From 48c463680b51fa767b4cd7bd62865f192d0354ac Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Sat, 6 Feb 2021 18:30:32 +0100 Subject: Reintroduce interface to saemix Also after the upgrade from buster to bullseye of my local system, some test results for saemix have changed. --- docs/dev/reference/saem.html | 56 ++++++++++++++++++++++---------------------- 1 file changed, 28 insertions(+), 28 deletions(-) (limited to 'docs/dev/reference/saem.html') diff --git a/docs/dev/reference/saem.html b/docs/dev/reference/saem.html index 59589378..4578db2a 100644 --- a/docs/dev/reference/saem.html +++ b/docs/dev/reference/saem.html @@ -74,7 +74,7 @@ Expectation Maximisation algorithm (SAEM)." /> mkin - 0.9.50.4 + 1.0.1.9000 @@ -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] "Mon Jan 25 14:41:42 2021" +#> [1] "Sat Feb 6 18:29:26 2021" #> .... #> Minimisation finished -#> [1] "Mon Jan 25 14:41:43 2021"
+#> [1] "Sat Feb 6 18:29:27 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] "Mon Jan 25 14:41:45 2021" +#> [1] "Sat Feb 6 18:29:28 2021" #> .... #> Minimisation finished -#> [1] "Mon Jan 25 14:41:46 2021"
f_saem_fomc <- saem(f_mmkin_parent["FOMC", ]) +#> [1] "Sat Feb 6 18:29:30 2021"
f_saem_fomc <- saem(f_mmkin_parent["FOMC", ])
#> Running main SAEM algorithm -#> [1] "Mon Jan 25 14:41:47 2021" +#> [1] "Sat Feb 6 18:29:30 2021" #> .... #> Minimisation finished -#> [1] "Mon Jan 25 14:41:49 2021"
f_saem_dfop <- saem(f_mmkin_parent["DFOP", ]) +#> [1] "Sat Feb 6 18:29:32 2021"
f_saem_dfop <- saem(f_mmkin_parent["DFOP", ])
#> Running main SAEM algorithm -#> [1] "Mon Jan 25 14:41:49 2021" +#> [1] "Sat Feb 6 18:29:32 2021" #> .... #> Minimisation finished -#> [1] "Mon Jan 25 14:41:52 2021"
+#> [1] "Sat Feb 6 18:29:35 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] "Mon Jan 25 14:41:55 2021" +#> [1] "Sat Feb 6 18:29:37 2021" #> .... #> Minimisation finished -#> [1] "Mon Jan 25 14:42:00 2021"
compare.saemix(list(f_saem_fomc$so, f_saem_fomc_tc$so)) +#> [1] "Sat Feb 6 18:29:42 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] "Mon Jan 25 14:42:02 2021" +#> [1] "Sat Feb 6 18:29:44 2021" #> .... #> Minimisation finished -#> [1] "Mon Jan 25 14:42:07 2021"
f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ]) +#> [1] "Sat Feb 6 18:29:48 2021"
f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ])
#> Running main SAEM algorithm -#> [1] "Mon Jan 25 14:42:08 2021" +#> [1] "Sat Feb 6 18:29:49 2021" #> .... #> Minimisation finished -#> [1] "Mon Jan 25 14:42:17 2021"
# We can use print, plot and summary methods to check the results +#> [1] "Sat Feb 6 18:29:57 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: 0.9.50.4 +#> mkin version used for pre-fitting: 1.0.1.9000 #> R version used for fitting: 4.0.3 -#> Date of fit: Mon Jan 25 14:42:18 2021 -#> Date of summary: Mon Jan 25 14:42:18 2021 +#> Date of fit: Sat Feb 6 18:29:57 2021 +#> Date of summary: Sat Feb 6 18:29:58 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 9.954 s using 300, 100 iterations +#> Fitted in 8.539 s using 300, 100 iterations #> #> Variance model: Constant variance #> @@ -489,12 +489,12 @@ using mmkin.

#> Dataset 6 parent 3 69.2 71.32042 2.12042 1.883 1.125873 #> Dataset 6 parent 6 58.1 56.45256 -1.64744 1.883 -0.874739 #> Dataset 6 parent 6 56.6 56.45256 -0.14744 1.883 -0.078288 -#> Dataset 6 parent 10 44.4 44.48523 0.08523 1.883 0.045256 +#> Dataset 6 parent 10 44.4 44.48523 0.08523 1.883 0.045257 #> Dataset 6 parent 10 43.4 44.48523 1.08523 1.883 0.576224 #> Dataset 6 parent 20 33.3 29.75774 -3.54226 1.883 -1.880826 #> Dataset 6 parent 20 29.2 29.75774 0.55774 1.883 0.296141 #> Dataset 6 parent 34 17.6 19.35710 1.75710 1.883 0.932966 -#> Dataset 6 parent 34 18.0 19.35710 1.35710 1.883 0.720578 +#> Dataset 6 parent 34 18.0 19.35710 1.35710 1.883 0.720579 #> Dataset 6 parent 55 10.5 10.48443 -0.01557 1.883 -0.008266 #> Dataset 6 parent 55 9.3 10.48443 1.18443 1.883 0.628895 #> Dataset 6 parent 90 4.5 3.78622 -0.71378 1.883 -0.378995 @@ -560,9 +560,9 @@ using mmkin.

#> Dataset 8 parent 1 64.9 67.73197 2.83197 1.883 1.503686 #> Dataset 8 parent 1 66.2 67.73197 1.53197 1.883 0.813428 #> Dataset 8 parent 3 43.5 41.58448 -1.91552 1.883 -1.017081 -#> Dataset 8 parent 3 44.1 41.58448 -2.51552 1.883 -1.335661 +#> Dataset 8 parent 3 44.1 41.58448 -2.51552 1.883 -1.335662 #> Dataset 8 parent 8 18.3 19.62286 1.32286 1.883 0.702395 -#> Dataset 8 parent 8 18.1 19.62286 1.52286 1.883 0.808589 +#> Dataset 8 parent 8 18.1 19.62286 1.52286 1.883 0.808588 #> Dataset 8 parent 14 10.2 10.77819 0.57819 1.883 0.306999 #> Dataset 8 parent 14 10.8 10.77819 -0.02181 1.883 -0.011582 #> Dataset 8 parent 27 4.9 3.26977 -1.63023 1.883 -0.865599 @@ -575,13 +575,13 @@ using mmkin.

#> Dataset 8 A1 1 7.7 7.61539 -0.08461 1.883 -0.044923 #> Dataset 8 A1 3 15.0 15.47954 0.47954 1.883 0.254622 #> Dataset 8 A1 3 15.1 15.47954 0.37954 1.883 0.201525 -#> Dataset 8 A1 8 21.2 20.22616 -0.97384 1.883 -0.517076 +#> Dataset 8 A1 8 21.2 20.22616 -0.97384 1.883 -0.517075 #> Dataset 8 A1 8 21.1 20.22616 -0.87384 1.883 -0.463979 #> Dataset 8 A1 14 19.7 20.00067 0.30067 1.883 0.159645 #> Dataset 8 A1 14 18.9 20.00067 1.10067 1.883 0.584419 -#> Dataset 8 A1 27 17.5 16.38142 -1.11858 1.883 -0.593929 -#> Dataset 8 A1 27 15.9 16.38142 0.48142 1.883 0.255619 -#> Dataset 8 A1 48 9.5 10.25357 0.75357 1.883 0.400123 +#> Dataset 8 A1 27 17.5 16.38142 -1.11858 1.883 -0.593928 +#> Dataset 8 A1 27 15.9 16.38142 0.48142 1.883 0.255620 +#> Dataset 8 A1 48 9.5 10.25357 0.75357 1.883 0.400124 #> Dataset 8 A1 48 9.8 10.25357 0.45357 1.883 0.240833 #> Dataset 8 A1 70 6.2 5.95728 -0.24272 1.883 -0.128878 #> Dataset 8 A1 70 6.1 5.95728 -0.14272 1.883 -0.075781 @@ -622,7 +622,7 @@ using mmkin.

#> Dataset 9 A1 91 10.0 10.09177 0.09177 1.883 0.048727 #> Dataset 9 A1 91 9.5 10.09177 0.59177 1.883 0.314211 #> Dataset 9 A1 120 9.1 7.91379 -1.18621 1.883 -0.629841 -#> Dataset 9 A1 120 9.0 7.91379 -1.08621 1.883 -0.576745 +#> Dataset 9 A1 120 9.0 7.91379 -1.08621 1.883 -0.576744 #> Dataset 10 parent 0 96.1 93.65257 -2.44743 1.883 -1.299505 #> Dataset 10 parent 0 94.3 93.65257 -0.64743 1.883 -0.343763 #> Dataset 10 parent 8 73.9 77.85906 3.95906 1.883 2.102132 -- cgit v1.2.1 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 From b9be19af5e3085216d0cd5af439332f631fa8b92 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Mon, 15 Feb 2021 17:36:12 +0100 Subject: Fully rebuild docs, rerun tests and check --- 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 02483bec..bdb1226e 100644 --- a/docs/dev/reference/saem.html +++ b/docs/dev/reference/saem.html @@ -74,7 +74,7 @@ Expectation Maximisation algorithm (SAEM)." /> mkin - 1.0.2.9000 + 1.0.3.9000
@@ -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 13 12:33:16 2021" +#> [1] "Mon Feb 15 17:12:32 2021" #> .... #> Minimisation finished -#> [1] "Sat Feb 13 12:33:18 2021"
+#> [1] "Mon Feb 15 17:12:34 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 13 12:33:19 2021" +#> [1] "Mon Feb 15 17:12:35 2021" #> .... #> Minimisation finished -#> [1] "Sat Feb 13 12:33:20 2021"
f_saem_fomc <- saem(f_mmkin_parent["FOMC", ]) +#> [1] "Mon Feb 15 17:12:36 2021"
f_saem_fomc <- saem(f_mmkin_parent["FOMC", ])
#> Running main SAEM algorithm -#> [1] "Sat Feb 13 12:33:20 2021" +#> [1] "Mon Feb 15 17:12:36 2021" #> .... #> Minimisation finished -#> [1] "Sat Feb 13 12:33:22 2021"
f_saem_dfop <- saem(f_mmkin_parent["DFOP", ]) +#> [1] "Mon Feb 15 17:12:38 2021"
f_saem_dfop <- saem(f_mmkin_parent["DFOP", ])
#> Running main SAEM algorithm -#> [1] "Sat Feb 13 12:33:23 2021" +#> [1] "Mon Feb 15 17:12:39 2021" #> .... #> Minimisation finished -#> [1] "Sat Feb 13 12:33:25 2021"
+#> [1] "Mon Feb 15 17:12:42 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 13 12:33:28 2021" +#> [1] "Mon Feb 15 17:12:44 2021" #> .... #> Minimisation finished -#> [1] "Sat Feb 13 12:33:32 2021"
compare.saemix(list(f_saem_fomc$so, f_saem_fomc_tc$so)) +#> [1] "Mon Feb 15 17:12:49 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 13 12:33:35 2021" +#> [1] "Mon Feb 15 17:12:51 2021" #> .... #> Minimisation finished -#> [1] "Sat Feb 13 12:33:39 2021"
f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ]) +#> [1] "Mon Feb 15 17:12:56 2021"
f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ])
#> Running main SAEM algorithm -#> [1] "Sat Feb 13 12:33:40 2021" +#> [1] "Mon Feb 15 17:12:56 2021" #> .... #> Minimisation finished -#> [1] "Sat Feb 13 12:33:48 2021"
# We can use print, plot and summary methods to check the results +#> [1] "Mon Feb 15 17:13:05 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: 1.0.2.9000 +#> mkin version used for pre-fitting: 1.0.3.9000 #> R version used for fitting: 4.0.3 -#> Date of fit: Sat Feb 13 12:33:48 2021 -#> Date of summary: Sat Feb 13 12:33:48 2021 +#> Date of fit: Mon Feb 15 17:13:05 2021 +#> Date of summary: Mon Feb 15 17:13:06 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 8.875 s using 300, 100 iterations +#> Fitted in 8.985 s using 300, 100 iterations #> #> Variance model: Constant variance #> -- cgit v1.2.1 From c73b2f30ec836c949885784ab576e814eb8070a9 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Tue, 9 Mar 2021 17:35:47 +0100 Subject: Some improvements for borderline cases - fit_with_errors for saem() - test_log_parms for mean_degparms() and saem() --- docs/dev/reference/saem.html | 72 ++++++++++++++++++++++++++++++-------------- 1 file changed, 49 insertions(+), 23 deletions(-) (limited to 'docs/dev/reference/saem.html') diff --git a/docs/dev/reference/saem.html b/docs/dev/reference/saem.html index bdb1226e..23102df3 100644 --- a/docs/dev/reference/saem.html +++ b/docs/dev/reference/saem.html @@ -74,7 +74,7 @@ Expectation Maximisation algorithm (SAEM)." /> mkin - 1.0.3.9000 + 1.0.4.9000
@@ -158,9 +158,12 @@ Expectation Maximisation algorithm (SAEM).

object, transformations = c("mkin", "saemix"), degparms_start = numeric(), + test_log_parms = FALSE, + conf.level = 0.6, solution_type = "auto", control = list(displayProgress = FALSE, print = FALSE, save = FALSE, save.graphs = FALSE), + fail_with_errors = TRUE, verbose = FALSE, quiet = FALSE, ... @@ -174,6 +177,7 @@ Expectation Maximisation algorithm (SAEM).

solution_type = "auto", transformations = c("mkin", "saemix"), degparms_start = numeric(), + test_log_parms = FALSE, verbose = FALSE, ... ) @@ -204,6 +208,18 @@ SFO or DFOP is used for the parent and there is either no metabolite or one.

degparms_start

Parameter values given as a named numeric vector will be used to override the starting values obtained from the 'mmkin' object.

+ + + test_log_parms +

If TRUE, an attempt is made to use more robust starting +values for population parameters fitted as log parameters in mkin (like +rate constants) by only considering rate constants that pass the t-test +when calculating mean degradation parameters using mean_degparms.

+ + + conf.level +

Possibility to adjust the required confidence level +for parameter that are tested if requested by 'test_log_parms'.

solution_type @@ -214,6 +230,11 @@ automatic choice is not desired

control

Passed to saemix::saemix

+ + fail_with_errors +

Should a failure to compute standard errors +from the inverse of the Fisher Information Matrix be a failure?

+ verbose

Should we print information about created objects of @@ -261,33 +282,36 @@ 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] "Mon Feb 15 17:12:32 2021" +#> [1] "Tue Mar 9 17:34:44 2021" #> .... #> Minimisation finished -#> [1] "Mon Feb 15 17:12:34 2021"
+#> [1] "Tue Mar 9 17:34:45 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] "Mon Feb 15 17:12:35 2021" +#> [1] "Tue Mar 9 17:34:46 2021" #> .... #> Minimisation finished -#> [1] "Mon Feb 15 17:12:36 2021"
f_saem_fomc <- saem(f_mmkin_parent["FOMC", ]) +#> [1] "Tue Mar 9 17:34:48 2021"
f_saem_fomc <- saem(f_mmkin_parent["FOMC", ])
#> Running main SAEM algorithm -#> [1] "Mon Feb 15 17:12:36 2021" +#> [1] "Tue Mar 9 17:34:48 2021" #> .... #> Minimisation finished -#> [1] "Mon Feb 15 17:12:38 2021"
f_saem_dfop <- saem(f_mmkin_parent["DFOP", ]) +#> [1] "Tue Mar 9 17:34:50 2021"
f_saem_dfop <- saem(f_mmkin_parent["DFOP", ])
#> Running main SAEM algorithm -#> [1] "Mon Feb 15 17:12:39 2021" +#> [1] "Tue Mar 9 17:34:51 2021" #> .... #> Minimisation finished -#> [1] "Mon Feb 15 17:12:42 2021"
+#> [1] "Tue Mar 9 17:34:53 2021"
# The returned saem.mmkin object contains an SaemixObject, therefore we can use # functions from saemix library(saemix)
#> Package saemix, version 3.1.9000 -#> please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr
compare.saemix(list(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so)) -
#> Error in compare.saemix(list(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so)): 'compare.saemix' requires at least two models.
plot(f_saem_fomc$so, plot.type = "convergence") +#> please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr
compare.saemix(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so) +
#> Likelihoods calculated by importance sampling
#> AIC BIC +#> 1 624.2484 622.2956 +#> 2 467.7096 464.9757 +#> 3 495.4373 491.9222
plot(f_saem_fomc$so, plot.type = "convergence")
#> Plotting convergence plots
plot(f_saem_fomc$so, plot.type = "individual.fit")
#> Plotting individual fits
plot(f_saem_fomc$so, plot.type = "npde")
#> Simulating data using nsim = 1000 simulated datasets @@ -324,11 +348,13 @@ 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 Feb 15 17:12:44 2021" +#> [1] "Tue Mar 9 17:34:55 2021" #> .... #> Minimisation finished -#> [1] "Mon Feb 15 17:12:49 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.
+#> [1] "Tue Mar 9 17:35:00 2021"
compare.saemix(f_saem_fomc$so, f_saem_fomc_tc$so) +
#> Likelihoods calculated by importance sampling
#> AIC BIC +#> 1 467.7096 464.9757 +#> 2 469.6831 466.5586
sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"), A1 = mkinsub("SFO"))
#> Temporary DLL for differentials generated and loaded
fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"), @@ -346,15 +372,15 @@ using mmkin.

