From e5d1df9a9b1f0951d7dfbaf24eee4294470b73e2 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Thu, 17 Nov 2022 14:54:20 +0100 Subject: Complete update of online docs for v1.2.0 --- docs/reference/synthetic_data_for_UBA_2014.html | 258 ++++++++++++------------ 1 file changed, 132 insertions(+), 126 deletions(-) (limited to 'docs/reference/synthetic_data_for_UBA_2014.html') diff --git a/docs/reference/synthetic_data_for_UBA_2014.html b/docs/reference/synthetic_data_for_UBA_2014.html index e9251de3..c00d1b55 100644 --- a/docs/reference/synthetic_data_for_UBA_2014.html +++ b/docs/reference/synthetic_data_for_UBA_2014.html @@ -32,7 +32,7 @@ Compare also the code in the example section to see the degradation models."> mkin - 1.1.0 + 1.2.0 @@ -59,11 +59,14 @@ Compare also the code in the example section to see the degradation models.">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models
  • - Example evaluation of FOCUS Example Dataset Z + Short demo of the multistart method
  • Performance benefit by using compiled model definitions in mkin
    • +
  • + Example evaluation of FOCUS Example Dataset Z +
  • Calculation of time weighted average concentrations with mkin
    • @@ -71,7 +74,10 @@ Compare also the code in the example section to see the degradation models.">Example evaluation of NAFTA SOP Attachment examples
  • - Some benchmark timings + Benchmark timings for mkin +
    • +
  • + Benchmark timings for saem.mmkin
  • @@ -117,7 +123,7 @@ Compare also the code in the example section to see the degradation models.">
    - 
    synthetic_data_for_UBA_2014
    + 
    synthetic_data_for_UBA_2014
    @@ -141,115 +147,115 @@ Compare also the code in the example section to see the degradation models.">

