##               value   type
-## parent_0       96.6  state
-## k_parent_sink   0.1 deparm
-## 
-## Starting values for the transformed parameters actually optimised:
-##                       value lower upper
-## parent_0          96.600000  -Inf   Inf
-## log_k_parent_sink -2.302585  -Inf   Inf
-## 
-## Fixed parameter values:
-## None
-## 
-## Optimised, transformed parameters with symmetric confidence intervals:
-##                   Estimate Std. Error  Lower   Upper
-## parent_0             96.44    1.94900 91.670 101.200
-## log_k_parent_sink    -5.03    0.07999 -5.225  -4.834
-## 
-## Parameter correlation:
-##                   parent_0 log_k_parent_sink
-## parent_0            1.0000            0.5865
-## log_k_parent_sink   0.5865            1.0000
-## 
-## Residual standard error: 3.651 on 6 degrees of freedom
-## 
-## Backtransformed parameters:
-## Confidence intervals for internally transformed parameters are asymmetric.
-## t-test (unrealistically) based on the assumption of normal distribution
-## for estimators of untransformed parameters.
-##                Estimate t value    Pr(>t)     Lower     Upper
-## parent_0      96.440000   49.49 2.283e-09 91.670000 1.012e+02
-## k_parent_sink  0.006541   12.50 8.008e-06  0.005378 7.955e-03
-## 
-## Chi2 error levels in percent:
-##          err.min n.optim df
-## All data   3.287       2  6
-## parent     3.287       2  6
-## 
-## Resulting formation fractions:
-##             ff
-## parent_sink  1
-## 
-## Estimated disappearance times:
-##        DT50 DT90
-## parent  106  352
      
-
      summary(mm.L4[["FOMC", 1]], data = FALSE)
      
-
      ## mkin version:    0.9.44.9000 
-## R version:       3.3.1 
-## Date of fit:     Sat Sep 10 04:13:42 2016 
-## Date of summary: Sat Sep 10 04:13:43 2016 
-## 
-## Equations:
-## d_parent = - (alpha/beta) * 1/((time/beta) + 1) * parent
-## 
-## Model predictions using solution type analytical 
-## 
-## Fitted with method Port using 66 model solutions performed in 0.15 s
-## 
-## Weighting: none
-## 
-## Starting values for parameters to be optimised:
-##          value   type
-## parent_0  96.6  state
-## alpha      1.0 deparm
-## beta      10.0 deparm
-## 
-## Starting values for the transformed parameters actually optimised:
-##               value lower upper
-## parent_0  96.600000  -Inf   Inf
-## log_alpha  0.000000  -Inf   Inf
-## log_beta   2.302585  -Inf   Inf
-## 
-## Fixed parameter values:
-## None
-## 
-## Optimised, transformed parameters with symmetric confidence intervals:
-##           Estimate Std. Error  Lower    Upper
-## parent_0   99.1400     1.6800 94.820 103.5000
-## log_alpha  -0.3506     0.3725 -1.308   0.6068
-## log_beta    4.1740     0.5635  2.726   5.6230
-## 
-## Parameter correlation:
-##           parent_0 log_alpha log_beta
-## parent_0    1.0000   -0.5365  -0.6083
-## log_alpha  -0.5365    1.0000   0.9913
-## log_beta   -0.6083    0.9913   1.0000
-## 
-## Residual standard error: 2.315 on 5 degrees of freedom
-## 
-## Backtransformed parameters:
-## Confidence intervals for internally transformed parameters are asymmetric.
-## t-test (unrealistically) based on the assumption of normal distribution
-## for estimators of untransformed parameters.
-##          Estimate t value    Pr(>t)   Lower   Upper
-## parent_0  99.1400  59.020 1.322e-08 94.8200 103.500
-## alpha      0.7042   2.685 2.178e-02  0.2703   1.835
-## beta      64.9800   1.775 6.807e-02 15.2600 276.600
-## 
-## Chi2 error levels in percent:
-##          err.min n.optim df
-## All data   2.029       3  5
-## parent     2.029       3  5
-## 
-## Estimated disappearance times:
-##         DT50 DT90 DT50back
-## parent 108.9 1644    494.9
      
-
      
-
      
-
      References
      
-
      Ranke, Johannes. Prüfung und Validierung von Modellierungssoftware als Alternative zu ModelMaker 4.0. Umweltbundesamt Projektnummer 27452.
      