# four minutes f_saem_sfo_sfo <- saem(f_mmkin["SFO-SFO", ])
#> Running main SAEM algorithm -#> [1] "Mon Feb 15 17:12:51 2021" +#> [1] "Tue Mar 9 17:35:02 2021" #> .... #> Minimisation finished -#> [1] "Mon Feb 15 17:12:56 2021"
f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ]) +#> [1] "Tue Mar 9 17:35:07 2021"
f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ])
#> Running main SAEM algorithm -#> [1] "Mon Feb 15 17:12:56 2021" +#> [1] "Tue Mar 9 17:35:07 2021" #> .... #> Minimisation finished -#> [1] "Mon Feb 15 17:13:05 2021"
# We can use print, plot and summary methods to check the results +#> [1] "Tue Mar 9 17:35:15 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 +421,10 @@ 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: 1.0.3.9000 -#> R version used for fitting: 4.0.3 -#> Date of fit: Mon Feb 15 17:13:05 2021 -#> Date of summary: Mon Feb 15 17:13:06 2021 +#> mkin version used for pre-fitting: 1.0.4.9000 +#> R version used for fitting: 4.0.4 +#> Date of fit: Tue Mar 9 17:35:16 2021 +#> Date of summary: Tue Mar 9 17:35:16 2021 #> #> Equations: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -413,7 +439,7 @@ using mmkin.

#> #> Model predictions using solution type analytical #> -#> Fitted in 8.985 s using 300, 100 iterations +#> Fitted in 8.668 s using 300, 100 iterations #> #> Variance model: Constant variance #> -- cgit v1.2.1 From 0c9b2f0e3c8ce65cb790c9e048476784cbbea070 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Fri, 11 Jun 2021 11:14:45 +0200 Subject: Finished 'summary.nlmixr.mmkin', checks, docs --- docs/dev/reference/saem.html | 399 ++++++++++++++++++++++--------------------- 1 file changed, 204 insertions(+), 195 deletions(-) (limited to 'docs/dev/reference/saem.html') diff --git a/docs/dev/reference/saem.html b/docs/dev/reference/saem.html index 23102df3..98faad6f 100644 --- a/docs/dev/reference/saem.html +++ b/docs/dev/reference/saem.html @@ -74,7 +74,7 @@ Expectation Maximisation algorithm (SAEM)." /> mkin - 1.0.4.9000 + 1.0.5
@@ -161,8 +161,9 @@ Expectation Maximisation algorithm (SAEM).

test_log_parms = FALSE, conf.level = 0.6, solution_type = "auto", - control = list(displayProgress = FALSE, print = FALSE, save = FALSE, save.graphs = - FALSE), + nbiter.saemix = c(300, 100), + control = list(displayProgress = FALSE, print = FALSE, nbiter.saemix = nbiter.saemix, + save = FALSE, save.graphs = FALSE), fail_with_errors = TRUE, verbose = FALSE, quiet = FALSE, @@ -214,7 +215,7 @@ be used to override the starting values obtained from the 'mmkin' object.

If TRUE, an attempt is made to use more robust starting values for population parameters fitted as log parameters in mkin (like rate constants) by only considering rate constants that pass the t-test -when calculating mean degradation parameters using mean_degparms.

+when calculating mean degradation parameters using mean_degparms.

conf.level @@ -225,10 +226,15 @@ for parameter that are tested if requested by 'test_log_parms'.

solution_type

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

+ + + nbiter.saemix +

Convenience option to increase the number of +iterations

control -

Passed to saemix::saemix

+

Passed to saemix::saemix.

fail_with_errors @@ -282,32 +288,35 @@ 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] "Tue Mar 9 17:34:44 2021" +#> [1] "Fri Jun 11 10:56:49 2021" #> .... #> Minimisation finished -#> [1] "Tue Mar 9 17:34:45 2021"
+#> [1] "Fri Jun 11 10:56:51 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] "Tue Mar 9 17:34:46 2021" +#> [1] "Fri Jun 11 10:56:53 2021" #> .... #> Minimisation finished -#> [1] "Tue Mar 9 17:34:48 2021"
f_saem_fomc <- saem(f_mmkin_parent["FOMC", ]) +#> [1] "Fri Jun 11 10:56:54 2021"
f_saem_fomc <- saem(f_mmkin_parent["FOMC", ])
#> Running main SAEM algorithm -#> [1] "Tue Mar 9 17:34:48 2021" +#> [1] "Fri Jun 11 10:56:54 2021" #> .... #> Minimisation finished -#> [1] "Tue Mar 9 17:34:50 2021"
f_saem_dfop <- saem(f_mmkin_parent["DFOP", ]) +#> [1] "Fri Jun 11 10:56:57 2021"
f_saem_dfop <- saem(f_mmkin_parent["DFOP", ])
#> Running main SAEM algorithm -#> [1] "Tue Mar 9 17:34:51 2021" +#> [1] "Fri Jun 11 10:56:57 2021" #> .... #> Minimisation finished -#> [1] "Tue Mar 9 17:34:53 2021"
+#> [1] "Fri Jun 11 10:57:00 2021"
# The returned saem.mmkin object contains an SaemixObject, therefore we can use # functions from saemix library(saemix)
#> Package saemix, version 3.1.9000 -#> please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr
compare.saemix(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so) +#> please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr
#> +#> Attaching package: ‘saemix’
#> The following object is masked from ‘package:RxODE’: +#> +#> phi
compare.saemix(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so)
#> Likelihoods calculated by importance sampling
#> AIC BIC #> 1 624.2484 622.2956 #> 2 467.7096 464.9757 @@ -348,10 +357,10 @@ 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] "Tue Mar 9 17:34:55 2021" +#> [1] "Fri Jun 11 10:57:03 2021" #> .... #> Minimisation finished -#> [1] "Tue Mar 9 17:35:00 2021"
compare.saemix(f_saem_fomc$so, f_saem_fomc_tc$so) +#> [1] "Fri Jun 11 10:57:09 2021"
compare.saemix(f_saem_fomc$so, f_saem_fomc_tc$so)
#> Likelihoods calculated by importance sampling
#> AIC BIC #> 1 467.7096 464.9757 #> 2 469.6831 466.5586
@@ -372,15 +381,15 @@ using mmkin.

# four minutes f_saem_sfo_sfo <- saem(f_mmkin["SFO-SFO", ])
#> Running main SAEM algorithm -#> [1] "Tue Mar 9 17:35:02 2021" +#> [1] "Fri Jun 11 10:57:12 2021" #> .... #> Minimisation finished -#> [1] "Tue Mar 9 17:35:07 2021"
f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ]) +#> [1] "Fri Jun 11 10:57:17 2021"
f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ])
#> Running main SAEM algorithm -#> [1] "Tue Mar 9 17:35:07 2021" +#> [1] "Fri Jun 11 10:57:17 2021" #> .... #> Minimisation finished -#> [1] "Tue Mar 9 17:35:15 2021"
# We can use print, plot and summary methods to check the results +#> [1] "Fri Jun 11 10:57:26 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: @@ -421,10 +430,10 @@ 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: 1.0.4.9000 -#> R version used for fitting: 4.0.4 -#> Date of fit: Tue Mar 9 17:35:16 2021 -#> Date of summary: Tue Mar 9 17:35:16 2021 +#> mkin version used for pre-fitting: 1.0.5 +#> R version used for fitting: 4.1.0 +#> Date of fit: Fri Jun 11 10:57:27 2021 +#> Date of summary: Fri Jun 11 10:57:27 2021 #> #> Equations: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -439,7 +448,7 @@ using mmkin.

#> #> Model predictions using solution type analytical #> -#> Fitted in 8.668 s using 300, 100 iterations +#> Fitted in 9.712 s using 300, 100 iterations #> #> Variance model: Constant variance #> @@ -509,176 +518,176 @@ using mmkin.

