From 6178249bbb5e9de7cb7f34287ee7de28a68fed6c Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Wed, 10 Aug 2022 15:38:17 +0200 Subject: Change dev branch used for docs, update static docs --- docs/dev/reference/nlme.mmkin.html | 239 +++++++++++++++++++++---------------- 1 file changed, 139 insertions(+), 100 deletions(-) (limited to 'docs/dev/reference/nlme.mmkin.html') diff --git a/docs/dev/reference/nlme.mmkin.html b/docs/dev/reference/nlme.mmkin.html index e138ddd4..2bbadb88 100644 --- a/docs/dev/reference/nlme.mmkin.html +++ b/docs/dev/reference/nlme.mmkin.html @@ -19,7 +19,7 @@ have been obtained by fitting the same model to a list of datasets."> mkin - 1.1.0 + 1.1.2 @@ -28,7 +28,7 @@ have been obtained by fitting the same model to a list of datasets.">Functions and data
  • - Example evaluation of FOCUS Laboratory Data L1 to L3
    • +
  • + Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models +
  • Example evaluation of FOCUS Example Dataset Z
    • @@ -88,78 +91,115 @@ have been obtained by fitting the same model to a list of datasets.

    - 
    # S3 method for mmkin
-nlme(
-  model,
-  data = "auto",
-  fixed = lapply(as.list(names(mean_degparms(model))), function(el) eval(parse(text =
-    paste(el, 1, sep = "~")))),
-  random = pdDiag(fixed),
-  groups,
-  start = mean_degparms(model, random = TRUE, test_log_parms = TRUE),
-  correlation = NULL,
-  weights = NULL,
-  subset,
-  method = c("ML", "REML"),
-  na.action = na.fail,
-  naPattern,
-  control = list(),
-  verbose = FALSE
-)
-
-# S3 method for nlme.mmkin
-print(x, digits = max(3, getOption("digits") - 3), ...)
-
-# S3 method for nlme.mmkin
-update(object, ...)
    + 
    # S3 method for mmkin
+nlme(
+  model,
+  data = "auto",
+  fixed = lapply(as.list(names(mean_degparms(model))), function(el) eval(parse(text =
+    paste(el, 1, sep = "~")))),
+  random = pdDiag(fixed),
+  groups,
+  start = mean_degparms(model, random = TRUE, test_log_parms = TRUE),
+  correlation = NULL,
+  weights = NULL,
+  subset,
+  method = c("ML", "REML"),
+  na.action = na.fail,
+  naPattern,
+  control = list(),
+  verbose = FALSE
+)
+
+# S3 method for nlme.mmkin
+print(x, digits = max(3, getOption("digits") - 3), ...)
+
+# S3 method for nlme.mmkin
+update(object, ...)

    Arguments

    model

    An mmkin row object.

    + +
    data

    Ignored, data are taken from the mmkin model

    + +
    fixed

    Ignored, all degradation parameters fitted in the mmkin model are used as fixed parameters

    + +
    random

    If not specified, no correlations between random effects are set up for the optimised degradation model parameters. This is achieved by using the nlme::pdDiag method.

    + +
    groups

    See the documentation of nlme

    + +
    start

    If not specified, mean values of the fitted degradation parameters taken from the mmkin object are used

    + +
    correlation

    See the documentation of nlme

    + +
    weights

    passed to nlme

    + +
    subset

    passed to nlme

    + +
    method

    passed to nlme

    + +
    na.action

    passed to nlme

    + +
    naPattern

    passed to nlme

    + +
    control

    passed to nlme

    + +
    verbose

    passed to nlme

    + +
    x

    An nlme.mmkin object to print

    + +
    digits

    Number of digits to use for printing

    + +
    ...

    Update specifications passed to update.nlme

    + +
    object

    An nlme.mmkin object to update

    +

    Value

    -

    Upon success, a fitted 'nlme.mmkin' object, which is an nlme object + + +

    Upon success, a fitted 'nlme.mmkin' object, which is an nlme object with additional elements. It also inherits from 'mixed.mmkin'.

    @@ -182,20 +222,19 @@ methods that will automatically work on 'nlme.mmkin' objects, such as

    Examples

    - 
    ds <- lapply(experimental_data_for_UBA_2019[6:10],
- function(x) subset(x$data[c("name", "time", "value")], name == "parent"))
-f <- mmkin(c("SFO", "DFOP"), ds, quiet = TRUE, cores = 1)
-library(nlme)
-f_nlme_sfo <- nlme(f["SFO", ])
-
-# \dontrun{
-
-  f_nlme_dfop <- nlme(f["DFOP", ])
-  anova(f_nlme_sfo, f_nlme_dfop)
+    
    ds <- lapply(experimental_data_for_UBA_2019[6:10],
+ function(x) subset(x$data[c("name", "time", "value")], name == "parent"))
+
+# \dontrun{
+  f <- mmkin(c("SFO", "DFOP"), ds, quiet = TRUE, cores = 1)
+  library(nlme)
+  f_nlme_sfo <- nlme(f["SFO", ])
+  f_nlme_dfop <- nlme(f["DFOP", ])
+  anova(f_nlme_sfo, f_nlme_dfop)
 #>             Model df      AIC      BIC    logLik   Test  L.Ratio p-value
 #> f_nlme_sfo      1  5 625.0539 637.5529 -307.5269                        
 #> f_nlme_dfop     2  9 495.1270 517.6253 -238.5635 1 vs 2 137.9269  <.0001
-  print(f_nlme_dfop)
+  print(f_nlme_dfop)
 #> Kinetic nonlinear mixed-effects model fit by maximum likelihood
 #> 
 #> Structural model:
@@ -220,50 +259,50 @@ methods that will automatically work on 'nlme.mmkin' objects, such as
 #>         parent_0 log_k1 log_k2 g_qlogis Residual
 #> StdDev:    2.488 0.8447   1.33   0.4652    2.321
 #> 
-  plot(f_nlme_dfop)
+  plot(f_nlme_dfop)
 