    Examples

    - 
    # \dontrun{
-# The data have been generated using the following kinetic models
-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")
+    
    # \dontrun{
+# The data have been generated using the following kinetic models
+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")
 #> Temporary DLL for differentials generated and loaded
-
-
-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_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")
 #> Temporary DLL for differentials generated and loaded
-
-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_lin <- mkinmod(parent = list(type = "DFOP", to = "M1"),
+                            M1 = list(type = "SFO", to = "M2"),
+                            M2 = list(type = "SFO"), use_of_ff = "max")
 #> Temporary DLL for differentials generated and loaded
-
-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")
+
+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")
 #> Temporary DLL for differentials generated and loaded
-
-# The model predictions without intentional error were generated as follows
-sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
-
-d_synth_SFO_lin <- mkinpredict(m_synth_SFO_lin,
-                               c(k_parent = 0.7, f_parent_to_M1 = 0.8,
-                                 k_M1 = 0.3, f_M1_to_M2 = 0.7,
-                                 k_M2 = 0.02),
-                               c(parent = 100, M1 = 0, M2 = 0),
-                               sampling_times)
-
-d_synth_DFOP_lin <- mkinpredict(m_synth_DFOP_lin,
-                                c(k1 = 0.2, k2 = 0.02, g = 0.5,
-                                  f_parent_to_M1 = 0.5, k_M1 = 0.3,
-                                  f_M1_to_M2 = 0.7, k_M2 = 0.02),
-                                 c(parent = 100, M1 = 0, M2 = 0),
-                                 sampling_times)
-
-d_synth_SFO_par <- mkinpredict(m_synth_SFO_par,
-                               c(k_parent = 0.2,
-                                 f_parent_to_M1 = 0.8, k_M1 = 0.01,
-                                 f_parent_to_M2 = 0.2, k_M2 = 0.02),
-                                 c(parent = 100, M1 = 0, M2 = 0),
-                                 sampling_times)
-
-d_synth_DFOP_par <- mkinpredict(m_synth_DFOP_par,
-                               c(k1 = 0.3, k2 = 0.02, g = 0.7,
-                                 f_parent_to_M1 = 0.6, k_M1 = 0.04,
-                                 f_parent_to_M2 = 0.4, k_M2 = 0.01),
-                                 c(parent = 100, M1 = 0, M2 = 0),
-                                 sampling_times)
-
-# Construct names for datasets with errors
-d_synth_names = paste0("d_synth_", c("SFO_lin", "SFO_par",
-                                     "DFOP_lin", "DFOP_par"))
-
-# Original function used or adding errors. The add_err function now published
-# with this package is a slightly generalised version where the names of
-# secondary compartments that should have an initial value of zero (M1 and M2
-# in this case) are not hardcoded any more.
-# add_err = function(d, sdfunc, LOD = 0.1, reps = 2, seed = 123456789)
-# {
-#   set.seed(seed)
-#   d_long = mkin_wide_to_long(d, time = "time")
-#   d_rep = data.frame(lapply(d_long, rep, each = 2))
-#   d_rep$value = rnorm(length(d_rep$value), d_rep$value, sdfunc(d_rep$value))
-#
-#   d_rep[d_rep$time == 0 & d_rep$name %in% c("M1", "M2"), "value"] <- 0
-#   d_NA <- transform(d_rep, value = ifelse(value < LOD, NA, value))
-#   d_NA$value <- round(d_NA$value, 1)
-#   return(d_NA)
-# }
-
-# The following is the simplified version of the two-component model of Rocke
-# and Lorenzato (1995)
-sdfunc_twocomp = function(value, sd_low, rsd_high) {
-  sqrt(sd_low^2 + value^2 * rsd_high^2)
-}
-
-# Add the errors.
-for (d_synth_name in d_synth_names)
-{
-  d_synth = get(d_synth_name)
-  assign(paste0(d_synth_name, "_a"), add_err(d_synth, function(value) 3))