#> #> Data: #> ds name time observed predicted residual std standardized -#> Dataset 6 parent 0 97.2 95.79523 -1.40477 1.883 -0.745888 -#> Dataset 6 parent 0 96.4 95.79523 -0.60477 1.883 -0.321114 -#> Dataset 6 parent 3 71.1 71.32042 0.22042 1.883 0.117035 -#> Dataset 6 parent 3 69.2 71.32042 2.12042 1.883 1.125873 -#> Dataset 6 parent 6 58.1 56.45256 -1.64744 1.883 -0.874739 -#> Dataset 6 parent 6 56.6 56.45256 -0.14744 1.883 -0.078288 -#> Dataset 6 parent 10 44.4 44.48523 0.08523 1.883 0.045257 -#> Dataset 6 parent 10 43.4 44.48523 1.08523 1.883 0.576224 -#> Dataset 6 parent 20 33.3 29.75774 -3.54226 1.883 -1.880826 -#> Dataset 6 parent 20 29.2 29.75774 0.55774 1.883 0.296141 -#> Dataset 6 parent 34 17.6 19.35710 1.75710 1.883 0.932966 -#> Dataset 6 parent 34 18.0 19.35710 1.35710 1.883 0.720579 -#> Dataset 6 parent 55 10.5 10.48443 -0.01557 1.883 -0.008266 -#> Dataset 6 parent 55 9.3 10.48443 1.18443 1.883 0.628895 -#> Dataset 6 parent 90 4.5 3.78622 -0.71378 1.883 -0.378995 -#> Dataset 6 parent 90 4.7 3.78622 -0.91378 1.883 -0.485188 -#> Dataset 6 parent 112 3.0 1.99608 -1.00392 1.883 -0.533048 -#> Dataset 6 parent 112 3.4 1.99608 -1.40392 1.883 -0.745435 -#> Dataset 6 parent 132 2.3 1.11539 -1.18461 1.883 -0.628990 -#> Dataset 6 parent 132 2.7 1.11539 -1.58461 1.883 -0.841377 -#> Dataset 6 A1 3 4.3 4.66132 0.36132 1.883 0.191849 -#> Dataset 6 A1 3 4.6 4.66132 0.06132 1.883 0.032559 -#> Dataset 6 A1 6 7.0 7.41087 0.41087 1.883 0.218157 -#> Dataset 6 A1 6 7.2 7.41087 0.21087 1.883 0.111964 -#> Dataset 6 A1 10 8.2 9.50878 1.30878 1.883 0.694921 -#> Dataset 6 A1 10 8.0 9.50878 1.50878 1.883 0.801114 -#> Dataset 6 A1 20 11.0 11.69902 0.69902 1.883 0.371157 -#> Dataset 6 A1 20 13.7 11.69902 -2.00098 1.883 -1.062455 -#> Dataset 6 A1 34 11.5 12.67784 1.17784 1.883 0.625396 -#> Dataset 6 A1 34 12.7 12.67784 -0.02216 1.883 -0.011765 -#> Dataset 6 A1 55 14.9 12.78556 -2.11444 1.883 -1.122701 -#> Dataset 6 A1 55 14.5 12.78556 -1.71444 1.883 -0.910314 -#> Dataset 6 A1 90 12.1 11.52954 -0.57046 1.883 -0.302898 -#> Dataset 6 A1 90 12.3 11.52954 -0.77046 1.883 -0.409092 -#> Dataset 6 A1 112 9.9 10.43825 0.53825 1.883 0.285793 -#> Dataset 6 A1 112 10.2 10.43825 0.23825 1.883 0.126503 -#> Dataset 6 A1 132 8.8 9.42830 0.62830 1.883 0.333609 -#> Dataset 6 A1 132 7.8 9.42830 1.62830 1.883 0.864577 -#> Dataset 7 parent 0 93.6 90.91477 -2.68523 1.883 -1.425772 -#> Dataset 7 parent 0 92.3 90.91477 -1.38523 1.883 -0.735514 -#> Dataset 7 parent 3 87.0 84.76874 -2.23126 1.883 -1.184726 -#> Dataset 7 parent 3 82.2 84.76874 2.56874 1.883 1.363919 -#> Dataset 7 parent 7 74.0 77.62735 3.62735 1.883 1.926003 -#> Dataset 7 parent 7 73.9 77.62735 3.72735 1.883 1.979100 -#> Dataset 7 parent 14 64.2 67.52266 3.32266 1.883 1.764224 -#> Dataset 7 parent 14 69.5 67.52266 -1.97734 1.883 -1.049904 -#> Dataset 7 parent 30 54.0 52.41949 -1.58051 1.883 -0.839202 -#> Dataset 7 parent 30 54.6 52.41949 -2.18051 1.883 -1.157783 -#> Dataset 7 parent 60 41.1 39.36582 -1.73418 1.883 -0.920794 -#> Dataset 7 parent 60 38.4 39.36582 0.96582 1.883 0.512818 -#> Dataset 7 parent 90 32.5 33.75388 1.25388 1.883 0.665771 -#> Dataset 7 parent 90 35.5 33.75388 -1.74612 1.883 -0.927132 -#> Dataset 7 parent 120 28.1 30.41716 2.31716 1.883 1.230335 -#> Dataset 7 parent 120 29.0 30.41716 1.41716 1.883 0.752464 -#> Dataset 7 parent 180 26.5 25.66046 -0.83954 1.883 -0.445767 -#> Dataset 7 parent 180 27.6 25.66046 -1.93954 1.883 -1.029832 -#> Dataset 7 A1 3 3.9 2.69355 -1.20645 1.883 -0.640585 -#> Dataset 7 A1 3 3.1 2.69355 -0.40645 1.883 -0.215811 -#> Dataset 7 A1 7 6.9 5.81807 -1.08193 1.883 -0.574470 -#> Dataset 7 A1 7 6.6 5.81807 -0.78193 1.883 -0.415180 -#> Dataset 7 A1 14 10.4 10.22529 -0.17471 1.883 -0.092767 -#> Dataset 7 A1 14 8.3 10.22529 1.92529 1.883 1.022265 -#> Dataset 7 A1 30 14.4 16.75484 2.35484 1.883 1.250345 -#> Dataset 7 A1 30 13.7 16.75484 3.05484 1.883 1.622022 -#> Dataset 7 A1 60 22.1 22.22540 0.12540 1.883 0.066583 -#> Dataset 7 A1 60 22.3 22.22540 -0.07460 1.883 -0.039610 -#> Dataset 7 A1 90 27.5 24.38799 -3.11201 1.883 -1.652376 -#> Dataset 7 A1 90 25.4 24.38799 -1.01201 1.883 -0.537344 -#> Dataset 7 A1 120 28.0 25.53294 -2.46706 1.883 -1.309927 -#> Dataset 7 A1 120 26.6 25.53294 -1.06706 1.883 -0.566572 -#> Dataset 7 A1 180 25.8 26.94943 1.14943 1.883 0.610309 -#> Dataset 7 A1 180 25.3 26.94943 1.64943 1.883 0.875793 -#> Dataset 8 parent 0 91.9 91.53246 -0.36754 1.883 -0.195151 -#> Dataset 8 parent 0 90.8 91.53246 0.73246 1.883 0.388914 -#> Dataset 8 parent 1 64.9 67.73197 2.83197 1.883 1.503686 -#> Dataset 8 parent 1 66.2 67.73197 1.53197 1.883 0.813428 -#> Dataset 8 parent 3 43.5 41.58448 -1.91552 1.883 -1.017081 -#> Dataset 8 parent 3 44.1 41.58448 -2.51552 1.883 -1.335662 -#> Dataset 8 parent 8 18.3 19.62286 1.32286 1.883 0.702395 -#> Dataset 8 parent 8 18.1 19.62286 1.52286 1.883 0.808588 -#> Dataset 8 parent 14 10.2 10.77819 0.57819 1.883 0.306999 -#> Dataset 8 parent 14 10.8 10.77819 -0.02181 1.883 -0.011582 -#> Dataset 8 parent 27 4.9 3.26977 -1.63023 1.883 -0.865599 -#> Dataset 8 parent 27 3.3 3.26977 -0.03023 1.883 -0.016051 -#> Dataset 8 parent 48 1.6 0.48024 -1.11976 1.883 -0.594557 -#> Dataset 8 parent 48 1.5 0.48024 -1.01976 1.883 -0.541460 -#> Dataset 8 parent 70 1.1 0.06438 -1.03562 1.883 -0.549881 -#> Dataset 8 parent 70 0.9 0.06438 -0.83562 1.883 -0.443688 -#> Dataset 8 A1 1 9.6 7.61539 -1.98461 1.883 -1.053761 -#> Dataset 8 A1 1 7.7 7.61539 -0.08461 1.883 -0.044923 -#> Dataset 8 A1 3 15.0 15.47954 0.47954 1.883 0.254622 -#> Dataset 8 A1 3 15.1 15.47954 0.37954 1.883 0.201525 -#> Dataset 8 A1 8 21.2 20.22616 -0.97384 1.883 -0.517075 -#> Dataset 8 A1 8 21.1 20.22616 -0.87384 1.883 -0.463979 -#> Dataset 8 A1 14 19.7 20.00067 0.30067 1.883 0.159645 -#> Dataset 8 A1 14 18.9 20.00067 1.10067 1.883 0.584419 -#> Dataset 8 A1 27 17.5 16.38142 -1.11858 1.883 -0.593928 -#> Dataset 8 A1 27 15.9 16.38142 0.48142 1.883 0.255620 -#> Dataset 8 A1 48 9.5 10.25357 0.75357 1.883 0.400124 -#> Dataset 8 A1 48 9.8 10.25357 0.45357 1.883 0.240833 -#> Dataset 8 A1 70 6.2 5.95728 -0.24272 1.883 -0.128878 -#> Dataset 8 A1 70 6.1 5.95728 -0.14272 1.883 -0.075781 -#> Dataset 9 parent 0 99.8 97.47274 -2.32726 1.883 -1.235697 -#> Dataset 9 parent 0 98.3 97.47274 -0.82726 1.883 -0.439246 -#> Dataset 9 parent 1 77.1 79.72257 2.62257 1.883 1.392500 -#> Dataset 9 parent 1 77.2 79.72257 2.52257 1.883 1.339404 -#> Dataset 9 parent 3 59.0 56.26497 -2.73503 1.883 -1.452212 -#> Dataset 9 parent 3 58.1 56.26497 -1.83503 1.883 -0.974342 -#> Dataset 9 parent 8 27.4 31.66985 4.26985 1.883 2.267151 -#> Dataset 9 parent 8 29.2 31.66985 2.46985 1.883 1.311410 -#> Dataset 9 parent 14 19.1 22.39789 3.29789 1.883 1.751071 -#> Dataset 9 parent 14 29.6 22.39789 -7.20211 1.883 -3.824090 -#> Dataset 9 parent 27 10.1 14.21758 4.11758 1.883 2.186301 -#> Dataset 9 parent 27 18.2 14.21758 -3.98242 1.883 -2.114537 -#> Dataset 9 parent 48 4.5 7.27921 2.77921 1.883 1.475671 -#> Dataset 9 parent 48 9.1 7.27921 -1.82079 1.883 -0.966780 -#> Dataset 9 parent 70 2.3 3.61470 1.31470 1.883 0.698065 -#> Dataset 9 parent 70 2.9 3.61470 0.71470 1.883 0.379485 -#> Dataset 9 parent 91 2.0 1.85303 -0.14697 1.883 -0.078038 -#> Dataset 9 parent 91 1.8 1.85303 0.05303 1.883 0.028155 -#> Dataset 9 parent 120 2.0 0.73645 -1.26355 1.883 -0.670906 -#> Dataset 9 parent 120 2.2 0.73645 -1.46355 1.883 -0.777099 -#> Dataset 9 A1 1 4.2 3.87843 -0.32157 1.883 -0.170743 -#> Dataset 9 A1 1 3.9 3.87843 -0.02157 1.883 -0.011453 -#> Dataset 9 A1 3 7.4 8.90535 1.50535 1.883 0.799291 -#> Dataset 9 A1 3 7.9 8.90535 1.00535 1.883 0.533807 -#> Dataset 9 A1 8 14.5 13.75172 -0.74828 1.883 -0.397312 -#> Dataset 9 A1 8 13.7 13.75172 0.05172 1.883 0.027462 -#> Dataset 9 A1 14 14.2 14.97541 0.77541 1.883 0.411715 -#> Dataset 9 A1 14 12.2 14.97541 2.77541 1.883 1.473650 -#> Dataset 9 A1 27 13.7 14.94728 1.24728 1.883 0.662266 -#> Dataset 9 A1 27 13.2 14.94728 1.74728 1.883 0.927750 -#> Dataset 9 A1 48 13.6 13.66078 0.06078 1.883 0.032272 -#> Dataset 9 A1 48 15.4 13.66078 -1.73922 1.883 -0.923470 -#> Dataset 9 A1 70 10.4 11.84899 1.44899 1.883 0.769365 -#> Dataset 9 A1 70 11.6 11.84899 0.24899 1.883 0.132204 -#> Dataset 9 A1 91 10.0 10.09177 0.09177 1.883 0.048727 -#> Dataset 9 A1 91 9.5 10.09177 0.59177 1.883 0.314211 -#> Dataset 9 A1 120 9.1 7.91379 -1.18621 1.883 -0.629841 -#> Dataset 9 A1 120 9.0 7.91379 -1.08621 1.883 -0.576744 -#> Dataset 10 parent 0 96.1 93.65257 -2.44743 1.883 -1.299505 -#> Dataset 10 parent 0 94.3 93.65257 -0.64743 1.883 -0.343763 -#> Dataset 10 parent 8 73.9 77.85906 3.95906 1.883 2.102132 -#> Dataset 10 parent 8 73.9 77.85906 3.95906 1.883 2.102132 -#> Dataset 10 parent 14 69.4 70.17143 0.77143 1.883 0.409606 -#> Dataset 10 parent 14 73.1 70.17143 -2.92857 1.883 -1.554974 -#> Dataset 10 parent 21 65.6 63.99188 -1.60812 1.883 -0.853862 -#> Dataset 10 parent 21 65.3 63.99188 -1.30812 1.883 -0.694572 -#> Dataset 10 parent 41 55.9 54.64292 -1.25708 1.883 -0.667467 -#> Dataset 10 parent 41 54.4 54.64292 0.24292 1.883 0.128985 -#> Dataset 10 parent 63 47.0 49.61303 2.61303 1.883 1.387433 -#> Dataset 10 parent 63 49.3 49.61303 0.31303 1.883 0.166207 -#> Dataset 10 parent 91 44.7 45.17807 0.47807 1.883 0.253839 -#> Dataset 10 parent 91 46.7 45.17807 -1.52193 1.883 -0.808096 -#> Dataset 10 parent 120 42.1 41.27970 -0.82030 1.883 -0.435552 -#> Dataset 10 parent 120 41.3 41.27970 -0.02030 1.883 -0.010778 -#> Dataset 10 A1 8 3.3 3.99294 0.69294 1.883 0.367929 -#> Dataset 10 A1 8 3.4 3.99294 0.59294 1.883 0.314832 -#> Dataset 10 A1 14 3.9 5.92756 2.02756 1.883 1.076570 -#> Dataset 10 A1 14 2.9 5.92756 3.02756 1.883 1.607538 -#> Dataset 10 A1 21 6.4 7.47313 1.07313 1.883 0.569799 -#> Dataset 10 A1 21 7.2 7.47313 0.27313 1.883 0.145025 -#> Dataset 10 A1 41 9.1 9.76819 0.66819 1.883 0.354786 -#> Dataset 10 A1 41 8.5 9.76819 1.26819 1.883 0.673367 -#> Dataset 10 A1 63 11.7 10.94733 -0.75267 1.883 -0.399643 -#> Dataset 10 A1 63 12.0 10.94733 -1.05267 1.883 -0.558933 -#> Dataset 10 A1 91 13.3 11.93773 -1.36227 1.883 -0.723321 -#> 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
+#> Dataset 6 parent 0 97.2 95.79523 1.40477 1.883 0.745888 +#> Dataset 6 parent 0 96.4 95.79523 0.60477 1.883 0.321114 +#> Dataset 6 parent 3 71.1 71.32042 -0.22042 1.883 -0.117035 +#> Dataset 6 parent 3 69.2 71.32042 -2.12042 1.883 -1.125873 +#> Dataset 6 parent 6 58.1 56.45256 1.64744 1.883 0.874739 +#> Dataset 6 parent 6 56.6 56.45256 0.14744 1.883 0.078288 +#> Dataset 6 parent 10 44.4 44.48523 -0.08523 1.883 -0.045257 +#> Dataset 6 parent 10 43.4 44.48523 -1.08523 1.883 -0.576224 +#> Dataset 6 parent 20 33.3 29.75774 3.54226 1.883 1.880826 +#> Dataset 6 parent 20 29.2 29.75774 -0.55774 1.883 -0.296141 +#> Dataset 6 parent 34 17.6 19.35710 -1.75710 1.883 -0.932966 +#> Dataset 6 parent 34 18.0 19.35710 -1.35710 1.883 -0.720579 +#> Dataset 6 parent 55 10.5 10.48443 0.01557 1.883 0.008266 +#> Dataset 6 parent 55 9.3 10.48443 -1.18443 1.883 -0.628895 +#> Dataset 6 parent 90 4.5 3.78622 0.71378 1.883 0.378995 +#> Dataset 6 parent 90 4.7 3.78622 0.91378 1.883 0.485188 +#> Dataset 6 parent 112 3.0 1.99608 1.00392 1.883 0.533048 +#> Dataset 6 parent 112 3.4 1.99608 1.40392 1.883 0.745435 +#> Dataset 6 parent 132 2.3 1.11539 1.18461 1.883 0.628990 +#> Dataset 6 parent 132 2.7 1.11539 1.58461 1.883 0.841377 +#> Dataset 6 A1 3 4.3 4.66132 -0.36132 1.883 -0.191849 +#> Dataset 6 A1 3 4.6 4.66132 -0.06132 1.883 -0.032559 +#> Dataset 6 A1 6 7.0 7.41087 -0.41087 1.883 -0.218157 +#> Dataset 6 A1 6 7.2 7.41087 -0.21087 1.883 -0.111964 +#> Dataset 6 A1 10 8.2 9.50878 -1.30878 1.883 -0.694921 +#> Dataset 6 A1 10 8.0 9.50878 -1.50878 1.883 -0.801114 +#> Dataset 6 A1 20 11.0 11.69902 -0.69902 1.883 -0.371157 +#> Dataset 6 A1 20 13.7 11.69902 2.00098 1.883 1.062455 +#> Dataset 6 A1 34 11.5 12.67784 -1.17784 1.883 -0.625396 +#> Dataset 6 A1 34 12.7 12.67784 0.02216 1.883 0.011765 +#> Dataset 6 A1 55 14.9 12.78556 2.11444 1.883 1.122701 +#> Dataset 6 A1 55 14.5 12.78556 1.71444 1.883 0.910314 +#> Dataset 6 A1 90 12.1 11.52954 0.57046 1.883 0.302898 +#> Dataset 6 A1 90 12.3 11.52954 0.77046 1.883 0.409092 +#> Dataset 6 A1 112 9.9 10.43825 -0.53825 1.883 -0.285793 +#> Dataset 6 A1 112 10.2 10.43825 -0.23825 1.883 -0.126503 +#> Dataset 6 A1 132 8.8 9.42830 -0.62830 1.883 -0.333609 +#> Dataset 6 A1 132 7.8 9.42830 -1.62830 1.883 -0.864577 +#> Dataset 7 parent 0 93.6 90.91477 2.68523 1.883 1.425772 +#> Dataset 7 parent 0 92.3 90.91477 1.38523 1.883 0.735514 +#> Dataset 7 parent 3 87.0 84.76874 2.23126 1.883 1.184726 +#> Dataset 7 parent 3 82.2 84.76874 -2.56874 1.883 -1.363919 +#> Dataset 7 parent 7 74.0 77.62735 -3.62735 1.883 -1.926003 +#> Dataset 7 parent 7 73.9 77.62735 -3.72735 1.883 -1.979100 +#> Dataset 7 parent 14 64.2 67.52266 -3.32266 1.883 -1.764224 +#> Dataset 7 parent 14 69.5 67.52266 1.97734 1.883 1.049904 +#> Dataset 7 parent 30 54.0 52.41949 1.58051 1.883 0.839202 +#> Dataset 7 parent 30 54.6 52.41949 2.18051 1.883 1.157783 +#> Dataset 7 parent 60 41.1 39.36582 1.73418 1.883 0.920794 +#> Dataset 7 parent 60 38.4 39.36582 -0.96582 1.883 -0.512818 +#> Dataset 7 parent 90 32.5 33.75388 -1.25388 1.883 -0.665771 +#> Dataset 7 parent 90 35.5 33.75388 1.74612 1.883 0.927132 +#> Dataset 7 parent 120 28.1 30.41716 -2.31716 1.883 -1.230335 +#> Dataset 7 parent 120 29.0 30.41716 -1.41716 1.883 -0.752464 +#> Dataset 7 parent 180 26.5 25.66046 0.83954 1.883 0.445767 +#> Dataset 7 parent 180 27.6 25.66046 1.93954 1.883 1.029832 +#> Dataset 7 A1 3 3.9 2.69355 1.20645 1.883 0.640585 +#> Dataset 7 A1 3 3.1 2.69355 0.40645 1.883 0.215811 +#> Dataset 7 A1 7 6.9 5.81807 1.08193 1.883 0.574470 +#> Dataset 7 A1 7 6.6 5.81807 0.78193 1.883 0.415180 +#> Dataset 7 A1 14 10.4 10.22529 0.17471 1.883 0.092767 +#> Dataset 7 A1 14 8.3 10.22529 -1.92529 1.883 -1.022265 +#> Dataset 7 A1 30 14.4 16.75484 -2.35484 1.883 -1.250345 +#> Dataset 7 A1 30 13.7 16.75484 -3.05484 1.883 -1.622022 +#> Dataset 7 A1 60 22.1 22.22540 -0.12540 1.883 -0.066583 +#> Dataset 7 A1 60 22.3 22.22540 0.07460 1.883 0.039610 +#> Dataset 7 A1 90 27.5 24.38799 3.11201 1.883 1.652376 +#> Dataset 7 A1 90 25.4 24.38799 1.01201 1.883 0.537344 +#> Dataset 7 A1 120 28.0 25.53294 2.46706 1.883 1.309927 +#> Dataset 7 A1 120 26.6 25.53294 1.06706 1.883 0.566572 +#> Dataset 7 A1 180 25.8 26.94943 -1.14943 1.883 -0.610309 +#> Dataset 7 A1 180 25.3 26.94943 -1.64943 1.883 -0.875793 +#> Dataset 8 parent 0 91.9 91.53246 0.36754 1.883 0.195151 +#> Dataset 8 parent 0 90.8 91.53246 -0.73246 1.883 -0.388914 +#> Dataset 8 parent 1 64.9 67.73197 -2.83197 1.883 -1.503686 +#> Dataset 8 parent 1 66.2 67.73197 -1.53197 1.883 -0.813428 +#> Dataset 8 parent 3 43.5 41.58448 1.91552 1.883 1.017081 +#> Dataset 8 parent 3 44.1 41.58448 2.51552 1.883 1.335662 +#> Dataset 8 parent 8 18.3 19.62286 -1.32286 1.883 -0.702395 +#> Dataset 8 parent 8 18.1 19.62286 -1.52286 1.883 -0.808588 +#> Dataset 8 parent 14 10.2 10.77819 -0.57819 1.883 -0.306999 +#> Dataset 8 parent 14 10.8 10.77819 0.02181 1.883 0.011582 +#> Dataset 8 parent 27 4.9 3.26977 1.63023 1.883 0.865599 +#> Dataset 8 parent 27 3.3 3.26977 0.03023 1.883 0.016051 +#> Dataset 8 parent 48 1.6 0.48024 1.11976 1.883 0.594557 +#> Dataset 8 parent 48 1.5 0.48024 1.01976 1.883 0.541460 +#> Dataset 8 parent 70 1.1 0.06438 1.03562 1.883 0.549881 +#> Dataset 8 parent 70 0.9 0.06438 0.83562 1.883 0.443688 +#> Dataset 8 A1 1 9.6 7.61539 1.98461 1.883 1.053761 +#> Dataset 8 A1 1 7.7 7.61539 0.08461 1.883 0.044923 +#> Dataset 8 A1 3 15.0 15.47954 -0.47954 1.883 -0.254622 +#> Dataset 8 A1 3 15.1 15.47954 -0.37954 1.883 -0.201525 +#> Dataset 8 A1 8 21.2 20.22616 0.97384 1.883 0.517075 +#> Dataset 8 A1 8 21.1 20.22616 0.87384 1.883 0.463979 +#> Dataset 8 A1 14 19.7 20.00067 -0.30067 1.883 -0.159645 +#> Dataset 8 A1 14 18.9 20.00067 -1.10067 1.883 -0.584419 +#> Dataset 8 A1 27 17.5 16.38142 1.11858 1.883 0.593928 +#> Dataset 8 A1 27 15.9 16.38142 -0.48142 1.883 -0.255620 +#> Dataset 8 A1 48 9.5 10.25357 -0.75357 1.883 -0.400124 +#> Dataset 8 A1 48 9.8 10.25357 -0.45357 1.883 -0.240833 +#> Dataset 8 A1 70 6.2 5.95728 0.24272 1.883 0.128878 +#> Dataset 8 A1 70 6.1 5.95728 0.14272 1.883 0.075781 +#> Dataset 9 parent 0 99.8 97.47274 2.32726 1.883 1.235697 +#> Dataset 9 parent 0 98.3 97.47274 0.82726 1.883 0.439246 +#> Dataset 9 parent 1 77.1 79.72257 -2.62257 1.883 -1.392500 +#> Dataset 9 parent 1 77.2 79.72257 -2.52257 1.883 -1.339404 +#> Dataset 9 parent 3 59.0 56.26497 2.73503 1.883 1.452212 +#> Dataset 9 parent 3 58.1 56.26497 1.83503 1.883 0.974342 +#> Dataset 9 parent 8 27.4 31.66985 -4.26985 1.883 -2.267151 +#> Dataset 9 parent 8 29.2 31.66985 -2.46985 1.883 -1.311410 +#> Dataset 9 parent 14 19.1 22.39789 -3.29789 1.883 -1.751071 +#> Dataset 9 parent 14 29.6 22.39789 7.20211 1.883 3.824090 +#> Dataset 9 parent 27 10.1 14.21758 -4.11758 1.883 -2.186301 +#> Dataset 9 parent 27 18.2 14.21758 3.98242 1.883 2.114537 +#> Dataset 9 parent 48 4.5 7.27921 -2.77921 1.883 -1.475671 +#> Dataset 9 parent 48 9.1 7.27921 1.82079 1.883 0.966780 +#> Dataset 9 parent 70 2.3 3.61470 -1.31470 1.883 -0.698065 +#> Dataset 9 parent 70 2.9 3.61470 -0.71470 1.883 -0.379485 +#> Dataset 9 parent 91 2.0 1.85303 0.14697 1.883 0.078038 +#> Dataset 9 parent 91 1.8 1.85303 -0.05303 1.883 -0.028155 +#> Dataset 9 parent 120 2.0 0.73645 1.26355 1.883 0.670906 +#> Dataset 9 parent 120 2.2 0.73645 1.46355 1.883 0.777099 +#> Dataset 9 A1 1 4.2 3.87843 0.32157 1.883 0.170743 +#> Dataset 9 A1 1 3.9 3.87843 0.02157 1.883 0.011453 +#> Dataset 9 A1 3 7.4 8.90535 -1.50535 1.883 -0.799291 +#> Dataset 9 A1 3 7.9 8.90535 -1.00535 1.883 -0.533807 +#> Dataset 9 A1 8 14.5 13.75172 0.74828 1.883 0.397312 +#> Dataset 9 A1 8 13.7 13.75172 -0.05172 1.883 -0.027462 +#> Dataset 9 A1 14 14.2 14.97541 -0.77541 1.883 -0.411715 +#> Dataset 9 A1 14 12.2 14.97541 -2.77541 1.883 -1.473650 +#> Dataset 9 A1 27 13.7 14.94728 -1.24728 1.883 -0.662266 +#> Dataset 9 A1 27 13.2 14.94728 -1.74728 1.883 -0.927750 +#> Dataset 9 A1 48 13.6 13.66078 -0.06078 1.883 -0.032272 +#> Dataset 9 A1 48 15.4 13.66078 1.73922 1.883 0.923470 +#> Dataset 9 A1 70 10.4 11.84899 -1.44899 1.883 -0.769365 +#> Dataset 9 A1 70 11.6 11.84899 -0.24899 1.883 -0.132204 +#> Dataset 9 A1 91 10.0 10.09177 -0.09177 1.883 -0.048727 +#> Dataset 9 A1 91 9.5 10.09177 -0.59177 1.883 -0.314211 +#> Dataset 9 A1 120 9.1 7.91379 1.18621 1.883 0.629841 +#> Dataset 9 A1 120 9.0 7.91379 1.08621 1.883 0.576744 +#> Dataset 10 parent 0 96.1 93.65257 2.44743 1.883 1.299505 +#> Dataset 10 parent 0 94.3 93.65257 0.64743 1.883 0.343763 +#> Dataset 10 parent 8 73.9 77.85906 -3.95906 1.883 -2.102132 +#> Dataset 10 parent 8 73.9 77.85906 -3.95906 1.883 -2.102132 +#> Dataset 10 parent 14 69.4 70.17143 -0.77143 1.883 -0.409606 +#> Dataset 10 parent 14 73.1 70.17143 2.92857 1.883 1.554974 +#> Dataset 10 parent 21 65.6 63.99188 1.60812 1.883 0.853862 +#> Dataset 10 parent 21 65.3 63.99188 1.30812 1.883 0.694572 +#> Dataset 10 parent 41 55.9 54.64292 1.25708 1.883 0.667467 +#> Dataset 10 parent 41 54.4 54.64292 -0.24292 1.883 -0.128985 +#> Dataset 10 parent 63 47.0 49.61303 -2.61303 1.883 -1.387433 +#> Dataset 10 parent 63 49.3 49.61303 -0.31303 1.883 -0.166207 +#> Dataset 10 parent 91 44.7 45.17807 -0.47807 1.883 -0.253839 +#> Dataset 10 parent 91 46.7 45.17807 1.52193 1.883 0.808096 +#> Dataset 10 parent 120 42.1 41.27970 0.82030 1.883 0.435552 +#> Dataset 10 parent 120 41.3 41.27970 0.02030 1.883 0.010778 +#> Dataset 10 A1 8 3.3 3.99294 -0.69294 1.883 -0.367929 +#> Dataset 10 A1 8 3.4 3.99294 -0.59294 1.883 -0.314832 +#> Dataset 10 A1 14 3.9 5.92756 -2.02756 1.883 -1.076570 +#> Dataset 10 A1 14 2.9 5.92756 -3.02756 1.883 -1.607538 +#> Dataset 10 A1 21 6.4 7.47313 -1.07313 1.883 -0.569799 +#> Dataset 10 A1 21 7.2 7.47313 -0.27313 1.883 -0.145025 +#> Dataset 10 A1 41 9.1 9.76819 -0.66819 1.883 -0.354786 +#> Dataset 10 A1 41 8.5 9.76819 -1.26819 1.883 -0.673367 +#> Dataset 10 A1 63 11.7 10.94733 0.75267 1.883 0.399643 +#> Dataset 10 A1 63 12.0 10.94733 1.05267 1.883 0.558933 +#> Dataset 10 A1 91 13.3 11.93773 1.36227 1.883 0.723321 +#> 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
# 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)) -- cgit v1.2.1 From 0b754ffa91b9496bdd2f892cf3ca2bd887028dea Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Tue, 27 Jul 2021 18:22:01 +0200 Subject: Fix dimethenamid vignette problems and update docs --- docs/dev/reference/saem.html | 480 +++++++++++++++++++++---------------------- 1 file changed, 240 insertions(+), 240 deletions(-) (limited to 'docs/dev/reference/saem.html') diff --git a/docs/dev/reference/saem.html b/docs/dev/reference/saem.html index 98faad6f..15271c8a 100644 --- a/docs/dev/reference/saem.html +++ b/docs/dev/reference/saem.html @@ -74,7 +74,7 @@ Expectation Maximisation algorithm (SAEM)." /> mkin - 1.0.5 + 1.1.0
@@ -158,7 +158,7 @@ Expectation Maximisation algorithm (SAEM).

