From a5874ab7fce4616e80be69366ff0685332f47bf1 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Tue, 27 Oct 2020 15:34:14 +0100 Subject: Add summary method for nlme.mmkin objects Improve and update docs --- docs/dev/reference/Rplot001.png | Bin 27839 -> 14324 bytes docs/dev/reference/index.html | 8 +- docs/dev/reference/nlme.mmkin.html | 96 ++++--- docs/dev/reference/plot.nlme.mmkin.html | 14 +- docs/dev/reference/sigma_twocomp.html | 11 +- docs/dev/reference/summary.nlme.mmkin.html | 446 +++++++++++++++++++++++++++++ 6 files changed, 530 insertions(+), 45 deletions(-) create mode 100644 docs/dev/reference/summary.nlme.mmkin.html (limited to 'docs/dev/reference') diff --git a/docs/dev/reference/Rplot001.png b/docs/dev/reference/Rplot001.png index cfc5bc2b..f001da49 100644 Binary files a/docs/dev/reference/Rplot001.png and b/docs/dev/reference/Rplot001.png differ diff --git a/docs/dev/reference/index.html b/docs/dev/reference/index.html index 24056025..fa3ec868 100644 --- a/docs/dev/reference/index.html +++ b/docs/dev/reference/index.html @@ -308,7 +308,7 @@ of an mmkin object

Mixed models

-

Create and work with nonlinear mixed models

+

Create and work with nonlinear mixed effects models

@@ -330,6 +330,12 @@ of an mmkin object

Plot a fitted nonlinear mixed model obtained via an mmkin row object

+ +

summary(<nlme.mmkin>) print(<summary.nlme.mmkin>)

+ +

Summary method for class "nlme.mmkin"

+ +

nlme_function() mean_degparms() nlme_data()

diff --git a/docs/dev/reference/nlme.mmkin.html b/docs/dev/reference/nlme.mmkin.html index 90aec9be..3fa4d97b 100644 --- a/docs/dev/reference/nlme.mmkin.html +++ b/docs/dev/reference/nlme.mmkin.html @@ -152,7 +152,7 @@ have been obtained by fitting the same model to a list of datasets.

# S3 method for mmkin
-nlme(
+nlme(
   model,
   data = sys.frame(sys.parent()),
   fixed,
@@ -255,6 +255,11 @@ parameters taken from the mmkin object are used

 
     
Upon success, a fitted nlme.mmkin object, which is an nlme object
 with additional elements

+    
Note

+
+    
As the object inherits from nlme::nlme, there is a wealth of
+methods that will automatically work on 'nlme.mmkin' objects, such as
+nlme::intervals(), nlme::anova.lme() and nlme::coef.lme().

     
See also

 
     
nlme_function

@@ -270,11 +275,20 @@ with additional elements

 
#>             df      AIC
 #> f_nlme_sfo   5 625.0539
 #> f_nlme_dfop  9 495.1270
print(f_nlme_dfop)
-
#> Nonlinear mixed-effects model fit by maximum likelihood
-#>   Model: value ~ (mkin::get_deg_func())(name, time, parent_0, log_k1,      log_k2, g_ilr) 
-#>   Data: "Not shown" 
-#>   Log-likelihood: -238.5635
-#>   Fixed: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_ilr ~ 1) 
+
#> Kinetic nonlinear mixed-effects model fit by maximum likelihood
+#> 
+#> Structural model:
+#> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
+#>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
+#>            * parent
+#> 
+#> Data:
+#> 90 observations of 1 variable(s) grouped in 5 datasets
+#> 
+#> Log-likelihood: -238.5635
+#> 
+#> Fixed effects:
+#>  list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_ilr ~ 1) 
 #>    parent_0      log_k1      log_k2       g_ilr 
 #> 94.17015133 -1.80015306 -4.14738870  0.02290935 
 #> 
@@ -284,9 +298,7 @@ with additional elements

 #>  Structure: Diagonal
 #>         parent_0    log_k1  log_k2     g_ilr Residual
 #> StdDev: 2.488249 0.8447273 1.32965 0.3289311 2.321364
-#> 
-#> Number of Observations: 90
-#> Number of Groups: 5 
plot(f_nlme_dfop)
+#> 
plot(f_nlme_dfop)
 
endpoints(f_nlme_dfop)
 
#> $distimes
 #>            DT50     DT90 DT50back  DT50_k1  DT50_k2
@@ -295,11 +307,18 @@ with additional elements

 # \dontrun{
   f_nlme_2 <- nlme(f["SFO", ], start = c(parent_0 = 100, log_k_parent = 0.1))
   update(f_nlme_2, random = parent_0 ~ 1)
-
#> Nonlinear mixed-effects model fit by maximum likelihood
-#>   Model: value ~ (mkin::get_deg_func())(name, time, parent_0, log_k_parent) 
-#>   Data: "Not shown" 
-#>   Log-likelihood: -404.3729
-#>   Fixed: list(parent_0 ~ 1, log_k_parent ~ 1) 
+
#> Kinetic nonlinear mixed-effects model fit by maximum likelihood
+#> 
+#> Structural model:
+#> d_parent/dt = - k_parent * parent
+#> 
+#> Data:
+#>  observations of 0 variable(s) grouped in 0 datasets
+#> 
+#> Log-likelihood: -404.3729
+#> 
+#> Fixed effects:
+#>  list(parent_0 ~ 1, log_k_parent ~ 1) 
 #>     parent_0 log_k_parent 
 #>    75.933480    -3.555983 
 #> 
@@ -307,9 +326,7 @@ with additional elements