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      Benchmark for a model that can also be solved with Eigenvalues
      
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      This evaluation is taken from the example section of mkinfit. When using an mkin version equal to or greater than 0.9-36 and a C compiler (gcc) is available, you will see a message that the model is being compiled from autogenerated C code when defining a model using mkinmod. The mkinmod() function checks for presence of the gcc compiler using
      
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      Sys.which("gcc")
      
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      ##            gcc 
-## "/usr/bin/gcc"
      
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      First, we build a simple degradation model for a parent compound with one metabolite.
      
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      library("mkin")
-SFO_SFO <- mkinmod(
-  parent = mkinsub("SFO", "m1"),
-  m1 = mkinsub("SFO"))
      
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      ## Successfully compiled differential equation model from auto-generated C code.
      
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      We can compare the performance of the Eigenvalue based solution against the compiled version and the R implementation of the differential equations using the microbenchmark package.
      
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      library("microbenchmark")
-library("ggplot2")
-mb.1 <- microbenchmark(
-  "deSolve, not compiled" = mkinfit(SFO_SFO, FOCUS_2006_D, 
-                                    solution_type = "deSolve", 
-                                    use_compiled = FALSE, quiet = TRUE),
-  "Eigenvalue based" = mkinfit(SFO_SFO, FOCUS_2006_D, 
-                               solution_type = "eigen", quiet = TRUE),
-  "deSolve, compiled" = mkinfit(SFO_SFO, FOCUS_2006_D, 
-                                solution_type = "deSolve", quiet = TRUE),
-  times = 3, control = list(warmup = 0))
      
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      ## Warning in microbenchmark(`deSolve, not compiled` = mkinfit(SFO_SFO,
-## FOCUS_2006_D, : Could not measure overhead. Your clock might lack
-## precision.
      
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      smb.1 <- summary(mb.1)
-print(mb.1)
      
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      ## Unit: milliseconds
-##                   expr       min        lq      mean    median        uq
-##  deSolve, not compiled 6507.8296 6549.5160 6597.4319 6591.2024 6642.2330
-##       Eigenvalue based  890.5249  917.6589  928.4907  944.7928  947.4735
-##      deSolve, compiled  735.4908  742.0363  749.3996  748.5817  756.3540
-##        max neval cld
-##  6693.2636     3   c
-##   950.1543     3  b 
-##   764.1264     3 a
      
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      autoplot(mb.1)
      
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      We see that using the compiled model is by a factor of 8.8 faster than using the R version with the default ode solver, and it is even faster than the Eigenvalue based solution implemented in R which does not need iterative solution of the ODEs:
      
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      rownames(smb.1) <- smb.1$expr
-smb.1["median"]/smb.1["deSolve, compiled", "median"]
      
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      ##                         median
-## deSolve, not compiled 8.804921
-## Eigenvalue based      1.262110
-## deSolve, compiled     1.000000
      
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      Benchmark for a model that can not be solved with Eigenvalues
      
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      This evaluation is also taken from the example section of mkinfit.
      
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      FOMC_SFO <- mkinmod(
-  parent = mkinsub("FOMC", "m1"),
-  m1 = mkinsub( "SFO"))
      
-
      ## Successfully compiled differential equation model from auto-generated C code.
      
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      mb.2 <- microbenchmark(
-  "deSolve, not compiled" = mkinfit(FOMC_SFO, FOCUS_2006_D, 
-                                    use_compiled = FALSE, quiet = TRUE),
-  "deSolve, compiled" = mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE),
-  times = 3, control = list(warmup = 0))
      
-
      ## Warning in microbenchmark(`deSolve, not compiled` = mkinfit(FOMC_SFO,
-## FOCUS_2006_D, : Could not measure overhead. Your clock might lack
-## precision.
      