object, transformations = c("mkin", "saemix"), degparms_start = numeric(), - test_log_parms = FALSE, + test_log_parms = TRUE, conf.level = 0.6, solution_type = "auto", nbiter.saemix = c(300, 100), @@ -288,27 +288,27 @@ 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] "Fri Jun 11 10:56:49 2021" +#> [1] "Tue Jul 27 16:31:02 2021" #> .... #> Minimisation finished -#> [1] "Fri Jun 11 10:56:51 2021"
+#> [1] "Tue Jul 27 16:31:04 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] "Fri Jun 11 10:56:53 2021" +#> [1] "Tue Jul 27 16:31:06 2021" #> .... #> Minimisation finished -#> [1] "Fri Jun 11 10:56:54 2021"
f_saem_fomc <- saem(f_mmkin_parent["FOMC", ]) +#> [1] "Tue Jul 27 16:31:07 2021"
f_saem_fomc <- saem(f_mmkin_parent["FOMC", ])
#> Running main SAEM algorithm -#> [1] "Fri Jun 11 10:56:54 2021" +#> [1] "Tue Jul 27 16:31:07 2021" #> .... #> Minimisation finished -#> [1] "Fri Jun 11 10:56:57 2021"
f_saem_dfop <- saem(f_mmkin_parent["DFOP", ]) +#> [1] "Tue Jul 27 16:31:09 2021"
f_saem_dfop <- saem(f_mmkin_parent["DFOP", ])
#> Running main SAEM algorithm -#> [1] "Fri Jun 11 10:56:57 2021" +#> [1] "Tue Jul 27 16:31:10 2021" #> .... #> Minimisation finished -#> [1] "Fri Jun 11 10:57:00 2021"
+#> [1] "Tue Jul 27 16:31:12 2021"
# The returned saem.mmkin object contains an SaemixObject, therefore we can use # functions from saemix library(saemix) @@ -357,10 +357,10 @@ 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] "Fri Jun 11 10:57:03 2021" +#> [1] "Tue Jul 27 16:31:16 2021" #> .... #> Minimisation finished -#> [1] "Fri Jun 11 10:57:09 2021"
compare.saemix(f_saem_fomc$so, f_saem_fomc_tc$so) +#> [1] "Tue Jul 27 16:31:20 2021"
compare.saemix(f_saem_fomc$so, f_saem_fomc_tc$so)
#> Likelihoods calculated by importance sampling
#> AIC BIC #> 1 467.7096 464.9757 #> 2 469.6831 466.5586
@@ -381,15 +381,15 @@ using mmkin.

# four minutes f_saem_sfo_sfo <- saem(f_mmkin["SFO-SFO", ])
#> Running main SAEM algorithm -#> [1] "Fri Jun 11 10:57:12 2021" +#> [1] "Tue Jul 27 16:31:24 2021" #> .... #> Minimisation finished -#> [1] "Fri Jun 11 10:57:17 2021"
f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ]) +#> [1] "Tue Jul 27 16:31:29 2021"
f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ])
#> Running main SAEM algorithm -#> [1] "Fri Jun 11 10:57:17 2021" +#> [1] "Tue Jul 27 16:31:30 2021" #> .... #> Minimisation finished -#> [1] "Fri Jun 11 10:57:26 2021"
# We can use print, plot and summary methods to check the results +#> [1] "Tue Jul 27 16:31:38 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: @@ -405,35 +405,35 @@ using mmkin.

#> #> Likelihood computed by importance sampling #> AIC BIC logLik -#> 841.6 836.5 -407.8 +#> 839.6 834.6 -406.8 #> #> Fitted parameters: #> estimate lower upper -#> parent_0 93.76647 91.15312 96.3798 -#> log_k_A1 -6.13235 -8.45788 -3.8068 -#> f_parent_qlogis -0.97364 -1.36940 -0.5779 -#> log_k1 -2.53176 -3.80372 -1.2598 -#> log_k2 -3.58667 -5.29524 -1.8781 -#> g_qlogis 0.01238 -1.07968 1.1044 -#> Var.parent_0 7.61106 -3.34955 18.5717 -#> Var.log_k_A1 4.64679 -2.73133 12.0249 -#> Var.f_parent_qlogis 0.19693 -0.05498 0.4488 -#> Var.log_k1 2.01717 -0.51980 4.5542 -#> Var.log_k2 3.63412 -0.92964 8.1979 -#> Var.g_qlogis 0.20045 -0.97425 1.3751 -#> a.1 1.88335 1.66636 2.1004 -#> SD.parent_0 2.75881 0.77234 4.7453 -#> SD.log_k_A1 2.15564 0.44429 3.8670 -#> SD.f_parent_qlogis 0.44377 0.15994 0.7276 -#> SD.log_k1 1.42027 0.52714 2.3134 -#> SD.log_k2 1.90634 0.70934 3.1033 -#> SD.g_qlogis 0.44771 -0.86417 1.7596
plot(f_saem_dfop_sfo) +#> parent_0 93.80521 91.22487 96.3856 +#> log_k_A1 -6.06244 -8.26517 -3.8597 +#> f_parent_qlogis -0.97319 -1.37024 -0.5761 +#> log_k1 -2.55394 -4.00815 -1.0997 +#> log_k2 -3.47160 -5.18763 -1.7556 +#> g_qlogis -0.09324 -1.42737 1.2409 +#> Var.parent_0 7.42157 -3.25683 18.1000 +#> Var.log_k_A1 4.22850 -2.46339 10.9204 +#> Var.f_parent_qlogis 0.19803 -0.05541 0.4515 +#> Var.log_k1 2.28644 -0.86079 5.4337 +#> Var.log_k2 3.35626 -1.14639 7.8589 +#> Var.g_qlogis 0.20084 -1.32516 1.7268 +#> a.1 1.88399 1.66794 2.1000 +#> SD.parent_0 2.72425 0.76438 4.6841 +#> SD.log_k_A1 2.05633 0.42919 3.6835 +#> SD.f_parent_qlogis 0.44501 0.16025 0.7298 +#> SD.log_k1 1.51210 0.47142 2.5528 +#> SD.log_k2 1.83201 0.60313 3.0609 +#> SD.g_qlogis 0.44816 -1.25437 2.1507
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: 1.0.5 +#> mkin version used for pre-fitting: 1.1.0 #> R version used for fitting: 4.1.0 -#> Date of fit: Fri Jun 11 10:57:27 2021 -#> Date of summary: Fri Jun 11 10:57:27 2021 +#> Date of fit: Tue Jul 27 16:31:39 2021 +#> Date of summary: Tue Jul 27 16:31:39 2021 #> #> Equations: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -448,13 +448,13 @@ using mmkin.

#> #> Model predictions using solution type analytical #> -#> Fitted in 9.712 s using 300, 100 iterations +#> Fitted in 9.479 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 -#> 93.8102 -9.7647 -0.9711 -1.8799 -4.2708 +#> 93.8102 -5.3734 -0.9711 -1.8799 -4.2708 #> g_qlogis #> 0.1356 #> @@ -465,46 +465,46 @@ using mmkin.

