From c6a1733974334b4e97a27170c60e481dc9e9f35d Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Fri, 11 Dec 2020 15:39:43 +0100 Subject: Update static docs --- docs/dev/reference/saem.html | 127 ++++++++++++++++++++++--------------------- 1 file changed, 65 insertions(+), 62 deletions(-) (limited to 'docs/dev/reference/saem.html') diff --git a/docs/dev/reference/saem.html b/docs/dev/reference/saem.html index e4ebf5d9..73699e19 100644 --- a/docs/dev/reference/saem.html +++ b/docs/dev/reference/saem.html @@ -151,11 +151,13 @@ effects models created from mmkin row objects using the Expectation Maximisation algorithm (SAEM).

- 
saem(object, control, ...)
+    
saem(object, ...)
 
 # S3 method for mmkin
 saem(
   object,
+  transformations = c("mkin", "saemix"),
+  solution_type = "auto",
   control = list(displayProgress = FALSE, print = FALSE, save = FALSE, save.graphs =
     FALSE),
   cores = 1,
@@ -168,7 +170,14 @@ Expectation Maximisation algorithm (SAEM).

 # S3 method for saem.mmkin
 print(x, digits = max(3, getOption("digits") - 3), ...)
 
-saemix_model(object, cores = 1, verbose = FALSE, ...)
+saemix_model(
+  object,
+  solution_type = "auto",
+  transformations = c("mkin", "saemix"),
+  cores = 1,
+  verbose = FALSE,
+  ...
+)
 
 saemix_data(object, verbose = FALSE, ...)

 
@@ -180,20 +189,26 @@ Expectation Maximisation algorithm (SAEM).

       
An mmkin row object containing several fits of the same
 mkinmod model to different datasets

     
-    
-      control
-      
Passed to saemix::saemix

-    
     
       ...
       
Further parameters passed to saemix::saemixModel.

     
     
-      cores
-      
The number of cores to be used for multicore processing using
-parallel::mclapply(). Using more than 1 core is experimental and may
-lead to excessive forking, apparently depending on the BLAS version
-used.

+      transformations
+      
Per default, all parameter transformations are done
+in mkin. If this argument is set to 'saemix', parameter transformations
+are done in 'saemix' for the supported cases. Currently this is only
+supported in cases where the initial concentration of the parent is not fixed,
+SFO or DFOP is used for the parent and there is either no metabolite or one.

+    
+    
+      solution_type
+      
Possibility to specify the solution type in case the
+automatic choice is not desired

+    
+    
+      control
+      
Passed to saemix::saemix

     
     
       verbose
@@ -243,31 +258,31 @@ using mmkin.

 ds <- lapply(experimental_data_for_UBA_2019[6:10],
  function(x) subset(x$data[c("name", "time", "value")]))
 names(ds) <- paste("Dataset", 6:10)
-f_mmkin_parent_p0_fixed <- mmkin("FOMC", ds, cores = 1,
+f_mmkin_parent_p0_fixed <- mmkin("FOMC", ds,
   state.ini = c(parent = 100), fixed_initials = "parent", quiet = TRUE)
 f_saem_p0_fixed <- saem(f_mmkin_parent_p0_fixed)
 
#> Running main SAEM algorithm
-#> [1] "Mon Nov 30 15:53:02 2020"
+#> [1] "Fri Dec 11 15:37:47 2020"
 #> ....
 #>     Minimisation finished
-#> [1] "Mon Nov 30 15:53:04 2020"

+#> [1] "Fri Dec 11 15:37:49 2020"

 f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE)
 f_saem_sfo <- saem(f_mmkin_parent["SFO", ])
 
#> Running main SAEM algorithm
-#> [1] "Mon Nov 30 15:53:05 2020"
+#> [1] "Fri Dec 11 15:37:51 2020"
 #> ....
 #>     Minimisation finished
-#> [1] "Mon Nov 30 15:53:07 2020"
f_saem_fomc <- saem(f_mmkin_parent["FOMC", ])
+#> [1] "Fri Dec 11 15:37:52 2020"
f_saem_fomc <- saem(f_mmkin_parent["FOMC", ])
 