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      smb.2 <- summary(mb.2)
-print(mb.2)
      
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      ## Unit: seconds
-##                   expr       min       lq      mean    median        uq
-##  deSolve, not compiled 13.741831 13.74759 13.815509 13.753350 13.852348
-##      deSolve, compiled  1.358402  1.35862  1.368666  1.358838  1.373798
-##        max neval cld
-##  13.951345     3   b
-##   1.388759     3  a
      
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      smb.2["median"]/smb.2["deSolve, compiled", "median"]
      
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      ##   median
-## 1     NA
-## 2     NA
      
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      autoplot(mb.2)
      
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      Here we get a performance benefit of a factor of 10.1 using the version of the differential equation model compiled from C code!
      
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      This vignette was built with mkin 0.9.44.9000 on
      
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      ## R version 3.3.1 (2016-06-21)
-## Platform: x86_64-pc-linux-gnu (64-bit)
-## Running under: Debian GNU/Linux 8 (jessie)
      
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      ## CPU model: Intel(R) Core(TM) i7-4710MQ CPU @ 2.50GHz
      
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-mkin - Kinetic evaluation of chemical degradation data
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      Wissenschaftlicher Berater, Kronacher Str. 8, 79639 Grenzach-Wyhlen, Germany
       Privatdozent at the University of Bremen
      
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      Abstract
      
-
      In the regulatory evaluation of chemical substances like plant protection products (pesticides), biocides and other chemicals, degradation data play an important role. For the evaluation of pesticide degradation experiments, detailed guidance has been developed, based on nonlinear optimisation. The R add-on package mkin (Ranke 2016) implements fitting some of the models recommended in this guidance from within R and calculates some statistical measures for data series within one or more compartments, for parent and metabolites.
      
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      require(mkin)
-m_SFO_SFO_SFO <- mkinmod(parent = mkinsub("SFO", "M1"),
-                         M1 = mkinsub("SFO", "M2"),
-                         M2 = mkinsub("SFO"), 
-                         use_of_ff = "max", quiet = TRUE)
-
-sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
-
-d_SFO_SFO_SFO <- mkinpredict(m_SFO_SFO_SFO,
-  c(k_parent = 0.03, 
-    f_parent_to_M1 = 0.5, k_M1 = log(2)/100, 
-    f_M1_to_M2 = 0.9, k_M2 = log(2)/50),
-  c(parent = 100, M1 = 0, M2 = 0),
-  sampling_times)
-
-d_SFO_SFO_SFO_err <- add_err(d_SFO_SFO_SFO, function(x) 3, n = 1, seed = 123456789 )
-
-f_SFO_SFO_SFO <- mkinfit(m_SFO_SFO_SFO, d_SFO_SFO_SFO_err[[1]], quiet = TRUE)
-
-plot_sep(f_SFO_SFO_SFO, lpos = c("topright", "bottomright", "bottomright"))
      
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      Background
      
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      Many approaches are possible regarding the evaluation of chemical degradation data.
      
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      The now deprecated kinfit package (Ranke 2015) in R (R Development Core Team 2016) implements the approach recommended in the kinetics report provided by the FOrum for Co-ordination of pesticide fate models and their USe (FOCUS Work Group on Degradation Kinetics 2006; 2014) for simple data series for one parent compound in one compartment.
      
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      The mkin package (Ranke 2016) extends this approach to data series with transformation products, commonly termed metabolites, and to more than one compartment. It is also possible to include back reactions, so equilibrium reactions and equilibrium partitioning can be specified, although this oftentimes leads to an overparameterisation of the model.
      
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      When the first mkin code was published in 2010, the most commonly used tools for fitting more complex kinetic degradation models to experimental data were KinGUI (Schäfer et al. 2007), a MATLAB based tool with a graphical user interface that was specifically tailored to the task and included some output as proposed by the FOCUS Kinetics Workgroup, and ModelMaker, a general purpose compartment based tool providing infrastructure for fitting dynamic simulation models based on differential equations to data.
      