#> #> Likelihood computed by importance sampling #> AIC BIC logLik -#> 841.6 836.5 -407.8 +#> 839.6 834.6 -406.8 #> #> Optimised parameters: #> est. lower upper -#> parent_0 93.76647 91.153 96.3798 -#> log_k_A1 -6.13235 -8.458 -3.8068 -#> f_parent_qlogis -0.97364 -1.369 -0.5779 -#> log_k1 -2.53176 -3.804 -1.2598 -#> log_k2 -3.58667 -5.295 -1.8781 -#> g_qlogis 0.01238 -1.080 1.1044 +#> parent_0 93.80521 91.225 96.3856 +#> log_k_A1 -6.06244 -8.265 -3.8597 +#> f_parent_qlogis -0.97319 -1.370 -0.5761 +#> log_k1 -2.55394 -4.008 -1.0997 +#> log_k2 -3.47160 -5.188 -1.7556 +#> g_qlogis -0.09324 -1.427 1.2409 #> #> Correlation: #> prnt_0 lg__A1 f_prn_ log_k1 log_k2 -#> log_k_A1 -0.013 -#> f_parent_qlogis -0.025 0.050 -#> log_k1 0.030 0.000 -0.005 -#> log_k2 0.010 0.005 -0.003 0.032 -#> g_qlogis -0.063 -0.015 0.010 -0.167 -0.177 +#> log_k_A1 -0.014 +#> f_parent_qlogis -0.025 0.054 +#> log_k1 0.027 -0.003 -0.005 +#> log_k2 0.011 0.005 -0.002 -0.070 +#> g_qlogis -0.067 -0.009 0.011 -0.189 -0.171 #> #> Random effects: #> est. lower upper -#> SD.parent_0 2.7588 0.7723 4.7453 -#> SD.log_k_A1 2.1556 0.4443 3.8670 -#> SD.f_parent_qlogis 0.4438 0.1599 0.7276 -#> SD.log_k1 1.4203 0.5271 2.3134 -#> SD.log_k2 1.9063 0.7093 3.1033 -#> SD.g_qlogis 0.4477 -0.8642 1.7596 +#> SD.parent_0 2.7243 0.7644 4.6841 +#> SD.log_k_A1 2.0563 0.4292 3.6835 +#> SD.f_parent_qlogis 0.4450 0.1602 0.7298 +#> SD.log_k1 1.5121 0.4714 2.5528 +#> SD.log_k2 1.8320 0.6031 3.0609 +#> SD.g_qlogis 0.4482 -1.2544 2.1507 #> #> Variance model: #> est. lower upper -#> a.1 1.883 1.666 2.1 +#> a.1 1.884 1.668 2.1 #> #> Backtransformed parameters: #> est. lower upper -#> parent_0 93.766473 9.115e+01 96.37983 -#> k_A1 0.002171 2.122e-04 0.02222 -#> f_parent_to_A1 0.274156 2.027e-01 0.35942 -#> k1 0.079519 2.229e-02 0.28371 -#> k2 0.027691 5.015e-03 0.15288 -#> g 0.503095 2.536e-01 0.75109 +#> parent_0 93.805214 9.122e+01 96.38556 +#> k_A1 0.002329 2.573e-04 0.02107 +#> f_parent_to_A1 0.274245 2.026e-01 0.35982 +#> k1 0.077775 1.817e-02 0.33296 +#> k2 0.031067 5.585e-03 0.17281 +#> g 0.476707 1.935e-01 0.77572 #> #> Resulting formation fractions: #> ff @@ -512,182 +512,182 @@ using mmkin.