 #>  Formula: parent_0 ~ 1 | ds
 #>            parent_0 Residual
 #> StdDev: 0.002416792 21.63027
-#> 
-#> Number of Observations: 90
-#> Number of Groups: 5 
  ds_2 <- lapply(experimental_data_for_UBA_2019[6:10],
+#> 
  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)
@@ -395,11 +412,20 @@ with additional elements

     AIC(f_nlme_sfo, f_nlme_sfo_tc, f_nlme_dfop, f_nlme_dfop_tc)
     print(f_nlme_dfop_tc)
   }
-
#> Nonlinear mixed-effects model fit by maximum likelihood
-#>   Model: value ~ (mkin::get_deg_func())(name, time, parent_0, log_k1,      log_k2, g_ilr) 
-#>   Data: "Not shown" 
-#>   Log-likelihood: -238.4298
-#>   Fixed: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_ilr ~ 1) 
+
#> Kinetic nonlinear mixed-effects model fit by maximum likelihood
+#> 
+#> Structural model:
+#> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
+#>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
+#>            * parent
+#> 
+#> Data:
+#> 90 observations of 1 variable(s) grouped in 5 datasets
+#> 
+#> Log-likelihood: -238.4298
+#> 
+#> Fixed effects:
+#>  list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_ilr ~ 1) 
 #>    parent_0      log_k1      log_k2       g_ilr 
 #> 94.04774463 -1.82339924 -4.16715509  0.04020161 
 #> 
@@ -415,19 +441,25 @@ with additional elements

 #>  Formula: ~fitted(.) 
 #>  Parameter estimates:
 #>      const       prop 
-#> 2.23222625 0.01262414 
-#> Number of Observations: 90
-#> Number of Groups: 5 

+#> 2.23222625 0.01262414 

   f_2_obs <- mmkin(list("SFO-SFO" = m_sfo_sfo,
    "DFOP-SFO" = m_dfop_sfo),
     ds_2, quiet = TRUE, error_model = "obs")
   f_nlme_sfo_sfo_obs <- nlme(f_2_obs["SFO-SFO", ])
   print(f_nlme_sfo_sfo_obs)
-
#> Nonlinear mixed-effects model fit by maximum likelihood
-#>   Model: value ~ (mkin::get_deg_func())(name, time, parent_0, log_k_parent_sink,      log_k_parent_A1, log_k_A1_sink) 
-#>   Data: "Not shown" 
-#>   Log-likelihood: -472.976
-#>   Fixed: list(parent_0 ~ 1, log_k_parent_sink ~ 1, log_k_parent_A1 ~ 1,      log_k_A1_sink ~ 1) 
+
#> Kinetic nonlinear mixed-effects model fit by maximum likelihood
+#> 
+#> Structural model:
+#> d_parent/dt = - k_parent_sink * parent - k_parent_A1 * parent
+#> d_A1/dt = + k_parent_A1 * parent - k_A1_sink * A1
+#> 
+#> Data:
+#> 170 observations of 2 variable(s) grouped in 5 datasets
+#> 
+#> Log-likelihood: -472.976
+#> 
+#> Fixed effects:
+#>  list(parent_0 ~ 1, log_k_parent_sink ~ 1, log_k_parent_A1 ~ 1,      log_k_A1_sink ~ 1) 
 #>          parent_0 log_k_parent_sink   log_k_parent_A1     log_k_A1_sink 
 #>         87.975536         -3.669816         -4.164127         -4.645073 
 #> 
@@ -443,9 +475,7 @@ with additional elements

 #>  Formula: ~1 | name 
 #>  Parameter estimates:
 #>    parent        A1 
-#> 1.0000000 0.2050003 
-#> Number of Observations: 170
-#> Number of Groups: 5 
  # The same with DFOP-SFO does not converge, apparently the variances of
+#> 1.0000000 0.2050003 
  # The same with DFOP-SFO does not converge, apparently the variances of
   # parent and A1 are too similar in this case, so that the model is
   # overparameterised
   #f_nlme_dfop_sfo_obs <- nlme(f_2_obs["DFOP-SFO", ], control = list(maxIter = 100))
diff --git a/docs/dev/reference/plot.nlme.mmkin.html b/docs/dev/reference/plot.nlme.mmkin.html
index afd9d8d0..267bef05 100644
--- a/docs/dev/reference/plot.nlme.mmkin.html
+++ b/docs/dev/reference/plot.nlme.mmkin.html
@@ -214,6 +214,14 @@ predicted values?

       
Maximum absolute value of the residuals. This is used for the
 scaling of the y axis and defaults to "auto".

     
+    
+      ncol.legend
+      
Number of columns to use in the legend

+    
+    
+      nrow.legend
+      
Number of rows to use in the legend

+    
     
       rel.height.legend
       
The relative height of the legend shown on top

@@ -259,7 +267,8 @@ corresponding model prediction lines for the different datasets.

 names(ds) <- paste0("ds ", 6:10)
 dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"),
   A1 = mkinsub("SFO"), quiet = TRUE)
-f <- mmkin(list("DFOP-SFO" = dfop_sfo), ds, quiet = TRUE, cores = 1)
+# \dontrun{
+f <- mmkin(list("DFOP-SFO" = dfop_sfo), ds, quiet = TRUE)
 plot(f[, 3:4], standardized = TRUE)
 

 library(nlme)
@@ -267,7 +276,8 @@ corresponding model prediction lines for the different datasets.

 # tolerance in order to speed up the fit for this example evaluation
 f_nlme <- nlme(f, control = list(pnlsMaxIter = 120, tolerance = 1e-3))
 plot(f_nlme)
-
+
# } +