#> Running main SAEM algorithm
-#> [1] "Mon Nov 30 15:53:07 2020"
+#> [1] "Fri Dec 11 15:37:52 2020"
 #> ....
 #>     Minimisation finished
-#> [1] "Mon Nov 30 15:53:09 2020"
f_saem_dfop <- saem(f_mmkin_parent["DFOP", ])
+#> [1] "Fri Dec 11 15:37:55 2020"
f_saem_dfop <- saem(f_mmkin_parent["DFOP", ])
 
#> Running main SAEM algorithm
-#> [1] "Mon Nov 30 15:53:10 2020"
+#> [1] "Fri Dec 11 15:37:55 2020"
 #> ....
 #>     Minimisation finished
-#> [1] "Mon Nov 30 15:53:13 2020"

+#> [1] "Fri Dec 11 15:37:58 2020"

 # The returned saem.mmkin object contains an SaemixObject, therefore we can use
 # functions from saemix
 library(saemix)
@@ -313,10 +328,10 @@ using mmkin.

 f_mmkin_parent_tc <- update(f_mmkin_parent, error_model = "tc")
 f_saem_fomc_tc <- saem(f_mmkin_parent_tc["FOMC", ])
 
#> Running main SAEM algorithm
-#> [1] "Mon Nov 30 15:53:15 2020"
+#> [1] "Fri Dec 11 15:38:01 2020"
 #> ....
 #>     Minimisation finished
-#> [1] "Mon Nov 30 15:53:20 2020"
compare.saemix(list(f_saem_fomc$so, f_saem_fomc_tc$so))
+#> [1] "Fri Dec 11 15:38:06 2020"
compare.saemix(list(f_saem_fomc$so, f_saem_fomc_tc$so))
 
#> Likelihoods computed by importance sampling 
#>        AIC      BIC
 #> 1 467.7096 464.9757
 #> 2 469.5208 466.3963

@@ -331,20 +346,21 @@ using mmkin.

 f_mmkin <- mmkin(list(
     "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo),
   ds, quiet = TRUE)
-# These take about five seconds each on this system, as we use
-# analytical solutions written for saemix. When using the analytical
-# solutions written for mkin this took around four minutes
+# saem fits of SFO-SFO and DFOP-SFO to these data take about five seconds
+# each on this system, as we use analytical solutions written for saemix.
+# When using the analytical solutions written for mkin this took around
+# four minutes
 f_saem_sfo_sfo <- saem(f_mmkin["SFO-SFO", ])
 
#> Running main SAEM algorithm
-#> [1] "Mon Nov 30 15:53:23 2020"
+#> [1] "Fri Dec 11 15:38:09 2020"
 #> ....
 #>     Minimisation finished
-#> [1] "Mon Nov 30 15:53:28 2020"
f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ])
+#> [1] "Fri Dec 11 15:38:14 2020"
f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ])
 
#> Running main SAEM algorithm
-#> [1] "Mon Nov 30 15:53:29 2020"
+#> [1] "Fri Dec 11 15:38:15 2020"
 #> ....
 #>     Minimisation finished
-#> [1] "Mon Nov 30 15:53:38 2020"
# We can use print, plot and summary methods to check the results
+#> [1] "Fri Dec 11 15:38:24 2020"
# We can use print, plot and summary methods to check the results
 print(f_saem_dfop_sfo)
 
#> Kinetic nonlinear mixed-effects model fit by SAEM
 #> Structural model:
@@ -387,8 +403,8 @@ using mmkin.

 
#> saemix version used for fitting:      3.1.9000 
 #> mkin version used for pre-fitting:  0.9.50.4 
 #> R version used for fitting:         4.0.3 
-#> Date of fit:     Mon Nov 30 15:53:38 2020 
-#> Date of summary: Mon Nov 30 15:53:39 2020 
+#> Date of fit:     Fri Dec 11 15:38:25 2020 
+#> Date of summary: Fri Dec 11 15:38:25 2020 
 #> 
 #> Equations:
 #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -403,15 +419,15 @@ using mmkin.

