From aa74f5a30853fb0a15c99c283e072f08ee819149 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Mon, 9 Nov 2020 17:24:53 +0100 Subject: saemix.mmkin and nlme.mmkin inherit from mixed.mmkin With a plot method. The class mixed.mmkin is currently only a virtual class created to unify the plotting method. --- docs/dev/reference/nlme.mmkin.html | 61 +++++++++++++++++++------------------- 1 file changed, 31 insertions(+), 30 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 c3cc53ff..defef75d 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,
@@ -253,8 +253,8 @@ parameters taken from the mmkin object are used

 
     
Value

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

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

     
Note

 
     
As the object inherits from nlme::nlme, there is a wealth of
@@ -262,7 +262,8 @@ methods that will automatically work on 'nlme.mmkin' objects, such as
 nlme::intervals(), nlme::anova.lme() and nlme::coef.lme().

     
See also

 
-    
nlme_function()

+    
nlme_function(), plot.mixed.mmkin, summary.nlme.mmkin,
+parms.nlme.mmkin

 
     
Examples

     
ds <- lapply(experimental_data_for_UBA_2019[6:10],
@@ -288,21 +289,21 @@ methods that will automatically work on 'nlme.mmkin' objects, such as
 #> 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 
+#>  list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) 
+#>    parent_0      log_k1      log_k2    g_qlogis 
+#> 94.17015185 -1.80015278 -4.14738834  0.03239833 
 #> 
 #> Random effects:
-#>  Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_ilr ~ 1)
+#>  Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1)
 #>  Level: ds
 #>  Structure: Diagonal
-#>         parent_0    log_k1  log_k2     g_ilr Residual
-#> StdDev: 2.488249 0.8447273 1.32965 0.3289311 2.321364
+#>         parent_0    log_k1  log_k2  g_qlogis Residual
+#> StdDev: 2.488249 0.8447275 1.32965 0.4651789 2.321364
 #> 
plot(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
+#> parent 10.79857 100.7937 30.34192 4.193937 43.85442
 #> 

 # \dontrun{
   f_nlme_2 <- nlme(f["SFO", ], start = c(parent_0 = 100, log_k_parent = 0.1))
@@ -353,35 +354,35 @@ methods that will automatically work on 'nlme.mmkin' objects, such as
     control = list(pnlsMaxIter = 120, tolerance = 5e-4), verbose = TRUE)
 
#> 
 #> **Iteration 1
-#> LME step: Loglik: -404.9582, nlminb iterations: 1
+#> LME step: Loglik: -404.9583, nlminb iterations: 1
 #> reStruct  parameters:
 #>        ds1        ds2        ds3        ds4        ds5        ds6 
-#> -0.4114355  0.9798697  1.6990037  0.7293315  0.3354323  1.7113046 
+#> -0.4114356  0.9798646  1.3524300  0.7293315  0.3354323  1.3647313 
 #>  Beginning PNLS step: ..  completed fit_nlme() step.
-#> PNLS step: RSS =  630.3644 
-#>  fixed effects: 93.82269  -5.455991  -0.6788957  -1.862196  -4.199671  0.05532828  
+#> PNLS step: RSS =  630.3633 
+#>  fixed effects: 93.82269  -5.455993  -0.9601037  -1.862196  -4.199671  0.07824609  
 #>  iterations: 120 
 #> Convergence crit. (must all become <= tolerance = 0.0005):
 #>     fixed  reStruct 
-#> 0.7885368 0.5822683 
+#> 0.7897284 0.5822782 
 #> 
 #> **Iteration 2
 #> LME step: Loglik: -407.7755, nlminb iterations: 11
 #> reStruct  parameters:
-#>          ds1          ds2          ds3          ds4          ds5          ds6 
-#> -0.371224133  0.003056179  1.789939402  0.724671158  0.301602977  1.754200729 
+#>         ds1         ds2         ds3         ds4         ds5         ds6 
+#> -0.37122411  0.00305562  1.44336560  0.72467122  0.30160310  1.40762692 
 #>  Beginning PNLS step: ..  completed fit_nlme() step.
-#> PNLS step: RSS =  630.3633 
-#>  fixed effects: 93.82269  -5.455992  -0.6788958  -1.862196  -4.199671  0.05532831  
+#> PNLS step: RSS =  630.3637 
+#>  fixed effects: 93.82269  -5.455992  -0.9601036  -1.862196  -4.199671  0.0782462  
 #>  iterations: 120 
 #> Convergence crit. (must all become <= tolerance = 0.0005):
 #>        fixed     reStruct 
-#> 4.789774e-07 2.200661e-05 

+#> 1.375673e-06 9.758294e-06 

   plot(f_nlme_dfop_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 -408.9273                        
+#> 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)
 
#> $ff
@@ -400,7 +401,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as
 #> $distimes
 #>             DT50     DT90 DT50back  DT50_k1  DT50_k2
 #> parent  11.07091 104.6320 31.49738 4.462384 46.20825
-#> A1     162.30536 539.1667       NA       NA       NA
+#> A1     162.30523 539.1663       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,
@@ -425,23 +426,23 @@ methods that will automatically work on 'nlme.mmkin' objects, such as
 #> 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 
+#>  list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) 
+#>    parent_0      log_k1      log_k2    g_qlogis 
+#> 94.04774566 -1.82339808 -4.16715311  0.05685186 
 #> 
 #> Random effects:
-#>  Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_ilr ~ 1)
+#>  Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1)
 #>  Level: ds
 #>  Structure: Diagonal
-#>         parent_0    log_k1   log_k2     g_ilr Residual
-#> StdDev: 2.473883 0.8499901 1.337187 0.3294411        1
+#>         parent_0    log_k1   log_k2  g_qlogis Residual
+#> StdDev: 2.473881 0.8499884 1.337185 0.4659005        1
 #> 
 #> Variance function:
 #>  Structure: Constant plus proportion of variance covariate
 #>  Formula: ~fitted(.) 
 #>  Parameter estimates:
 #>      const       prop 
-#> 2.23222625 0.01262414 

+#> 2.23224114 0.01262341 

   f_2_obs <- mmkin(list("SFO-SFO" = m_sfo_sfo,
    "DFOP-SFO" = m_dfop_sfo),
     ds_2, quiet = TRUE, error_model = "obs")
-- 
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