-  endpoints(f_nlme_dfop)
+  endpoints(f_nlme_dfop)
 #> $distimes
 #>            DT50     DT90 DT50back  DT50_k1  DT50_k2
 #> parent 10.79857 100.7937 30.34193 4.193938 43.85443
 #> 
-
-  ds_2 <- lapply(experimental_data_for_UBA_2019[6:10],
-   function(x) x$data[c("name", "time", "value")])
-  m_sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"),
-    A1 = mkinsub("SFO"), use_of_ff = "min", quiet = TRUE)
-  m_sfo_sfo_ff <- mkinmod(parent = mkinsub("SFO", "A1"),
-    A1 = mkinsub("SFO"), use_of_ff = "max", quiet = TRUE)
-  m_dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"),
-    A1 = mkinsub("SFO"), quiet = TRUE)
-
-  f_2 <- mmkin(list("SFO-SFO" = m_sfo_sfo,
-   "SFO-SFO-ff" = m_sfo_sfo_ff,
-   "DFOP-SFO" = m_dfop_sfo),
-    ds_2, quiet = TRUE)
-
-  f_nlme_sfo_sfo <- nlme(f_2["SFO-SFO", ])
-  plot(f_nlme_sfo_sfo)
+
+  ds_2 <- lapply(experimental_data_for_UBA_2019[6:10],
+   function(x) x$data[c("name", "time", "value")])
+  m_sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"),
+    A1 = mkinsub("SFO"), use_of_ff = "min", quiet = TRUE)
+  m_sfo_sfo_ff <- mkinmod(parent = mkinsub("SFO", "A1"),
+    A1 = mkinsub("SFO"), use_of_ff = "max", quiet = TRUE)
+  m_dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"),
+    A1 = mkinsub("SFO"), quiet = TRUE)
+
+  f_2 <- mmkin(list("SFO-SFO" = m_sfo_sfo,
+   "SFO-SFO-ff" = m_sfo_sfo_ff,
+   "DFOP-SFO" = m_dfop_sfo),
+    ds_2, quiet = TRUE)
+
+  f_nlme_sfo_sfo <- nlme(f_2["SFO-SFO", ])
+  plot(f_nlme_sfo_sfo)
 
-
-  # With formation fractions this does not coverge with defaults
-  # f_nlme_sfo_sfo_ff <- nlme(f_2["SFO-SFO-ff", ])
-  #plot(f_nlme_sfo_sfo_ff)
-
-  # For the following, we need to increase pnlsMaxIter and the tolerance
-  # to get convergence
-  f_nlme_dfop_sfo <- nlme(f_2["DFOP-SFO", ],
-    control = list(pnlsMaxIter = 120, tolerance = 5e-4))
-
-  plot(f_nlme_dfop_sfo)
+
+  # With formation fractions this does not coverge with defaults
+  # f_nlme_sfo_sfo_ff <- nlme(f_2["SFO-SFO-ff", ])
+  #plot(f_nlme_sfo_sfo_ff)
+
+  # For the following, we need to increase pnlsMaxIter and the tolerance
+  # to get convergence
+  f_nlme_dfop_sfo <- nlme(f_2["DFOP-SFO", ],
+    control = list(pnlsMaxIter = 120, tolerance = 5e-4))
+
+  plot(f_nlme_dfop_sfo)
 