-  assign(paste0(d_synth_name, "_b"), add_err(d_synth, function(value) 7))
-  assign(paste0(d_synth_name, "_c"), add_err(d_synth,
-                           function(value) sdfunc_twocomp(value, 0.5, 0.07)))
-
-}
-
-d_synth_err_names = c(
-  paste(rep(d_synth_names, each = 3), letters[1:3], sep = "_")
-)
-
-# This is just one example of an evaluation using the kinetic model used for
-# the generation of the data
-  fit <- mkinfit(m_synth_SFO_lin, synthetic_data_for_UBA_2014[[1]]$data,
-                 quiet = TRUE)
-  plot_sep(fit)
+
+# The model predictions without intentional error were generated as follows
+sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
+
+d_synth_SFO_lin <- mkinpredict(m_synth_SFO_lin,
+                               c(k_parent = 0.7, f_parent_to_M1 = 0.8,
+                                 k_M1 = 0.3, f_M1_to_M2 = 0.7,
+                                 k_M2 = 0.02),
+                               c(parent = 100, M1 = 0, M2 = 0),
+                               sampling_times)
+
+d_synth_DFOP_lin <- mkinpredict(m_synth_DFOP_lin,
+                                c(k1 = 0.2, k2 = 0.02, g = 0.5,
+                                  f_parent_to_M1 = 0.5, k_M1 = 0.3,
+                                  f_M1_to_M2 = 0.7, k_M2 = 0.02),
+                                 c(parent = 100, M1 = 0, M2 = 0),
+                                 sampling_times)
+
+d_synth_SFO_par <- mkinpredict(m_synth_SFO_par,
+                               c(k_parent = 0.2,
+                                 f_parent_to_M1 = 0.8, k_M1 = 0.01,
+                                 f_parent_to_M2 = 0.2, k_M2 = 0.02),
+                                 c(parent = 100, M1 = 0, M2 = 0),
+                                 sampling_times)
+
+d_synth_DFOP_par <- mkinpredict(m_synth_DFOP_par,
+                               c(k1 = 0.3, k2 = 0.02, g = 0.7,
+                                 f_parent_to_M1 = 0.6, k_M1 = 0.04,
+                                 f_parent_to_M2 = 0.4, k_M2 = 0.01),
+                                 c(parent = 100, M1 = 0, M2 = 0),
+                                 sampling_times)
+
+# Construct names for datasets with errors
+d_synth_names = paste0("d_synth_", c("SFO_lin", "SFO_par",
+                                     "DFOP_lin", "DFOP_par"))
+
+# Original function used or adding errors. The add_err function now published
+# with this package is a slightly generalised version where the names of
+# secondary compartments that should have an initial value of zero (M1 and M2
+# in this case) are not hardcoded any more.
+# add_err = function(d, sdfunc, LOD = 0.1, reps = 2, seed = 123456789)
+# {
+#   set.seed(seed)
+#   d_long = mkin_wide_to_long(d, time = "time")
+#   d_rep = data.frame(lapply(d_long, rep, each = 2))
+#   d_rep$value = rnorm(length(d_rep$value), d_rep$value, sdfunc(d_rep$value))
+#
+#   d_rep[d_rep$time == 0 & d_rep$name %in% c("M1", "M2"), "value"] <- 0
+#   d_NA <- transform(d_rep, value = ifelse(value < LOD, NA, value))
+#   d_NA$value <- round(d_NA$value, 1)
+#   return(d_NA)
+# }
+
+# The following is the simplified version of the two-component model of Rocke
+# and Lorenzato (1995)
+sdfunc_twocomp = function(value, sd_low, rsd_high) {
+  sqrt(sd_low^2 + value^2 * rsd_high^2)
+}
+
+# Add the errors.
+for (d_synth_name in d_synth_names)
+{
+  d_synth = get(d_synth_name)
+  assign(paste0(d_synth_name, "_a"), add_err(d_synth, function(value) 3))
+  assign(paste0(d_synth_name, "_b"), add_err(d_synth, function(value) 7))
+  assign(paste0(d_synth_name, "_c"), add_err(d_synth,
+                           function(value) sdfunc_twocomp(value, 0.5, 0.07)))
+
+}
+
+d_synth_err_names = c(
+  paste(rep(d_synth_names, each = 3), letters[1:3], sep = "_")
+)
+
+# This is just one example of an evaluation using the kinetic model used for
+# the generation of the data
+  fit <- mkinfit(m_synth_SFO_lin, synthetic_data_for_UBA_2014[[1]]$data,
+                 quiet = TRUE)
+  plot_sep(fit)
 