 #> 
 #> Model predictions using solution type analytical 
 #> 
-#> Fitted in 9.963 s using 300, 100 iterations
+#> Fitted in 10.096 s using 300, 100 iterations
 #> 
 #> Variance model: Constant variance 
 #> 
 #> Mean of starting values for individual parameters:
 #>        parent_0        log_k_A1 f_parent_qlogis          log_k1          log_k2 
-#>      93.8101519      -9.7647455      -0.9711148      -1.8799371      -4.2708142 
+#>         93.8102         -9.7647         -0.9711         -1.8799         -4.2708 
 #>        g_qlogis 
-#>       0.1356441 
+#>          0.1356 
 #> 
 #> Fixed degradation parameter values:
 #> None
@@ -422,7 +438,7 @@ using mmkin.

 #>     AIC   BIC logLik
 #>   841.6 836.5 -407.8
 #> 
-#> Optimised, transformed parameters with symmetric confidence intervals:
+#> Optimised parameters:
 #>                     est.  lower   upper
 #> parent_0        93.76647 91.153 96.3798
 #> log_k_A1        -6.13235 -8.458 -3.8068
@@ -452,7 +468,7 @@ using mmkin.

 #>      est. lower upper
 #> a.1 1.883 1.666   2.1
 #> 
-#> Backtransformed parameters with asymmetric confidence intervals:
+#> Backtransformed parameters:
 #>                     est.     lower    upper
 #> parent_0       93.766473 9.115e+01 96.37983
 #> k_A1            0.002171 2.122e-04  0.02222
@@ -643,32 +659,19 @@ using mmkin.

 #>  Dataset 10     A1   91     13.2  11.93773 -1.26227 1.883    -0.670224
 #>  Dataset 10     A1  120     14.3  12.77666 -1.52334 1.883    -0.808847
 #>  Dataset 10     A1  120     12.1  12.77666  0.67666 1.883     0.359282

-# Using a single core, the following takes about 6 minutes as we do not have an
-# analytical solution. Using 10 cores it is slower instead of faster
-f_saem_fomc <- saem(f_mmkin["FOMC-SFO", ], cores = 1)
-
#> Running main SAEM algorithm
-#> [1] "Mon Nov 30 15:53:39 2020"
-#> DLSODA-  At current T (=R1), MXSTEP (=I1) steps   
-#>       taken on this call before reaching TOUT     
-#> In above message, I1 = 5000
-#>  
-#> In above message, R1 = 0.00156238
-#>  
-#> DLSODA-  At T (=R1) and step size H (=R2), the    
-#>       corrector convergence failed repeatedly     
-#>       or with ABS(H) = HMIN   
-#> In above message, R1 = 0, R2 = 1.1373e-10
-#>  
-#> DLSODA-  At current T (=R1), MXSTEP (=I1) steps   
-#>       taken on this call before reaching TOUT     
-#> In above message, I1 = 5000
-#>  
-#> In above message, R1 = 2.24752e-06
-#>  
-#> ....
-#>     Minimisation finished
-#> [1] "Mon Nov 30 16:00:45 2020"
plot(f_saem_fomc)
-
# }
+# The following takes about 6 minutes
+#f_saem_dfop_sfo_deSolve <- saem(f_mmkin["DFOP-SFO", ], solution_type = "deSolve",
+#  control = list(nbiter.saemix = c(200, 80), nbdisplay = 10))
+
+#saemix::compare.saemix(list(
+#  f_saem_dfop_sfo$so,
+#  f_saem_dfop_sfo_deSolve$so))
+
+# If the model supports it, we can also use eigenvalue based solutions, which
+# take a similar amount of time
+#f_saem_sfo_sfo_eigen <- saem(f_mmkin["SFO-SFO", ], solution_type = "eigen",
+#  control = list(nbiter.saemix = c(200, 80), nbdisplay = 10))
+# }