-
-  anova(f_nlme_dfop_sfo, f_nlme_sfo_sfo)
+
+  anova(f_nlme_dfop_sfo, f_nlme_sfo_sfo)
 #>                 Model df       AIC       BIC    logLik   Test  L.Ratio p-value
 #> f_nlme_dfop_sfo     1 13  843.8547  884.6201 -408.9274                        
 #> f_nlme_sfo_sfo      2  9 1085.1821 1113.4043 -533.5910 1 vs 2 249.3274  <.0001
-
-  endpoints(f_nlme_sfo_sfo)
+
+  endpoints(f_nlme_sfo_sfo)
 #> $ff
 #> parent_sink   parent_A1     A1_sink 
 #>   0.5912432   0.4087568   1.0000000 
@@ -273,7 +312,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as
 #> parent 19.13518  63.5657
 #> A1     66.02155 219.3189
 #> 
-  endpoints(f_nlme_dfop_sfo)
+  endpoints(f_nlme_dfop_sfo)
 #> $ff
 #>   parent_A1 parent_sink 
 #>   0.2768574   0.7231426 
@@ -283,17 +322,17 @@ methods that will automatically work on 'nlme.mmkin' objects, such as
 #> parent  11.07091 104.6320 31.49737 4.462383 46.20825
 #> A1     162.30519 539.1662       NA       NA       NA
 #> 
-
-  if (length(findFunction("varConstProp")) > 0) { # tc error model for nlme available
-    # Attempts to fit metabolite kinetics with the tc error model are possible,
-    # but need tweeking of control values and sometimes do not converge
-
-    f_tc <- mmkin(c("SFO", "DFOP"), ds, quiet = TRUE, error_model = "tc")
-    f_nlme_sfo_tc <- nlme(f_tc["SFO", ])
-    f_nlme_dfop_tc <- nlme(f_tc["DFOP", ])
-    AIC(f_nlme_sfo, f_nlme_sfo_tc, f_nlme_dfop, f_nlme_dfop_tc)
-    print(f_nlme_dfop_tc)
-  }
+
+  if (length(findFunction("varConstProp")) > 0) { # tc error model for nlme available
+    # Attempts to fit metabolite kinetics with the tc error model are possible,
+    # but need tweeking of control values and sometimes do not converge
+
+    f_tc <- mmkin(c("SFO", "DFOP"), ds, quiet = TRUE, error_model = "tc")
+    f_nlme_sfo_tc <- nlme(f_tc["SFO", ])
+    f_nlme_dfop_tc <- nlme(f_tc["DFOP", ])
+    AIC(f_nlme_sfo, f_nlme_sfo_tc, f_nlme_dfop, f_nlme_dfop_tc)
+    print(f_nlme_dfop_tc)
+  }
 #> Kinetic nonlinear mixed-effects model fit by maximum likelihood
 #> 
 #> Structural model:
@@ -324,10 +363,10 @@ methods that will automatically work on 'nlme.mmkin' objects, such as
 #>  Parameter estimates:
 #>      const       prop 
 #> 2.23223147 0.01262395 
-
-  f_2_obs <- update(f_2, error_model = "obs")
-  f_nlme_sfo_sfo_obs <- nlme(f_2_obs["SFO-SFO", ])
-  print(f_nlme_sfo_sfo_obs)
+
+  f_2_obs <- update(f_2, error_model = "obs")
+  f_nlme_sfo_sfo_obs <- nlme(f_2_obs["SFO-SFO", ])
+  print(f_nlme_sfo_sfo_obs)
 #> Kinetic nonlinear mixed-effects model fit by maximum likelihood
 #> 
 #> Structural model:
@@ -357,23 +396,23 @@ methods that will automatically work on 'nlme.mmkin' objects, such as
 #>  Parameter estimates:
 #>    parent        A1 
 #> 1.0000000 0.2049995 
-  f_nlme_dfop_sfo_obs <- nlme(f_2_obs["DFOP-SFO", ],
-    control = list(pnlsMaxIter = 120, tolerance = 5e-4))
-
-  f_2_tc <- update(f_2, error_model = "tc")
-  # f_nlme_sfo_sfo_tc <- nlme(f_2_tc["SFO-SFO", ]) # No convergence with 50 iterations
-  # f_nlme_dfop_sfo_tc <- nlme(f_2_tc["DFOP-SFO", ],
-  #  control = list(pnlsMaxIter = 120, tolerance = 5e-4)) # Error in X[, fmap[[nm]]] <- gradnm
-
-  anova(f_nlme_dfop_sfo, f_nlme_dfop_sfo_obs)
+  f_nlme_dfop_sfo_obs <- nlme(f_2_obs["DFOP-SFO", ],
+    control = list(pnlsMaxIter = 120, tolerance = 5e-4))
+
+  f_2_tc <- update(f_2, error_model = "tc")
+  # f_nlme_sfo_sfo_tc <- nlme(f_2_tc["SFO-SFO", ]) # No convergence with 50 iterations
+  # f_nlme_dfop_sfo_tc <- nlme(f_2_tc["DFOP-SFO", ],
+  #  control = list(pnlsMaxIter = 120, tolerance = 5e-4)) # Error in X[, fmap[[nm]]] <- gradnm
+
+  anova(f_nlme_dfop_sfo, f_nlme_dfop_sfo_obs)
 #>                     Model df      AIC      BIC    logLik   Test  L.Ratio
 #> f_nlme_dfop_sfo         1 13 843.8547 884.6201 -408.9274                
 #> f_nlme_dfop_sfo_obs     2 14 817.5338 861.4350 -394.7669 1 vs 2 28.32091
 #>                     p-value
 #> f_nlme_dfop_sfo            
 #> f_nlme_dfop_sfo_obs  <.0001
-
-# }
+
+# }
    @@ -388,7 +427,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as
    -

    Site built with pkgdown 2.0.2.

    +

    Site built with pkgdown 2.0.6.

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