#> parent_sink 0.7258 #> #> Estimated disappearance times: -#> DT50 DT90 DT50back DT50_k1 DT50_k2 -#> parent 14.11 59.53 17.92 8.717 25.03 -#> A1 319.21 1060.38 NA NA NA +#> DT50 DT90 DT50back DT50_k1 DT50_k2 +#> parent 13.96 55.4 16.68 8.912 22.31 +#> A1 297.65 988.8 NA NA NA #> #> Data: -#> ds name time observed predicted residual std standardized -#> Dataset 6 parent 0 97.2 95.79523 1.40477 1.883 0.745888 -#> Dataset 6 parent 0 96.4 95.79523 0.60477 1.883 0.321114 -#> Dataset 6 parent 3 71.1 71.32042 -0.22042 1.883 -0.117035 -#> Dataset 6 parent 3 69.2 71.32042 -2.12042 1.883 -1.125873 -#> Dataset 6 parent 6 58.1 56.45256 1.64744 1.883 0.874739 -#> Dataset 6 parent 6 56.6 56.45256 0.14744 1.883 0.078288 -#> Dataset 6 parent 10 44.4 44.48523 -0.08523 1.883 -0.045257 -#> Dataset 6 parent 10 43.4 44.48523 -1.08523 1.883 -0.576224 -#> Dataset 6 parent 20 33.3 29.75774 3.54226 1.883 1.880826 -#> Dataset 6 parent 20 29.2 29.75774 -0.55774 1.883 -0.296141 -#> Dataset 6 parent 34 17.6 19.35710 -1.75710 1.883 -0.932966 -#> Dataset 6 parent 34 18.0 19.35710 -1.35710 1.883 -0.720579 -#> Dataset 6 parent 55 10.5 10.48443 0.01557 1.883 0.008266 -#> Dataset 6 parent 55 9.3 10.48443 -1.18443 1.883 -0.628895 -#> Dataset 6 parent 90 4.5 3.78622 0.71378 1.883 0.378995 -#> Dataset 6 parent 90 4.7 3.78622 0.91378 1.883 0.485188 -#> Dataset 6 parent 112 3.0 1.99608 1.00392 1.883 0.533048 -#> Dataset 6 parent 112 3.4 1.99608 1.40392 1.883 0.745435 -#> Dataset 6 parent 132 2.3 1.11539 1.18461 1.883 0.628990 -#> Dataset 6 parent 132 2.7 1.11539 1.58461 1.883 0.841377 -#> Dataset 6 A1 3 4.3 4.66132 -0.36132 1.883 -0.191849 -#> Dataset 6 A1 3 4.6 4.66132 -0.06132 1.883 -0.032559 -#> Dataset 6 A1 6 7.0 7.41087 -0.41087 1.883 -0.218157 -#> Dataset 6 A1 6 7.2 7.41087 -0.21087 1.883 -0.111964 -#> Dataset 6 A1 10 8.2 9.50878 -1.30878 1.883 -0.694921 -#> Dataset 6 A1 10 8.0 9.50878 -1.50878 1.883 -0.801114 -#> Dataset 6 A1 20 11.0 11.69902 -0.69902 1.883 -0.371157 -#> Dataset 6 A1 20 13.7 11.69902 2.00098 1.883 1.062455 -#> Dataset 6 A1 34 11.5 12.67784 -1.17784 1.883 -0.625396 -#> Dataset 6 A1 34 12.7 12.67784 0.02216 1.883 0.011765 -#> Dataset 6 A1 55 14.9 12.78556 2.11444 1.883 1.122701 -#> Dataset 6 A1 55 14.5 12.78556 1.71444 1.883 0.910314 -#> Dataset 6 A1 90 12.1 11.52954 0.57046 1.883 0.302898 -#> Dataset 6 A1 90 12.3 11.52954 0.77046 1.883 0.409092 -#> Dataset 6 A1 112 9.9 10.43825 -0.53825 1.883 -0.285793 -#> Dataset 6 A1 112 10.2 10.43825 -0.23825 1.883 -0.126503 -#> Dataset 6 A1 132 8.8 9.42830 -0.62830 1.883 -0.333609 -#> Dataset 6 A1 132 7.8 9.42830 -1.62830 1.883 -0.864577 -#> Dataset 7 parent 0 93.6 90.91477 2.68523 1.883 1.425772 -#> Dataset 7 parent 0 92.3 90.91477 1.38523 1.883 0.735514 -#> Dataset 7 parent 3 87.0 84.76874 2.23126 1.883 1.184726 -#> Dataset 7 parent 3 82.2 84.76874 -2.56874 1.883 -1.363919 -#> Dataset 7 parent 7 74.0 77.62735 -3.62735 1.883 -1.926003 -#> Dataset 7 parent 7 73.9 77.62735 -3.72735 1.883 -1.979100 -#> Dataset 7 parent 14 64.2 67.52266 -3.32266 1.883 -1.764224 -#> Dataset 7 parent 14 69.5 67.52266 1.97734 1.883 1.049904 -#> Dataset 7 parent 30 54.0 52.41949 1.58051 1.883 0.839202 -#> Dataset 7 parent 30 54.6 52.41949 2.18051 1.883 1.157783 -#> Dataset 7 parent 60 41.1 39.36582 1.73418 1.883 0.920794 -#> Dataset 7 parent 60 38.4 39.36582 -0.96582 1.883 -0.512818 -#> Dataset 7 parent 90 32.5 33.75388 -1.25388 1.883 -0.665771 -#> Dataset 7 parent 90 35.5 33.75388 1.74612 1.883 0.927132 -#> Dataset 7 parent 120 28.1 30.41716 -2.31716 1.883 -1.230335 -#> Dataset 7 parent 120 29.0 30.41716 -1.41716 1.883 -0.752464 -#> Dataset 7 parent 180 26.5 25.66046 0.83954 1.883 0.445767 -#> Dataset 7 parent 180 27.6 25.66046 1.93954 1.883 1.029832 -#> Dataset 7 A1 3 3.9 2.69355 1.20645 1.883 0.640585 -#> Dataset 7 A1 3 3.1 2.69355 0.40645 1.883 0.215811 -#> Dataset 7 A1 7 6.9 5.81807 1.08193 1.883 0.574470 -#> Dataset 7 A1 7 6.6 5.81807 0.78193 1.883 0.415180 -#> Dataset 7 A1 14 10.4 10.22529 0.17471 1.883 0.092767 -#> Dataset 7 A1 14 8.3 10.22529 -1.92529 1.883 -1.022265 -#> Dataset 7 A1 30 14.4 16.75484 -2.35484 1.883 -1.250345 -#> Dataset 7 A1 30 13.7 16.75484 -3.05484 1.883 -1.622022 -#> Dataset 7 A1 60 22.1 22.22540 -0.12540 1.883 -0.066583 -#> Dataset 7 A1 60 22.3 22.22540 0.07460 1.883 0.039610 -#> Dataset 7 A1 90 27.5 24.38799 3.11201 1.883 1.652376 -#> Dataset 7 A1 90 25.4 24.38799 1.01201 1.883 0.537344 -#> Dataset 7 A1 120 28.0 25.53294 2.46706 1.883 1.309927 -#> Dataset 7 A1 120 26.6 25.53294 1.06706 1.883 0.566572 -#> Dataset 7 A1 180 25.8 26.94943 -1.14943 1.883 -0.610309 -#> Dataset 7 A1 180 25.3 26.94943 -1.64943 1.883 -0.875793 -#> Dataset 8 parent 0 91.9 91.53246 0.36754 1.883 0.195151 -#> Dataset 8 parent 0 90.8 91.53246 -0.73246 1.883 -0.388914 -#> Dataset 8 parent 1 64.9 67.73197 -2.83197 1.883 -1.503686 -#> Dataset 8 parent 1 66.2 67.73197 -1.53197 1.883 -0.813428 -#> Dataset 8 parent 3 43.5 41.58448 1.91552 1.883 1.017081 -#> Dataset 8 parent 3 44.1 41.58448 2.51552 1.883 1.335662 -#> Dataset 8 parent 8 18.3 19.62286 -1.32286 1.883 -0.702395 -#> Dataset 8 parent 8 18.1 19.62286 -1.52286 1.883 -0.808588 -#> Dataset 8 parent 14 10.2 10.77819 -0.57819 1.883 -0.306999 -#> Dataset 8 parent 14 10.8 10.77819 0.02181 1.883 0.011582 -#> Dataset 8 parent 27 4.9 3.26977 1.63023 1.883 0.865599 -#> Dataset 8 parent 27 3.3 3.26977 0.03023 1.883 0.016051 -#> Dataset 8 parent 48 1.6 0.48024 1.11976 1.883 0.594557 -#> Dataset 8 parent 48 1.5 0.48024 1.01976 1.883 0.541460 -#> Dataset 8 parent 70 1.1 0.06438 1.03562 1.883 0.549881 -#> Dataset 8 parent 70 0.9 0.06438 0.83562 1.883 0.443688 -#> Dataset 8 A1 1 9.6 7.61539 1.98461 1.883 1.053761 -#> Dataset 8 A1 1 7.7 7.61539 0.08461 1.883 0.044923 -#> Dataset 8 A1 3 15.0 15.47954 -0.47954 1.883 -0.254622 -#> Dataset 8 A1 3 15.1 15.47954 -0.37954 1.883 -0.201525 -#> Dataset 8 A1 8 21.2 20.22616 0.97384 1.883 0.517075 -#> Dataset 8 A1 8 21.1 20.22616 0.87384 1.883 0.463979 -#> Dataset 8 A1 14 19.7 20.00067 -0.30067 1.883 -0.159645 -#> Dataset 8 A1 14 18.9 20.00067 -1.10067 1.883 -0.584419 -#> Dataset 8 A1 27 17.5 16.38142 1.11858 1.883 0.593928 -#> Dataset 8 A1 27 15.9 16.38142 -0.48142 1.883 -0.255620 -#> Dataset 8 A1 48 9.5 10.25357 -0.75357 1.883 -0.400124 -#> Dataset 8 A1 48 9.8 10.25357 -0.45357 1.883 -0.240833 -#> Dataset 8 A1 70 6.2 5.95728 0.24272 1.883 0.128878 -#> Dataset 8 A1 70 6.1 5.95728 0.14272 1.883 0.075781 -#> Dataset 9 parent 0 99.8 97.47274 2.32726 1.883 1.235697 -#> Dataset 9 parent 0 98.3 97.47274 0.82726 1.883 0.439246 -#> Dataset 9 parent 1 77.1 79.72257 -2.62257 1.883 -1.392500 -#> Dataset 9 parent 1 77.2 79.72257 -2.52257 1.883 -1.339404 -#> Dataset 9 parent 3 59.0 56.26497 2.73503 1.883 1.452212 -#> Dataset 9 parent 3 58.1 56.26497 1.83503 1.883 0.974342 -#> Dataset 9 parent 8 27.4 31.66985 -4.26985 1.883 -2.267151 -#> Dataset 9 parent 8 29.2 31.66985 -2.46985 1.883 -1.311410 -#> Dataset 9 parent 14 19.1 22.39789 -3.29789 1.883 -1.751071 -#> Dataset 9 parent 14 29.6 22.39789 7.20211 1.883 3.824090 -#> Dataset 9 parent 27 10.1 14.21758 -4.11758 1.883 -2.186301 -#> Dataset 9 parent 27 18.2 14.21758 3.98242 1.883 2.114537 -#> Dataset 9 parent 48 4.5 7.27921 -2.77921 1.883 -1.475671 -#> Dataset 9 parent 48 9.1 7.27921 1.82079 1.883 0.966780 -#> Dataset 9 parent 70 2.3 3.61470 -1.31470 1.883 -0.698065 -#> Dataset 9 parent 70 2.9 3.61470 -0.71470 1.883 -0.379485 -#> Dataset 9 parent 91 2.0 1.85303 0.14697 1.883 0.078038 -#> Dataset 9 parent 91 1.8 1.85303 -0.05303 1.883 -0.028155 -#> Dataset 9 parent 120 2.0 0.73645 1.26355 1.883 0.670906 -#> Dataset 9 parent 120 2.2 0.73645 1.46355 1.883 0.777099 -#> Dataset 9 A1 1 4.2 3.87843 0.32157 1.883 0.170743 -#> Dataset 9 A1 1 3.9 3.87843 0.02157 1.883 0.011453 -#> Dataset 9 A1 3 7.4 8.90535 -1.50535 1.883 -0.799291 -#> Dataset 9 A1 3 7.9 8.90535 -1.00535 1.883 -0.533807 -#> Dataset 9 A1 8 14.5 13.75172 0.74828 1.883 0.397312 -#> Dataset 9 A1 8 13.7 13.75172 -0.05172 1.883 -0.027462 -#> Dataset 9 A1 14 14.2 14.97541 -0.77541 1.883 -0.411715 -#> Dataset 9 A1 14 12.2 14.97541 -2.77541 1.883 -1.473650 -#> Dataset 9 A1 27 13.7 14.94728 -1.24728 1.883 -0.662266 -#> Dataset 9 A1 27 13.2 14.94728 -1.74728 1.883 -0.927750 -#> Dataset 9 A1 48 13.6 13.66078 -0.06078 1.883 -0.032272 -#> Dataset 9 A1 48 15.4 13.66078 1.73922 1.883 0.923470 -#> Dataset 9 A1 70 10.4 11.84899 -1.44899 1.883 -0.769365 -#> Dataset 9 A1 70 11.6 11.84899 -0.24899 1.883 -0.132204 -#> Dataset 9 A1 91 10.0 10.09177 -0.09177 1.883 -0.048727 -#> Dataset 9 A1 91 9.5 10.09177 -0.59177 1.883 -0.314211 -#> Dataset 9 A1 120 9.1 7.91379 1.18621 1.883 0.629841 -#> Dataset 9 A1 120 9.0 7.91379 1.08621 1.883 0.576744 -#> Dataset 10 parent 0 96.1 93.65257 2.44743 1.883 1.299505 -#> Dataset 10 parent 0 94.3 93.65257 0.64743 1.883 0.343763 -#> Dataset 10 parent 8 73.9 77.85906 -3.95906 1.883 -2.102132 -#> Dataset 10 parent 8 73.9 77.85906 -3.95906 1.883 -2.102132 -#> Dataset 10 parent 14 69.4 70.17143 -0.77143 1.883 -0.409606 -#> Dataset 10 parent 14 73.1 70.17143 2.92857 1.883 1.554974 -#> Dataset 10 parent 21 65.6 63.99188 1.60812 1.883 0.853862 -#> Dataset 10 parent 21 65.3 63.99188 1.30812 1.883 0.694572 -#> Dataset 10 parent 41 55.9 54.64292 1.25708 1.883 0.667467 -#> Dataset 10 parent 41 54.4 54.64292 -0.24292 1.883 -0.128985 -#> Dataset 10 parent 63 47.0 49.61303 -2.61303 1.883 -1.387433 -#> Dataset 10 parent 63 49.3 49.61303 -0.31303 1.883 -0.166207 -#> Dataset 10 parent 91 44.7 45.17807 -0.47807 1.883 -0.253839 -#> Dataset 10 parent 91 46.7 45.17807 1.52193 1.883 0.808096 -#> Dataset 10 parent 120 42.1 41.27970 0.82030 1.883 0.435552 -#> Dataset 10 parent 120 41.3 41.27970 0.02030 1.883 0.010778 -#> Dataset 10 A1 8 3.3 3.99294 -0.69294 1.883 -0.367929 -#> Dataset 10 A1 8 3.4 3.99294 -0.59294 1.883 -0.314832 -#> Dataset 10 A1 14 3.9 5.92756 -2.02756 1.883 -1.076570 -#> Dataset 10 A1 14 2.9 5.92756 -3.02756 1.883 -1.607538 -#> Dataset 10 A1 21 6.4 7.47313 -1.07313 1.883 -0.569799 -#> Dataset 10 A1 21 7.2 7.47313 -0.27313 1.883 -0.145025 -#> Dataset 10 A1 41 9.1 9.76819 -0.66819 1.883 -0.354786 -#> Dataset 10 A1 41 8.5 9.76819 -1.26819 1.883 -0.673367 -#> Dataset 10 A1 63 11.7 10.94733 0.75267 1.883 0.399643 -#> Dataset 10 A1 63 12.0 10.94733 1.05267 1.883 0.558933 -#> Dataset 10 A1 91 13.3 11.93773 1.36227 1.883 0.723321 -#> 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
+#> ds name time observed predicted residual std standardized +#> Dataset 6 parent 0 97.2 95.75408 1.445920 1.884 0.767479 +#> Dataset 6 parent 0 96.4 95.75408 0.645920 1.884 0.342847 +#> Dataset 6 parent 3 71.1 71.22466 -0.124662 1.884 -0.066169 +#> Dataset 6 parent 3 69.2 71.22466 -2.024662 1.884 -1.074669 +#> Dataset 6 parent 6 58.1 56.42290 1.677100 1.884 0.890187 +#> Dataset 6 parent 6 56.6 56.42290 0.177100 1.884 0.094003 +#> Dataset 6 parent 10 44.4 44.55255 -0.152554 1.884 -0.080974 +#> Dataset 6 parent 10 43.4 44.55255 -1.152554 1.884 -0.611763 +#> Dataset 6 parent 20 33.3 29.88846 3.411537 1.884 1.810807 +#> Dataset 6 parent 20 29.2 29.88846 -0.688463 1.884 -0.365429 +#> Dataset 6 parent 34 17.6 19.40826 -1.808260 1.884 -0.959805 +#> Dataset 6 parent 34 18.0 19.40826 -1.408260 1.884 -0.747489 +#> Dataset 6 parent 55 10.5 10.45560 0.044398 1.884 0.023566 +#> Dataset 6 parent 55 9.3 10.45560 -1.155602 1.884 -0.613381 +#> Dataset 6 parent 90 4.5 3.74026 0.759744 1.884 0.403264 +#> Dataset 6 parent 90 4.7 3.74026 0.959744 1.884 0.509421 +#> Dataset 6 parent 112 3.0 1.96015 1.039853 1.884 0.551943 +#> Dataset 6 parent 112 3.4 1.96015 1.439853 1.884 0.764258 +#> Dataset 6 parent 132 2.3 1.08940 1.210603 1.884 0.642575 +#> Dataset 6 parent 132 2.7 1.08940 1.610603 1.884 0.854890 +#> Dataset 6 A1 3 4.3 4.75601 -0.456009 1.884 -0.242045 +#> Dataset 6 A1 3 4.6 4.75601 -0.156009 1.884 -0.082808 +#> Dataset 6 A1 6 7.0 7.53839 -0.538391 1.884 -0.285772 +#> Dataset 6 A1 6 7.2 7.53839 -0.338391 1.884 -0.179614 +#> Dataset 6 A1 10 8.2 9.64728 -1.447276 1.884 -0.768198 +#> Dataset 6 A1 10 8.0 9.64728 -1.647276 1.884 -0.874356 +#> Dataset 6 A1 20 11.0 11.83954 -0.839545 1.884 -0.445621 +#> Dataset 6 A1 20 13.7 11.83954 1.860455 1.884 0.987509 +#> Dataset 6 A1 34 11.5 12.81233 -1.312327 1.884 -0.696569 +#> Dataset 6 A1 34 12.7 12.81233 -0.112327 1.884 -0.059622 +#> Dataset 6 A1 55 14.9 12.87919 2.020809 1.884 1.072624 +#> Dataset 6 A1 55 14.5 12.87919 1.620809 1.884 0.860308 +#> Dataset 6 A1 90 12.1 11.52464 0.575364 1.884 0.305397 +#> Dataset 6 A1 90 12.3 11.52464 0.775364 1.884 0.411555 +#> Dataset 6 A1 112 9.9 10.37694 -0.476938 1.884 -0.253153 +#> Dataset 6 A1 112 10.2 10.37694 -0.176938 1.884 -0.093917 +#> Dataset 6 A1 132 8.8 9.32474 -0.524742 1.884 -0.278528 +#> Dataset 6 A1 132 7.8 9.32474 -1.524742 1.884 -0.809317 +#> Dataset 7 parent 0 93.6 90.16918 3.430816 1.884 1.821040 +#> Dataset 7 parent 0 92.3 90.16918 2.130816 1.884 1.131014 +#> Dataset 7 parent 3 87.0 84.05442 2.945583 1.884 1.563483 +#> Dataset 7 parent 3 82.2 84.05442 -1.854417 1.884 -0.984304 +#> Dataset 7 parent 7 74.0 77.00960 -3.009596 1.884 -1.597461 +#> Dataset 7 parent 7 73.9 77.00960 -3.109596 1.884 -1.650540 +#> Dataset 7 parent 14 64.2 67.15684 -2.956840 1.884 -1.569459 +#> Dataset 7 parent 14 69.5 67.15684 2.343160 1.884 1.243724 +#> Dataset 7 parent 30 54.0 52.66290 1.337101 1.884 0.709719 +#> Dataset 7 parent 30 54.6 52.66290 1.937101 1.884 1.028192 +#> Dataset 7 parent 60 41.1 40.04995 1.050050 1.884 0.557355 +#> Dataset 7 parent 60 38.4 40.04995 -1.649950 1.884 -0.875775 +#> Dataset 7 parent 90 32.5 34.09675 -1.596746 1.884 -0.847535 +#> Dataset 7 parent 90 35.5 34.09675 1.403254 1.884 0.744832 +#> Dataset 7 parent 120 28.1 30.12281 -2.022814 1.884 -1.073688 +#> Dataset 7 parent 120 29.0 30.12281 -1.122814 1.884 -0.595977 +#> Dataset 7 parent 180 26.5 24.10888 2.391123 1.884 1.269182 +#> Dataset 7 parent 180 27.6 24.10888 3.491123 1.884 1.853050 +#> Dataset 7 A1 3 3.9 2.77684 1.123161 1.884 0.596161 +#> Dataset 7 A1 3 3.1 2.77684 0.323161 1.884 0.171530 +#> Dataset 7 A1 7 6.9 5.96705 0.932950 1.884 0.495200 +#> Dataset 7 A1 7 6.6 5.96705 0.632950 1.884 0.335963 +#> Dataset 7 A1 14 10.4 10.40535 -0.005348 1.884 -0.002839 +#> Dataset 7 A1 14 8.3 10.40535 -2.105348 1.884 -1.117496 +#> Dataset 7 A1 30 14.4 16.83722 -2.437216 1.884 -1.293648 +#> Dataset 7 A1 30 13.7 16.83722 -3.137216 1.884 -1.665200 +#> Dataset 7 A1 60 22.1 22.15018 -0.050179 1.884 -0.026635 +#> Dataset 7 A1 60 22.3 22.15018 0.149821 1.884 0.079523 +#> Dataset 7 A1 90 27.5 24.36286 3.137143 1.884 1.665161 +#> Dataset 7 A1 90 25.4 24.36286 1.037143 1.884 0.550504 +#> Dataset 7 A1 120 28.0 25.64064 2.359361 1.884 1.252323 +#> Dataset 7 A1 120 26.6 25.64064 0.959361 1.884 0.509218 +#> Dataset 7 A1 180 25.8 27.25486 -1.454858 1.884 -0.772223 +#> Dataset 7 A1 180 25.3 27.25486 -1.954858 1.884 -1.037617 +#> Dataset 8 parent 0 91.9 91.72652 0.173479 1.884 0.092081 +#> Dataset 8 parent 0 90.8 91.72652 -0.926521 1.884 -0.491787 +#> Dataset 8 parent 1 64.9 67.22810 -2.328104 1.884 -1.235732 +#> Dataset 8 parent 1 66.2 67.22810 -1.028104 1.884 -0.545706 +#> Dataset 8 parent 3 43.5 41.46375 2.036251 1.884 1.080820 +#> Dataset 8 parent 3 44.1 41.46375 2.636251 1.884 1.399293 +#> Dataset 8 parent 8 18.3 19.83597 -1.535968 1.884 -0.815275 +#> Dataset 8 parent 8 18.1 19.83597 -1.735968 1.884 -0.921433 +#> Dataset 8 parent 14 10.2 10.34793 -0.147927 1.884 -0.078518 +#> Dataset 8 parent 14 10.8 10.34793 0.452073 1.884 0.239956 +#> Dataset 8 parent 27 4.9 2.67641 2.223595 1.884 1.180260 +#> Dataset 8 parent 27 3.3 2.67641 0.623595 1.884 0.330997 +#> Dataset 8 parent 48 1.6 0.30218 1.297822 1.884 0.688870 +#> Dataset 8 parent 48 1.5 0.30218 1.197822 1.884 0.635791 +#> Dataset 8 parent 70 1.1 0.03075 1.069248 1.884 0.567545 +#> Dataset 8 parent 70 0.9 0.03075 0.869248 1.884 0.461388 +#> Dataset 8 A1 1 9.6 7.74066 1.859342 1.884 0.986918 +#> Dataset 8 A1 1 7.7 7.74066 -0.040658 1.884 -0.021581 +#> Dataset 8 A1 3 15.0 15.37549 -0.375495 1.884 -0.199309 +#> Dataset 8 A1 3 15.1 15.37549 -0.275495 1.884 -0.146230 +#> Dataset 8 A1 8 21.2 19.95900 1.241003 1.884 0.658711 +#> Dataset 8 A1 8 21.1 19.95900 1.141003 1.884 0.605632 +#> Dataset 8 A1 14 19.7 19.92898 -0.228978 1.884 -0.121539 +#> Dataset 8 A1 14 18.9 19.92898 -1.028978 1.884 -0.546170 +#> Dataset 8 A1 27 17.5 16.34046 1.159536 1.884 0.615469 +#> Dataset 8 A1 27 15.9 16.34046 -0.440464 1.884 -0.233793 +#> Dataset 8 A1 48 9.5 10.12131 -0.621313 1.884 -0.329786 +#> Dataset 8 A1 48 9.8 10.12131 -0.321313 1.884 -0.170550 +#> Dataset 8 A1 70 6.2 5.84753 0.352469 1.884 0.187087 +#> Dataset 8 A1 70 6.1 5.84753 0.252469 1.884 0.134008 +#> Dataset 9 parent 0 99.8 98.23600 1.564002 1.884 0.830155 +#> Dataset 9 parent 0 98.3 98.23600 0.064002 1.884 0.033972 +#> Dataset 9 parent 1 77.1 79.68007 -2.580074 1.884 -1.369475 +#> Dataset 9 parent 1 77.2 79.68007 -2.480074 1.884 -1.316396 +#> Dataset 9 parent 3 59.0 55.81142 3.188584 1.884 1.692465 +#> Dataset 9 parent 3 58.1 55.81142 2.288584 1.884 1.214755 +#> Dataset 9 parent 8 27.4 31.81995 -4.419948 1.884 -2.346060 +#> Dataset 9 parent 8 29.2 31.81995 -2.619948 1.884 -1.390640 +#> Dataset 9 parent 14 19.1 22.78328 -3.683282 1.884 -1.955046 +#> Dataset 9 parent 14 29.6 22.78328 6.816718 1.884 3.618240 +#> Dataset 9 parent 27 10.1 14.15172 -4.051720 1.884 -2.150609 +#> Dataset 9 parent 27 18.2 14.15172 4.048280 1.884 2.148783 +#> Dataset 9 parent 48 4.5 6.86094 -2.360941 1.884 -1.253162 +#> Dataset 9 parent 48 9.1 6.86094 2.239059 1.884 1.188468 +#> Dataset 9 parent 70 2.3 3.21580 -0.915798 1.884 -0.486096 +#> Dataset 9 parent 70 2.9 3.21580 -0.315798 1.884 -0.167622 +#> Dataset 9 parent 91 2.0 1.56010 0.439897 1.884 0.233492 +#> Dataset 9 parent 91 1.8 1.56010 0.239897 1.884 0.127335 +#> Dataset 9 parent 120 2.0 0.57458 1.425424 1.884 0.756600 +#> Dataset 9 parent 120 2.2 0.57458 1.625424 1.884 0.862757 +#> Dataset 9 A1 1 4.2 4.01796 0.182037 1.884 0.096623 +#> Dataset 9 A1 1 3.9 4.01796 -0.117963 1.884 -0.062613 +#> Dataset 9 A1 3 7.4 9.08527 -1.685270 1.884 -0.894523 +#> Dataset 9 A1 3 7.9 9.08527 -1.185270 1.884 -0.629129 +#> Dataset 9 A1 8 14.5 13.75054 0.749457 1.884 0.397804 +#> Dataset 9 A1 8 13.7 13.75054 -0.050543 1.884 -0.026827 +#> Dataset 9 A1 14 14.2 14.91180 -0.711804 1.884 -0.377818 +#> Dataset 9 A1 14 12.2 14.91180 -2.711804 1.884 -1.439396 +#> Dataset 9 A1 27 13.7 14.97813 -1.278129 1.884 -0.678417 +#> Dataset 9 A1 27 13.2 14.97813 -1.778129 1.884 -0.943812 +#> Dataset 9 A1 48 13.6 13.75574 -0.155745 1.884 -0.082668 +#> Dataset 9 A1 48 15.4 13.75574 1.644255 1.884 0.872753 +#> Dataset 9 A1 70 10.4 11.92861 -1.528608 1.884 -0.811369 +#> Dataset 9 A1 70 11.6 11.92861 -0.328608 1.884 -0.174422 +#> Dataset 9 A1 91 10.0 10.14395 -0.143947 1.884 -0.076405 +#> Dataset 9 A1 91 9.5 10.14395 -0.643947 1.884 -0.341800 +#> Dataset 9 A1 120 9.1 7.93869 1.161307 1.884 0.616409 +#> Dataset 9 A1 120 9.0 7.93869 1.061307 1.884 0.563330 +#> Dataset 10 parent 0 96.1 93.65914 2.440862 1.884 1.295583 +#> Dataset 10 parent 0 94.3 93.65914 0.640862 1.884 0.340163 +#> Dataset 10 parent 8 73.9 77.83065 -3.930647 1.884 -2.086344 +#> Dataset 10 parent 8 73.9 77.83065 -3.930647 1.884 -2.086344 +#> Dataset 10 parent 14 69.4 70.15862 -0.758619 1.884 -0.402667 +#> Dataset 10 parent 14 73.1 70.15862 2.941381 1.884 1.561253 +#> Dataset 10 parent 21 65.6 64.00840 1.591600 1.884 0.844804 +#> Dataset 10 parent 21 65.3 64.00840 1.291600 1.884 0.685567 +#> Dataset 10 parent 41 55.9 54.71192 1.188076 1.884 0.630618 +#> Dataset 10 parent 41 54.4 54.71192 -0.311924 1.884 -0.165566 +#> Dataset 10 parent 63 47.0 49.66775 -2.667747 1.884 -1.416011 +#> Dataset 10 parent 63 49.3 49.66775 -0.367747 1.884 -0.195196 +#> Dataset 10 parent 91 44.7 45.17119 -0.471186 1.884 -0.250101 +#> Dataset 10 parent 91 46.7 45.17119 1.528814 1.884 0.811478 +#> Dataset 10 parent 120 42.1 41.20430 0.895699 1.884 0.475427 +#> Dataset 10 parent 120 41.3 41.20430 0.095699 1.884 0.050796 +#> Dataset 10 A1 8 3.3 4.00920 -0.709204 1.884 -0.376438 +#> Dataset 10 A1 8 3.4 4.00920 -0.609204 1.884 -0.323359 +#> Dataset 10 A1 14 3.9 5.94267 -2.042668 1.884 -1.084226 +#> Dataset 10 A1 14 2.9 5.94267 -3.042668 1.884 -1.615015 +#> Dataset 10 A1 21 6.4 7.48222 -1.082219 1.884 -0.574430 +#> Dataset 10 A1 21 7.2 7.48222 -0.282219 1.884 -0.149799 +#> Dataset 10 A1 41 9.1 9.76246 -0.662460 1.884 -0.351626 +#> Dataset 10 A1 41 8.5 9.76246 -1.262460 1.884 -0.670100 +#> Dataset 10 A1 63 11.7 10.93972 0.760278 1.884 0.403547 +#> Dataset 10 A1 63 12.0 10.93972 1.060278 1.884 0.562784 +#> Dataset 10 A1 91 13.3 11.93666 1.363337 1.884 0.723645 +#> Dataset 10 A1 91 13.2 11.93666 1.263337 1.884 0.670566 +#> Dataset 10 A1 120 14.3 12.78218 1.517817 1.884 0.805641 +#> Dataset 10 A1 120 12.1 12.78218 -0.682183 1.884 -0.362095
# 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)) -- cgit v1.2.1 From 137612045c23198f10d6e8612c32e266c2a6c00e Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Thu, 29 Jul 2021 12:17:56 +0200 Subject: Go back to 1.0.x version, 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 15271c8a..0334e0e1 100644 --- a/docs/dev/reference/saem.html +++ b/docs/dev/reference/saem.html @@ -74,7 +74,7 @@ Expectation Maximisation algorithm (SAEM)." /> mkin - 1.1.0 + 1.0.5
@@ -288,27 +288,27 @@ 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] "Tue Jul 27 16:31:02 2021" +#> [1] "Thu Jul 29 12:14:07 2021" #> .... #> Minimisation finished -#> [1] "Tue Jul 27 16:31:04 2021"
+#> [1] "Thu Jul 29 12:14:08 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] "Tue Jul 27 16:31:06 2021" +#> [1] "Thu Jul 29 12:14:11 2021" #> .... #> Minimisation finished -#> [1] "Tue Jul 27 16:31:07 2021"
f_saem_fomc <- saem(f_mmkin_parent["FOMC", ]) +#> [1] "Thu Jul 29 12:14:12 2021"
f_saem_fomc <- saem(f_mmkin_parent["FOMC", ])
#> Running main SAEM algorithm -#> [1] "Tue Jul 27 16:31:07 2021" +#> [1] "Thu Jul 29 12:14:12 2021" #> .... #> Minimisation finished -#> [1] "Tue Jul 27 16:31:09 2021"
f_saem_dfop <- saem(f_mmkin_parent["DFOP", ]) +#> [1] "Thu Jul 29 12:14:14 2021"
f_saem_dfop <- saem(f_mmkin_parent["DFOP", ])
#> Running main SAEM algorithm -#> [1] "Tue Jul 27 16:31:10 2021" +#> [1] "Thu Jul 29 12:14:15 2021" #> .... #> Minimisation finished -#> [1] "Tue Jul 27 16:31:12 2021"
+#> [1] "Thu Jul 29 12:14:18 2021"
# The returned saem.mmkin object contains an SaemixObject, therefore we can use # functions from saemix library(saemix) @@ -357,10 +357,10 @@ 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] "Tue Jul 27 16:31:16 2021" +#> [1] "Thu Jul 29 12:14:21 2021" #> .... #> Minimisation finished -#> [1] "Tue Jul 27 16:31:20 2021"
compare.saemix(f_saem_fomc$so, f_saem_fomc_tc$so) +#> [1] "Thu Jul 29 12:14:27 2021"
compare.saemix(f_saem_fomc$so, f_saem_fomc_tc$so)
#> Likelihoods calculated by importance sampling
#> AIC BIC #> 1 467.7096 464.9757 #> 2 469.6831 466.5586
@@ -381,15 +381,15 @@ using mmkin.