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      The code was first uploaded to the BerliOS platform. When this was taken down, the version control history was imported into the R-Forge site, where the code is still mirrored today (see e.g. the initial commit on 11 May 2010).
      
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      At that time, the R package FME (Flexible Modelling Environment) (Soetaert and Petzoldt 2010) was already available, and provided a good basis for developing a package specifically tailored to the task. The remaining challenge was to make it as easy as possible for the users (including the author of this vignette) to specify the system of differential equations and to include the output requested by the FOCUS guidance, such as the relative standard deviation that has to be assumed for the residuals, such that the \(\chi^2\) goodness-of-fit test as defined by the FOCUS kinetics workgroup would pass using an significance level \(\alpha\) of 0.05.
      
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      Also, mkin introduced using analytical solutions for parent only kinetics for improved optimization speed. Later, Eigenvalue based solutions were introduced to mkin for the case of linear differential equations (i.e. where the FOMC or DFOP models were not used for the parent compound), greatly improving the optimization speed for these cases.
      
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      The possibility to specify back-reactions and a biphasic model (SFORB) for metabolites were present in mkin from the very beginning.
      
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-
      Derived software tools
      
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      Soon after the publication of mkin, two derived tools were published, namely KinGUII (available from Bayer Crop Science) and CAKE (commissioned to Tessella by Syngenta), which added a graphical user interface (GUI), and added fitting by iteratively reweighted least squares (IRLS) and characterisation of likely parameter distributions by Markov Chain Monte Carlo (MCMC) sampling.
      
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      CAKE focuses on a smooth use experience, sacrificing some flexibility in the model definition, originally allowing only two primary metabolites in parallel. The current version 3.2 of CAKE release in March 2016 uses a basic scheme for up to six metabolites in a flexible arrangement, but does not support back-reactions (non-instantaneous equilibria) or biphasic kinetics for metabolites.
      
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      KinGUI offers an even more flexible widget for specifying complex kinetic models. Back-reactions (non-instanteneous equilibria) were supported early on, but until 2014, only simple first-order models could be specified for transformation products. Starting with KinGUII version 2.1, biphasic modelling of metabolites was also available in KinGUII.
      
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      A further graphical user interface (GUI) that has recently been brought to a decent degree of maturity is the browser based GUI named gmkin. Please see its documentation page and manual for further information.
      
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      Recent developments
      
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      Currently (June 2016), the main features available in mkin which are not present in KinGUII or CAKE, are the speed increase by using compiled code when a compiler is present, parallel model fitting on multicore machines using the mmkin function, and the estimation of parameter confidence intervals based on transformed parameters. These are explained in more detail below.
      
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      Internal parameter transformations
      
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      For rate constants, the log transformation is used, as proposed by Bates and Watts (1988, 77, 149). Approximate intervals are constructed for the transformed rate constants (compare Bates and Watts 1988, 135), i.e. for their logarithms. Confidence intervals for the rate constants are then obtained using the appropriate backtransformation using the exponential function.
      
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      In the first version of mkin allowing for specifying models using formation fractions, a home-made reparameterisation was used in order to ensure that the sum of formation fractions would not exceed unity.
      
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      This method is still used in the current version of KinGUII (v2.1 from April 2014), with a modification that allows for fixing the pathway to sink to zero. CAKE uses penalties in the objective function in order to enforce this constraint.
      
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      In 2012, an alternative reparameterisation of the formation fractions was proposed together with René Lehmann (Ranke and Lehmann 2012), based on isometric logratio transformation (ILR). The aim was to improve the validity of the linear approximation of the objective function during the parameter estimation procedure as well as in the subsequent calculation of parameter confidence intervals.
      
