From 3dde3b95f1db925c89cd04d19f95c6fc9f68f473 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Sat, 13 Feb 2021 12:40:44 +0100 Subject: Update docs --- docs/dev/reference/saem.html | 38 +++++++++++++++++++------------------- 1 file changed, 19 insertions(+), 19 deletions(-) (limited to 'docs/dev/reference/saem.html') diff --git a/docs/dev/reference/saem.html b/docs/dev/reference/saem.html index 4578db2a..02483bec 100644 --- a/docs/dev/reference/saem.html +++ b/docs/dev/reference/saem.html @@ -74,7 +74,7 @@ Expectation Maximisation algorithm (SAEM)." /> mkin - 1.0.1.9000 + 1.0.2.9000 @@ -261,27 +261,27 @@ using mmkin.

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] "Sat Feb 6 18:29:26 2021" +#> [1] "Sat Feb 13 12:33:16 2021" #> .... #> Minimisation finished -#> [1] "Sat Feb 6 18:29:27 2021"
+#> [1] "Sat Feb 13 12:33:18 2021"
f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE) f_saem_sfo <- saem(f_mmkin_parent["SFO", ])
#> Running main SAEM algorithm -#> [1] "Sat Feb 6 18:29:28 2021" +#> [1] "Sat Feb 13 12:33:19 2021" #> .... #> Minimisation finished -#> [1] "Sat Feb 6 18:29:30 2021"
f_saem_fomc <- saem(f_mmkin_parent["FOMC", ]) +#> [1] "Sat Feb 13 12:33:20 2021"
f_saem_fomc <- saem(f_mmkin_parent["FOMC", ])
#> Running main SAEM algorithm -#> [1] "Sat Feb 6 18:29:30 2021" +#> [1] "Sat Feb 13 12:33:20 2021" #> .... #> Minimisation finished -#> [1] "Sat Feb 6 18:29:32 2021"
f_saem_dfop <- saem(f_mmkin_parent["DFOP", ]) +#> [1] "Sat Feb 13 12:33:22 2021"
f_saem_dfop <- saem(f_mmkin_parent["DFOP", ])
#> Running main SAEM algorithm -#> [1] "Sat Feb 6 18:29:32 2021" +#> [1] "Sat Feb 13 12:33:23 2021" #> .... #> Minimisation finished -#> [1] "Sat Feb 6 18:29:35 2021"
+#> [1] "Sat Feb 13 12:33:25 2021"
# The returned saem.mmkin object contains an SaemixObject, therefore we can use # functions from saemix library(saemix) @@ -324,10 +324,10 @@ 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] "Sat Feb 6 18:29:37 2021" +#> [1] "Sat Feb 13 12:33:28 2021" #> .... #> Minimisation finished -#> [1] "Sat Feb 6 18:29:42 2021"
compare.saemix(list(f_saem_fomc$so, f_saem_fomc_tc$so)) +#> [1] "Sat Feb 13 12:33:32 2021"
compare.saemix(list(f_saem_fomc$so, f_saem_fomc_tc$so))
#> Error in compare.saemix(list(f_saem_fomc$so, f_saem_fomc_tc$so)): 'compare.saemix' requires at least two models.
sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"), A1 = mkinsub("SFO")) @@ -346,15 +346,15 @@ using mmkin.

# four minutes f_saem_sfo_sfo <- saem(f_mmkin["SFO-SFO", ])
#> Running main SAEM algorithm -#> [1] "Sat Feb 6 18:29:44 2021" +#> [1] "Sat Feb 13 12:33:35 2021" #> .... #> Minimisation finished -#> [1] "Sat Feb 6 18:29:48 2021"
f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ]) +#> [1] "Sat Feb 13 12:33:39 2021"
f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ])
#> Running main SAEM algorithm -#> [1] "Sat Feb 6 18:29:49 2021" +#> [1] "Sat Feb 13 12:33:40 2021" #> .... #> Minimisation finished -#> [1] "Sat Feb 6 18:29:57 2021"
# We can use print, plot and summary methods to check the results +#> [1] "Sat Feb 13 12:33:48 2021"
# 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: @@ -395,10 +395,10 @@ using mmkin.

#> SD.g_qlogis 0.44771 -0.86417 1.7596
plot(f_saem_dfop_sfo)
summary(f_saem_dfop_sfo, data = TRUE)
#> saemix version used for fitting: 3.1.9000 -#> mkin version used for pre-fitting: 1.0.1.9000 +#> mkin version used for pre-fitting: 1.0.2.9000 #> R version used for fitting: 4.0.3 -#> Date of fit: Sat Feb 6 18:29:57 2021 -#> Date of summary: Sat Feb 6 18:29:58 2021 +#> Date of fit: Sat Feb 13 12:33:48 2021 +#> Date of summary: Sat Feb 13 12:33:48 2021 #> #> Equations: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -413,7 +413,7 @@ using mmkin.

#> #> Model predictions using solution type analytical #> -#> Fitted in 8.539 s using 300, 100 iterations +#> Fitted in 8.875 s using 300, 100 iterations #> #> Variance model: Constant variance #> -- cgit v1.2.1