# four minutes f_saem_sfo_sfo <- saem(f_mmkin["SFO-SFO", ])
#> Running main SAEM algorithm -#> [1] "Tue Jul 27 16:31:24 2021" +#> [1] "Thu Jul 29 12:14:31 2021" #> .... #> Minimisation finished -#> [1] "Tue Jul 27 16:31:29 2021"
f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ]) +#> [1] "Thu Jul 29 12:14:36 2021"
f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ])
#> Running main SAEM algorithm -#> [1] "Tue Jul 27 16:31:30 2021" +#> [1] "Thu Jul 29 12:14:36 2021" #> .... #> Minimisation finished -#> [1] "Tue Jul 27 16:31:38 2021"
# We can use print, plot and summary methods to check the results +#> [1] "Thu Jul 29 12:14:46 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: @@ -430,10 +430,10 @@ using mmkin.

#> SD.g_qlogis 0.44816 -1.25437 2.1507
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: 1.1.0 +#> mkin version used for pre-fitting: 1.0.5 #> R version used for fitting: 4.1.0 -#> Date of fit: Tue Jul 27 16:31:39 2021 -#> Date of summary: Tue Jul 27 16:31:39 2021 +#> Date of fit: Thu Jul 29 12:14:46 2021 +#> Date of summary: Thu Jul 29 12:14:46 2021 #> #> Equations: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -448,7 +448,7 @@ using mmkin.

#> #> Model predictions using solution type analytical #> -#> Fitted in 9.479 s using 300, 100 iterations +#> Fitted in 9.987 s using 300, 100 iterations #> #> Variance model: Constant variance #> -- cgit v1.2.1 From 51fab94230e926cec690dc455964bd797a97b7c7 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Wed, 4 Aug 2021 16:37:52 +0200 Subject: Improve AIC table in vignette --- docs/dev/reference/saem.html | 34 +++++++++++++++++----------------- 1 file changed, 17 insertions(+), 17 deletions(-) (limited to 'docs/dev/reference/saem.html') diff --git a/docs/dev/reference/saem.html b/docs/dev/reference/saem.html index 0334e0e1..620173b2 100644 --- a/docs/dev/reference/saem.html +++ b/docs/dev/reference/saem.html @@ -288,27 +288,27 @@ 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] "Thu Jul 29 12:14:07 2021" +#> [1] "Wed Aug 4 16:22:05 2021" #> .... #> Minimisation finished -#> [1] "Thu Jul 29 12:14:08 2021"
+#> [1] "Wed Aug 4 16:22:06 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] "Thu Jul 29 12:14:11 2021" +#> [1] "Wed Aug 4 16:22:08 2021" #> .... #> Minimisation finished -#> [1] "Thu Jul 29 12:14:12 2021"
f_saem_fomc <- saem(f_mmkin_parent["FOMC", ]) +#> [1] "Wed Aug 4 16:22:10 2021"
f_saem_fomc <- saem(f_mmkin_parent["FOMC", ])
#> Running main SAEM algorithm -#> [1] "Thu Jul 29 12:14:12 2021" +#> [1] "Wed Aug 4 16:22:10 2021" #> .... #> Minimisation finished -#> [1] "Thu Jul 29 12:14:14 2021"
f_saem_dfop <- saem(f_mmkin_parent["DFOP", ]) +#> [1] "Wed Aug 4 16:22:12 2021"
f_saem_dfop <- saem(f_mmkin_parent["DFOP", ])
#> Running main SAEM algorithm -#> [1] "Thu Jul 29 12:14:15 2021" +#> [1] "Wed Aug 4 16:22:12 2021" #> .... #> Minimisation finished -#> [1] "Thu Jul 29 12:14:18 2021"
+#> [1] "Wed Aug 4 16:22:16 2021"
# The returned saem.mmkin object contains an SaemixObject, therefore we can use # functions from saemix library(saemix) @@ -357,10 +357,10 @@ 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] "Thu Jul 29 12:14:21 2021" +#> [1] "Wed Aug 4 16:22:19 2021" #> .... #> Minimisation finished -#> [1] "Thu Jul 29 12:14:27 2021"
compare.saemix(f_saem_fomc$so, f_saem_fomc_tc$so) +#> [1] "Wed Aug 4 16:22:24 2021"
compare.saemix(f_saem_fomc$so, f_saem_fomc_tc$so)
#> Likelihoods calculated by importance sampling
#> AIC BIC #> 1 467.7096 464.9757 #> 2 469.6831 466.5586
@@ -381,15 +381,15 @@ using mmkin.

# four minutes f_saem_sfo_sfo <- saem(f_mmkin["SFO-SFO", ])
#> Running main SAEM algorithm -#> [1] "Thu Jul 29 12:14:31 2021" +#> [1] "Wed Aug 4 16:22:27 2021" #> .... #> Minimisation finished -#> [1] "Thu Jul 29 12:14:36 2021"
f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ]) +#> [1] "Wed Aug 4 16:22:32 2021"
f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ])
#> Running main SAEM algorithm -#> [1] "Thu Jul 29 12:14:36 2021" +#> [1] "Wed Aug 4 16:22:33 2021" #> .... #> Minimisation finished -#> [1] "Thu Jul 29 12:14:46 2021"
# We can use print, plot and summary methods to check the results +#> [1] "Wed Aug 4 16:22:42 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: @@ -432,8 +432,8 @@ using mmkin.

#> saemix version used for fitting: 3.1.9000 #> mkin version used for pre-fitting: 1.0.5 #> R version used for fitting: 4.1.0 -#> Date of fit: Thu Jul 29 12:14:46 2021 -#> Date of summary: Thu Jul 29 12:14:46 2021 +#> Date of fit: Wed Aug 4 16:22:43 2021 +#> Date of summary: Wed Aug 4 16:22:43 2021 #> #> Equations: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -448,7 +448,7 @@ using mmkin.

#> #> Model predictions using solution type analytical #> -#> Fitted in 9.987 s using 300, 100 iterations +#> Fitted in 10.143 s using 300, 100 iterations #> #> Variance model: Constant variance #> -- cgit v1.2.1 From c41381a961263c28d60976e68923157916c78b15 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Thu, 16 Sep 2021 15:31:13 +0200 Subject: Adapt and improve the dimethenamid vignette Adapt to the corrected data and unify control parameters for saemix and nlmixr with saem. Update docs --- docs/dev/reference/saem.html | 40 ++++++++++++++++++++-------------------- 1 file changed, 20 insertions(+), 20 deletions(-) (limited to 'docs/dev/reference/saem.html') diff --git a/docs/dev/reference/saem.html b/docs/dev/reference/saem.html index 620173b2..8d986126 100644 --- a/docs/dev/reference/saem.html +++ b/docs/dev/reference/saem.html @@ -74,7 +74,7 @@ Expectation Maximisation algorithm (SAEM)." /> mkin - 1.0.5 + 1.1.0
@@ -288,27 +288,27 @@ 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] "Wed Aug 4 16:22:05 2021" +#> [1] "Thu Sep 16 14:34:42 2021" #> .... #> Minimisation finished -#> [1] "Wed Aug 4 16:22:06 2021"
+#> [1] "Thu Sep 16 14:34:43 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] "Wed Aug 4 16:22:08 2021" +#> [1] "Thu Sep 16 14:34:45 2021" #> .... #> Minimisation finished -#> [1] "Wed Aug 4 16:22:10 2021"
f_saem_fomc <- saem(f_mmkin_parent["FOMC", ]) +#> [1] "Thu Sep 16 14:34:47 2021"
f_saem_fomc <- saem(f_mmkin_parent["FOMC", ])
#> Running main SAEM algorithm -#> [1] "Wed Aug 4 16:22:10 2021" +#> [1] "Thu Sep 16 14:34:47 2021" #> .... #> Minimisation finished -#> [1] "Wed Aug 4 16:22:12 2021"
f_saem_dfop <- saem(f_mmkin_parent["DFOP", ]) +#> [1] "Thu Sep 16 14:34:49 2021"
f_saem_dfop <- saem(f_mmkin_parent["DFOP", ])
#> Running main SAEM algorithm -#> [1] "Wed Aug 4 16:22:12 2021" +#> [1] "Thu Sep 16 14:34:49 2021" #> .... #> Minimisation finished -#> [1] "Wed Aug 4 16:22:16 2021"
+#> [1] "Thu Sep 16 14:34:52 2021"
# The returned saem.mmkin object contains an SaemixObject, therefore we can use # functions from saemix library(saemix) @@ -357,10 +357,10 @@ 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] "Wed Aug 4 16:22:19 2021" +#> [1] "Thu Sep 16 14:34:55 2021" #> .... #> Minimisation finished -#> [1] "Wed Aug 4 16:22:24 2021"
compare.saemix(f_saem_fomc$so, f_saem_fomc_tc$so) +#> [1] "Thu Sep 16 14:35:00 2021"
compare.saemix(f_saem_fomc$so, f_saem_fomc_tc$so)
#> Likelihoods calculated by importance sampling
#> AIC BIC #> 1 467.7096 464.9757 #> 2 469.6831 466.5586
@@ -381,15 +381,15 @@ using mmkin.

# four minutes f_saem_sfo_sfo <- saem(f_mmkin["SFO-SFO", ])
#> Running main SAEM algorithm -#> [1] "Wed Aug 4 16:22:27 2021" +#> [1] "Thu Sep 16 14:35:03 2021" #> .... #> Minimisation finished -#> [1] "Wed Aug 4 16:22:32 2021"
f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ]) +#> [1] "Thu Sep 16 14:35:08 2021"
f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ])
#> Running main SAEM algorithm -#> [1] "Wed Aug 4 16:22:33 2021" +#> [1] "Thu Sep 16 14:35:08 2021" #> .... #> Minimisation finished -#> [1] "Wed Aug 4 16:22:42 2021"
# We can use print, plot and summary methods to check the results +#> [1] "Thu Sep 16 14:35:17 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: @@ -430,10 +430,10 @@ using mmkin.

#> SD.g_qlogis 0.44816 -1.25437 2.1507
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: 1.0.5 -#> R version used for fitting: 4.1.0 -#> Date of fit: Wed Aug 4 16:22:43 2021 -#> Date of summary: Wed Aug 4 16:22:43 2021 +#> mkin version used for pre-fitting: 1.1.0 +#> R version used for fitting: 4.1.1 +#> Date of fit: Thu Sep 16 14:35:18 2021 +#> Date of summary: Thu Sep 16 14:35:18 2021 #> #> Equations: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -448,7 +448,7 @@ using mmkin.

#> #> Model predictions using solution type analytical #> -#> Fitted in 10.143 s using 300, 100 iterations +#> Fitted in 9.349 s using 300, 100 iterations #> #> Variance model: Constant variance #> -- cgit v1.2.1 From ff83d8b2ba623513d92ac90fac4a1b0ec98c2cb5 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Tue, 5 Oct 2021 17:33:52 +0200 Subject: Update docs --- docs/dev/reference/saem.html | 388 ++----------------------------------------- 1 file changed, 16 insertions(+), 372 deletions(-) (limited to 'docs/dev/reference/saem.html') diff --git a/docs/dev/reference/saem.html b/docs/dev/reference/saem.html index 8d986126..83a62359 100644 --- a/docs/dev/reference/saem.html +++ b/docs/dev/reference/saem.html @@ -287,28 +287,12 @@ using mmkin.