-  summary(fit)
-#> mkin version used for fitting:    1.1.0 
-#> R version used for fitting:       4.2.0 
-#> Date of fit:     Wed May 18 20:42:21 2022 
-#> Date of summary: Wed May 18 20:42:21 2022 
+  summary(fit)
+#> mkin version used for fitting:    1.2.0 
+#> R version used for fitting:       4.2.2 
+#> Date of fit:     Thu Nov 17 14:04:10 2022 
+#> Date of summary: Thu Nov 17 14:04:11 2022 
 #> 
 #> Equations:
 #> d_parent/dt = - k_parent * parent
@@ -258,7 +264,7 @@ Compare also the code in the example section to see the degradation models.">#> 
 #> Model predictions using solution type deSolve 
 #> 
-#> Fitted using 830 model solutions performed in 1.716 s
+#> Fitted using 833 model solutions performed in 0.574 s
 #> 
 #> Error model: Constant variance 
 #> 
@@ -310,15 +316,15 @@ Compare also the code in the example section to see the degradation models.">#> log_k_M2         2.819e-02    7.166e-02 -3.929e-01  1.000e+00      -2.658e-01
 #> f_parent_qlogis -4.624e-01   -5.682e-01  7.478e-01 -2.658e-01       1.000e+00
 #> f_M1_qlogis      1.614e-01    4.102e-01 -8.109e-01  5.419e-01      -8.605e-01
-#> sigma            2.063e-07   -1.980e-07  1.088e-07  1.041e-07       7.820e-09
+#> sigma           -2.900e-08   -8.030e-09 -2.741e-08  3.938e-08      -2.681e-08
 #>                 f_M1_qlogis      sigma
-#> parent_0          1.614e-01  2.063e-07
-#> log_k_parent      4.102e-01 -1.980e-07
-#> log_k_M1         -8.109e-01  1.088e-07
-#> log_k_M2          5.419e-01  1.041e-07
-#> f_parent_qlogis  -8.605e-01  7.820e-09
-#> f_M1_qlogis       1.000e+00 -6.495e-08
-#> sigma            -6.495e-08  1.000e+00
+#> parent_0          1.614e-01 -2.900e-08
+#> log_k_parent      4.102e-01 -8.030e-09
+#> log_k_M1         -8.109e-01 -2.741e-08
+#> log_k_M2          5.419e-01  3.938e-08
+#> f_parent_qlogis  -8.605e-01 -2.681e-08
+#> f_M1_qlogis       1.000e+00  4.971e-08
+#> sigma             4.971e-08  1.000e+00
 #> 
 #> Backtransformed parameters:
 #> Confidence intervals for internally transformed parameters are asymmetric.
@@ -355,8 +361,8 @@ Compare also the code in the example section to see the degradation models.">#> 
 #> Data:
 #>  time variable observed  predicted residual
-#>     0   parent    101.5  1.021e+02 -0.56249
-#>     0   parent    101.2  1.021e+02 -0.86249
+#>     0   parent    101.5  1.021e+02 -0.56248
+#>     0   parent    101.2  1.021e+02 -0.86248
 #>     1   parent     53.9  4.873e+01  5.17118
 #>     1   parent     47.5  4.873e+01 -1.22882
 #>     3   parent     10.4  1.111e+01 -0.70773
@@ -365,8 +371,8 @@ Compare also the code in the example section to see the degradation models.">#>     7   parent      0.3  5.772e-01 -0.27717
 #>    14   parent      3.5  3.264e-03  3.49674
 #>    28   parent      3.2  1.045e-07  3.20000
-#>    90   parent      0.6  9.534e-10  0.60000
-#>   120   parent      3.5 -5.941e-10  3.50000
+#>    90   parent      0.6  9.530e-10  0.60000
+#>   120   parent      3.5 -5.940e-10  3.50000
 #>     1       M1     36.4  3.479e+01  1.61088
 #>     1       M1     37.4  3.479e+01  2.61088
 #>     3       M1     34.3  3.937e+01 -5.07027
@@ -376,9 +382,9 @@ Compare also the code in the example section to see the degradation models.">#>    14       M1      5.8  1.995e+00  3.80469
 #>    14       M1      1.2  1.995e+00 -0.79531
 #>    60       M1      0.5  2.111e-06  0.50000
-#>    90       M1      3.2 -9.675e-10  3.20000
-#>   120       M1      1.5  7.671e-10  1.50000
-#>   120       M1      0.6  7.671e-10  0.60000
+#>    90       M1      3.2 -9.670e-10  3.20000
+#>   120       M1      1.5  7.670e-10  1.50000
+#>   120       M1      0.6  7.670e-10  0.60000
 #>     1       M2      4.8  4.455e+00  0.34517
 #>     3       M2     20.9  2.153e+01 -0.62527
 #>     3       M2     19.3  2.153e+01 -2.22527
@@ -394,7 +400,7 @@ Compare also the code in the example section to see the degradation models.">#>    90       M2     10.8  1.013e+01  0.67130
 #>   120       M2      9.8  5.521e+00  4.27893
 #>   120       M2      3.3  5.521e+00 -2.22107
-# }
+# }
    @@ -409,7 +415,7 @@ Compare also the code in the example section to see the degradation models.">
    -

    Site built with pkgdown 2.0.3.

    +

    Site built with pkgdown 2.0.6.

    -- cgit v1.2.1