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|
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<title>[.mmkin. mkin 0.9-41</title>
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Johannes Ranke
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<h1>Subsetting method for mmkin objects</h1>
<div class="row">
<div class="span8">
<h2>Usage</h2>
<pre><div>"["(x, i, j, ..., drop = FALSE)</div></pre>
<h2>Arguments</h2>
<dl>
<dt>x</dt>
<dd>An <code><a href='mmkin.html'>mmkin</a> object</code></dd>
<dt>i</dt>
<dd>Row index selecting the fits for specific models</dd>
<dt>j</dt>
<dd>Column index selecting the fits to specific datasets</dd>
<dt>...</dt>
<dd>Not used, only there to satisfy the generic method definition</dd>
<dt>drop</dt>
<dd>If FALSE, the method always returns an mmkin object, otherwise either
a list of mkinfit objects or a single mkinfit object.</dd>
</dl>
<div class="Description">
<h2>Description</h2>
<p>Subsetting method for mmkin objects.</p>
</div>
<div class="Value">
<h2>Value</h2>
<p><dl>
An object of class <code><a href='mmkin.html'>mmkin</a></code>.
</dl></p>
</div>
<h2 id="examples">Examples</h2>
<pre class="examples"><div class='input'> # 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", ]
</div>
<div class='output'> dataset
model B C
FOMC List,42 List,42
attr(,"class")
[1] "mmkin"
</div>
<div class='input'> fits[, "B"]
</div>
<div class='output'> dataset
model B
SFO List,42
FOMC List,42
attr(,"class")
[1] "mmkin"
</div>
<div class='input'> fits[, "B", drop = TRUE]$FOMC
</div>
<div class='output'>$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 parent parent
1.046192647 -3.322396479 3.655156669 -1.705316770 0.406306255 -0.123734689 -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
0.260 0.000 0.262
$mkinmod
<mkinmod> model generated with
Use of formation fractions $use_of_ff: min
Specification $spec:
$parent
$type: FOMC
$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: 0x2a98780>
$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: 0x2a98780>
$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
$date
[1] "Mon Nov 9 09:05:53 2015"
attr(,"class")
[1] "mkinfit" "modFit"
</div>
<div class='input'> fits["SFO", "B"]
</div>
<div class='output'> dataset
model B
SFO List,42
attr(,"class")
[1] "mmkin"
</div>
<div class='input'> fits[["SFO", "B"]] # This is equivalent to
</div>
<div class='output'>$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 parent parent
0.55407218 -2.98452128 4.20445742 -1.68599939 -0.58185357 -0.72033730 -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
0.088 0.000 0.086
$mkinmod
<mkinmod> model generated with
Use of formation fractions $use_of_ff: min
Specification $spec:
$parent
$type: SFO
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: 0x2ca6db8>
$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: 0x2ca6db8>
$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] "Mon Nov 9 09:05:53 2015"
attr(,"class")
[1] "mkinfit" "modFit"
</div>
<div class='input'> fits["SFO", "B", drop = TRUE]
</div>
<div class='output'>[[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 parent parent
0.55407218 -2.98452128 4.20445742 -1.68599939 -0.58185357 -0.72033730 -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
0.088 0.000 0.086
$mkinmod
<mkinmod> model generated with
Use of formation fractions $use_of_ff: min
Specification $spec:
$parent
$type: SFO
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: 0x2ca6db8>
$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: 0x2ca6db8>
$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] "Mon Nov 9 09:05:53 2015"
attr(,"class")
[1] "mkinfit" "modFit"
</div></pre>
</div>
<div class="span4">
<!-- <ul>
<li>[.mmkin</li>
</ul>
<ul>
</ul> -->
<h2>Author</h2>
Johannes Ranke
</div>
</div>
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