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      Confidence intervals based on transformed parameters
      
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      In the first attempt at providing improved parameter confidence intervals introduced to mkin in 2013, confidence intervals obtained from FME on the transformed parameters were simply all backtransformed one by one to yield asymetric confidence intervals for the backtransformed parameters.
      
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      However, while there is a 1:1 relation between the rate constants in the model and the transformed parameters fitted in the model, the parameters obtained by the isometric logratio transformation are calculated from the set of formation fractions that quantify the paths to each of the compounds formed from a specific parent compound, and no such 1:1 relation exists.
      
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      Therefore, parameter confidence intervals for formation fractions obtained with this method only appear valid for the case of a single transformation product, where only one formation fraction is to be estimated, directly corresponding to one component of the ilr transformed parameter.
      
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      The confidence intervals obtained by backtransformation for the cases where a 1:1 relation between transformed and original parameter exist are considered by the author of this vignette to be more accurate than those obtained using a re-estimation of the Hessian matrix after backtransformation, as implemented in the FME package.
      
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      Parameter t-test based on untransformed parameters
      
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      The standard output of many nonlinear regression software packages includes the results from a test for significant difference from zero for all parameters. Such a test is also recommended to check the validity of rate constants in the FOCUS guidance (FOCUS Work Group on Degradation Kinetics 2014, 96ff).
      
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      It has been argued that the precondition for this test, i.e. normal distribution of the estimator for the parameters, is not fulfilled in the case of nonlinear regression (Ranke and Lehmann 2015). However, this test is commonly used by industry, consultants and national authorities in order to decide on the reliability of parameter estimates, based on the FOCUS guidance mentioned above. Therefore, the results of this one-sided t-test are included in the summary output from mkin.
      
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      As it is not reasonable to test for significant difference of the transformed parameters (e.g. \(log(k)\)) from zero, the t-test is calculated based on the model definition before parameter transformation, i.e. in a similar way as in packages that do not apply such an internal parameter transformation. A note is included in the mkin output, pointing to the fact that the t-test is based on the unjustified assumption of normal distribution of the parameter estimators.
      
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      References
      
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      Bates, D., and D. Watts. 1988. Nonlinear Regression and Its Applications. Wiley-Interscience.
      
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      FOCUS Work Group on Degradation Kinetics. 2006. Guidance Document on Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in EU Registration. Report of the FOCUS Work Group on Degradation Kinetics. http://focus.jrc.ec.europa.eu/dk.
      
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      ———. 2014. Generic Guidance for Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in EU Registration. 1.1. http://focus.jrc.ec.europa.eu/dk.
      
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      R Development Core Team. 2016. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. http://www.R-project.org.
      
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      Ranke, J. 2015. `kinfit`: Routines for Fitting Simple Kinetic Models to Chemical Degradation Data. http://CRAN.R-project.org/package=kinfit.
      
-
      ———. 2016. `mkin`: Kinetic Evaluation of Chemical Degradation Data. http://CRAN.R-project.org/package=mkin.
      
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      Ranke, J., and R. Lehmann. 2012. “Parameter Reliability in Kinetic Evaluation of Environmental Metabolism Data - Assessment and the Influence of Model Specification.” In SETAC World 20-24 May. Berlin.
      
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      ———. 2015. “To T-Test or Not to T-Test, That Is the Question.” In XV Symposium on Pesticide Chemistry 2-4 September 2015. Piacenza. http://chem.uft.uni-bremen.de/ranke/posters/piacenza_2015.pdf.
      
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      Schäfer, D., B. Mikolasch, P. Rainbird, and B. Harvey. 2007. “KinGUI: a New Kinetic Software Tool for Evaluations According to FOCUS Degradation Kinetics.” In Proceedings of the XIII Symposium Pesticide Chemistry, edited by Del Re A. A. M., Capri E., Fragoulis G., and Trevisan M., 916–23. Piacenza.
      
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      Soetaert, Karline, and Thomas Petzoldt. 2010. “Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME.” Journal of Statistical Software 33 (3): 1–28. http://www.jstatsoft.org/v33/i03/.
      
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