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] "Thu Sep 16 14:34:42 2021" -#> .... -#> Minimisation finished -#> [1] "Thu Sep 16 14:34:43 2021"
+
#> Warning: argument is not a function
#>
#> Error in rxModelVars_(obj): Not compatible with STRSXP: [type=NULL].
f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE) f_saem_sfo <- saem(f_mmkin_parent["SFO", ]) -
#> Running main SAEM algorithm -#> [1] "Thu Sep 16 14:34:45 2021" -#> .... -#> Minimisation finished -#> [1] "Thu Sep 16 14:34:47 2021"
f_saem_fomc <- saem(f_mmkin_parent["FOMC", ]) -
#> Running main SAEM algorithm -#> [1] "Thu Sep 16 14:34:47 2021" -#> .... -#> Minimisation finished -#> [1] "Thu Sep 16 14:34:49 2021"
f_saem_dfop <- saem(f_mmkin_parent["DFOP", ]) -
#> Running main SAEM algorithm -#> [1] "Thu Sep 16 14:34:49 2021" -#> .... -#> Minimisation finished -#> [1] "Thu Sep 16 14:34:52 2021"
+
#> Warning: argument is not a function
#>
#> Error in rxModelVars_(obj): Not compatible with STRSXP: [type=NULL].
f_saem_fomc <- saem(f_mmkin_parent["FOMC", ]) +
#> Warning: argument is not a function
#>
#> Error in rxModelVars_(obj): Not compatible with STRSXP: [type=NULL].
f_saem_dfop <- saem(f_mmkin_parent["DFOP", ]) +
#> Warning: argument is not a function
#>
#> Error in rxModelVars_(obj): Not compatible with STRSXP: [type=NULL].
# The returned saem.mmkin object contains an SaemixObject, therefore we can use # functions from saemix library(saemix) @@ -317,53 +301,15 @@ using mmkin.

#> Attaching package: ‘saemix’
#> The following object is masked from ‘package:RxODE’: #> #> phi
compare.saemix(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so) -
#> Likelihoods calculated by importance sampling
#> AIC BIC -#> 1 624.2484 622.2956 -#> 2 467.7096 464.9757 -#> 3 495.4373 491.9222
plot(f_saem_fomc$so, plot.type = "convergence") -
#> Plotting convergence plots
plot(f_saem_fomc$so, plot.type = "individual.fit") -
#> Plotting individual fits
plot(f_saem_fomc$so, plot.type = "npde") -
#> Simulating data using nsim = 1000 simulated datasets -#> Computing WRES and npde . -#> Plotting npde
#> --------------------------------------------- -#> Distribution of npde: -#> mean= -0.01528 (SE= 0.098 ) -#> variance= 0.862 (SE= 0.13 ) -#> skewness= 0.5016 -#> kurtosis= 1.18 -#> --------------------------------------------- -#> -#> Statistical tests -#> Wilcoxon signed rank test : 0.679 -#> Fisher variance test : 0.36 -#> SW test of normality : 0.0855 . -#> Global adjusted p-value : 0.257 -#> --- -#> Signif. codes: '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 -#> ---------------------------------------------
plot(f_saem_fomc$so, plot.type = "vpc") -
#> Performing simulations under the model. -#> Plotting VPC -#> Method used for VPC: binning by quantiles on X , dividing into the following intervals -#> Interval Centered.On -#> 1 (-1,3] 1.3 -#> 2 (3,8] 7.4 -#> 3 (8,14] 13.2 -#> 4 (14,21] 20.5 -#> 5 (21,37.7] 29.5 -#> 6 (37.7,60] 50.4 -#> 7 (60,90] 76.6 -#> 8 (90,120] 109.0 -#> 9 (120,180] 156.0
+
#> Error in compare.saemix(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so): object 'f_saem_sfo' not found
plot(f_saem_fomc$so, plot.type = "convergence") +
#> Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'plot': object 'f_saem_fomc' not found
plot(f_saem_fomc$so, plot.type = "individual.fit") +
#> Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'plot': object 'f_saem_fomc' not found
plot(f_saem_fomc$so, plot.type = "npde") +
#> Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'plot': object 'f_saem_fomc' not found
plot(f_saem_fomc$so, plot.type = "vpc") +
#> Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'plot': object 'f_saem_fomc' not found
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] "Thu Sep 16 14:34:55 2021" -#> .... -#> Minimisation finished -#> [1] "Thu Sep 16 14:35:00 2021"
compare.saemix(f_saem_fomc$so, f_saem_fomc_tc$so) -
#> Likelihoods calculated by importance sampling
#> AIC BIC -#> 1 467.7096 464.9757 -#> 2 469.6831 466.5586
+
#> Warning: argument is not a function
#>
#> Error in rxModelVars_(obj): Not compatible with STRSXP: [type=NULL].
compare.saemix(f_saem_fomc$so, f_saem_fomc_tc$so) +
#> Error in compare.saemix(f_saem_fomc$so, f_saem_fomc_tc$so): object 'f_saem_fomc' not found
sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"), A1 = mkinsub("SFO"))
#> Temporary DLL for differentials generated and loaded
fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"), @@ -380,314 +326,12 @@ using mmkin.

# 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] "Thu Sep 16 14:35:03 2021" -#> .... -#> Minimisation finished -#> [1] "Thu Sep 16 14:35:08 2021"
f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ]) -
#> Running main SAEM algorithm -#> [1] "Thu Sep 16 14:35:08 2021" -#> .... -#> Minimisation finished -#> [1] "Thu Sep 16 14:35:17 2021"
# We can use print, plot and summary methods to check the results +
#> Warning: argument is not a function
#>
#> Error in rxModelVars_(obj): Not compatible with STRSXP: [type=NULL].
f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ]) +
#> Warning: argument is not a function
#>
#> Error in rxModelVars_(obj): Not compatible with STRSXP: [type=NULL].
# 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: -#> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * -#> time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) -#> * parent -#> d_A1/dt = + f_parent_to_A1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) -#> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * -#> exp(-k2 * time))) * parent - k_A1 * A1 -#> -#> Data: -#> 170 observations of 2 variable(s) grouped in 5 datasets -#> -#> Likelihood computed by importance sampling -#> AIC BIC logLik -#> 839.6 834.6 -406.8 -#> -#> Fitted parameters: -#> estimate lower upper -#> parent_0 93.80521 91.22487 96.3856 -#> log_k_A1 -6.06244 -8.26517 -3.8597 -#> f_parent_qlogis -0.97319 -1.37024 -0.5761 -#> log_k1 -2.55394 -4.00815 -1.0997 -#> log_k2 -3.47160 -5.18763 -1.7556 -#> g_qlogis -0.09324 -1.42737 1.2409 -#> Var.parent_0 7.42157 -3.25683 18.1000 -#> Var.log_k_A1 4.22850 -2.46339 10.9204 -#> Var.f_parent_qlogis 0.19803 -0.05541 0.4515 -#> Var.log_k1 2.28644 -0.86079 5.4337 -#> Var.log_k2 3.35626 -1.14639 7.8589 -#> Var.g_qlogis 0.20084 -1.32516 1.7268 -#> a.1 1.88399 1.66794 2.1000 -#> SD.parent_0 2.72425 0.76438 4.6841 -#> SD.log_k_A1 2.05633 0.42919 3.6835 -#> SD.f_parent_qlogis 0.44501 0.16025 0.7298 -#> SD.log_k1 1.51210 0.47142 2.5528 -#> SD.log_k2 1.83201 0.60313 3.0609 -#> SD.g_qlogis 0.44816 -1.25437 2.1507
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: 1.1.0 -#> R version used for fitting: 4.1.1 -#> Date of fit: Thu Sep 16 14:35:18 2021 -#> Date of summary: Thu Sep 16 14:35:18 2021 -#> -#> Equations: -#> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * -#> time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) -#> * parent -#> d_A1/dt = + f_parent_to_A1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) -#> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * -#> exp(-k2 * time))) * parent - k_A1 * A1 -#> -#> Data: -#> 170 observations of 2 variable(s) grouped in 5 datasets -#> -#> Model predictions using solution type analytical -#> -#> Fitted in 9.349 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 -#> 93.8102 -5.3734 -0.9711 -1.8799 -4.2708 -#> g_qlogis -#> 0.1356 -#> -#> Fixed degradation parameter values: -#> None -#> -#> Results: -#> -#> Likelihood computed by importance sampling -#> AIC BIC logLik -#> 839.6 834.6 -406.8 -#> -#> Optimised parameters: -#> est. lower upper -#> parent_0 93.80521 91.225 96.3856 -#> log_k_A1 -6.06244 -8.265 -3.8597 -#> f_parent_qlogis -0.97319 -1.370 -0.5761 -#> log_k1 -2.55394 -4.008 -1.0997 -#> log_k2 -3.47160 -5.188 -1.7556 -#> g_qlogis -0.09324 -1.427 1.2409 -#> -#> Correlation: -#> prnt_0 lg__A1 f_prn_ log_k1 log_k2 -#> log_k_A1 -0.014 -#> f_parent_qlogis -0.025 0.054 -#> log_k1 0.027 -0.003 -0.005 -#> log_k2 0.011 0.005 -0.002 -0.070 -#> g_qlogis -0.067 -0.009 0.011 -0.189 -0.171 -#> -#> Random effects: -#> est. lower upper -#> SD.parent_0 2.7243 0.7644 4.6841 -#> SD.log_k_A1 2.0563 0.4292 3.6835 -#> SD.f_parent_qlogis 0.4450 0.1602 0.7298 -#> SD.log_k1 1.5121 0.4714 2.5528 -#> SD.log_k2 1.8320 0.6031 3.0609 -#> SD.g_qlogis 0.4482 -1.2544 2.1507 -#> -#> Variance model: -#> est. lower upper -#> a.1 1.884 1.668 2.1 -#> -#> Backtransformed parameters: -#> est. lower upper -#> parent_0 93.805214 9.122e+01 96.38556 -#> k_A1 0.002329 2.573e-04 0.02107 -#> f_parent_to_A1 0.274245 2.026e-01 0.35982 -#> k1 0.077775 1.817e-02 0.33296 -#> k2 0.031067 5.585e-03 0.17281 -#> g 0.476707 1.935e-01 0.77572 -#> -#> Resulting formation fractions: -#> ff -#> parent_A1 0.2742 -#> parent_sink 0.7258 -#> -#> Estimated disappearance times: -#> DT50 DT90 DT50back DT50_k1 DT50_k2 -#> parent 13.96 55.4 16.68 8.912 22.31 -#> A1 297.65 988.8 NA NA NA -#> -#> Data: -#> ds name time observed predicted residual std standardized -#> Dataset 6 parent 0 97.2 95.75408 1.445920 1.884 0.767479 -#> Dataset 6 parent 0 96.4 95.75408 0.645920 1.884 0.342847 -#> Dataset 6 parent 3 71.1 71.22466 -0.124662 1.884 -0.066169 -#> Dataset 6 parent 3 69.2 71.22466 -2.024662 1.884 -1.074669 -#> Dataset 6 parent 6 58.1 56.42290 1.677100 1.884 0.890187 -#> Dataset 6 parent 6 56.6 56.42290 0.177100 1.884 0.094003 -#> Dataset 6 parent 10 44.4 44.55255 -0.152554 1.884 -0.080974 -#> Dataset 6 parent 10 43.4 44.55255 -1.152554 1.884 -0.611763 -#> Dataset 6 parent 20 33.3 29.88846 3.411537 1.884 1.810807 -#> Dataset 6 parent 20 29.2 29.88846 -0.688463 1.884 -0.365429 -#> Dataset 6 parent 34 17.6 19.40826 -1.808260 1.884 -0.959805 -#> Dataset 6 parent 34 18.0 19.40826 -1.408260 1.884 -0.747489 -#> Dataset 6 parent 55 10.5 10.45560 0.044398 1.884 0.023566 -#> Dataset 6 parent 55 9.3 10.45560 -1.155602 1.884 -0.613381 -#> Dataset 6 parent 90 4.5 3.74026 0.759744 1.884 0.403264 -#> Dataset 6 parent 90 4.7 3.74026 0.959744 1.884 0.509421 -#> Dataset 6 parent 112 3.0 1.96015 1.039853 1.884 0.551943 -#> Dataset 6 parent 112 3.4 1.96015 1.439853 1.884 0.764258 -#> Dataset 6 parent 132 2.3 1.08940 1.210603 1.884 0.642575 -#> Dataset 6 parent 132 2.7 1.08940 1.610603 1.884 0.854890 -#> Dataset 6 A1 3 4.3 4.75601 -0.456009 1.884 -0.242045 -#> Dataset 6 A1 3 4.6 4.75601 -0.156009 1.884 -0.082808 -#> Dataset 6 A1 6 7.0 7.53839 -0.538391 1.884 -0.285772 -#> Dataset 6 A1 6 7.2 7.53839 -0.338391 1.884 -0.179614 -#> Dataset 6 A1 10 8.2 9.64728 -1.447276 1.884 -0.768198 -#> Dataset 6 A1 10 8.0 9.64728 -1.647276 1.884 -0.874356 -#> Dataset 6 A1 20 11.0 11.83954 -0.839545 1.884 -0.445621 -#> Dataset 6 A1 20 13.7 11.83954 1.860455 1.884 0.987509 -#> Dataset 6 A1 34 11.5 12.81233 -1.312327 1.884 -0.696569 -#> Dataset 6 A1 34 12.7 12.81233 -0.112327 1.884 -0.059622 -#> Dataset 6 A1 55 14.9 12.87919 2.020809 1.884 1.072624 -#> Dataset 6 A1 55 14.5 12.87919 1.620809 1.884 0.860308 -#> Dataset 6 A1 90 12.1 11.52464 0.575364 1.884 0.305397 -#> Dataset 6 A1 90 12.3 11.52464 0.775364 1.884 0.411555 -#> Dataset 6 A1 112 9.9 10.37694 -0.476938 1.884 -0.253153 -#> Dataset 6 A1 112 10.2 10.37694 -0.176938 1.884 -0.093917 -#> Dataset 6 A1 132 8.8 9.32474 -0.524742 1.884 -0.278528 -#> Dataset 6 A1 132 7.8 9.32474 -1.524742 1.884 -0.809317 -#> Dataset 7 parent 0 93.6 90.16918 3.430816 1.884 1.821040 -#> Dataset 7 parent 0 92.3 90.16918 2.130816 1.884 1.131014 -#> Dataset 7 parent 3 87.0 84.05442 2.945583 1.884 1.563483 -#> Dataset 7 parent 3 82.2 84.05442 -1.854417 1.884 -0.984304 -#> Dataset 7 parent 7 74.0 77.00960 -3.009596 1.884 -1.597461 -#> Dataset 7 parent 7 73.9 77.00960 -3.109596 1.884 -1.650540 -#> Dataset 7 parent 14 64.2 67.15684 -2.956840 1.884 -1.569459 -#> Dataset 7 parent 14 69.5 67.15684 2.343160 1.884 1.243724 -#> Dataset 7 parent 30 54.0 52.66290 1.337101 1.884 0.709719 -#> Dataset 7 parent 30 54.6 52.66290 1.937101 1.884 1.028192 -#> Dataset 7 parent 60 41.1 40.04995 1.050050 1.884 0.557355 -#> Dataset 7 parent 60 38.4 40.04995 -1.649950 1.884 -0.875775 -#> Dataset 7 parent 90 32.5 34.09675 -1.596746 1.884 -0.847535 -#> Dataset 7 parent 90 35.5 34.09675 1.403254 1.884 0.744832 -#> Dataset 7 parent 120 28.1 30.12281 -2.022814 1.884 -1.073688 -#> Dataset 7 parent 120 29.0 30.12281 -1.122814 1.884 -0.595977 -#> Dataset 7 parent 180 26.5 24.10888 2.391123 1.884 1.269182 -#> Dataset 7 parent 180 27.6 24.10888 3.491123 1.884 1.853050 -#> Dataset 7 A1 3 3.9 2.77684 1.123161 1.884 0.596161 -#> Dataset 7 A1 3 3.1 2.77684 0.323161 1.884 0.171530 -#> Dataset 7 A1 7 6.9 5.96705 0.932950 1.884 0.495200 -#> Dataset 7 A1 7 6.6 5.96705 0.632950 1.884 0.335963 -#> Dataset 7 A1 14 10.4 10.40535 -0.005348 1.884 -0.002839 -#> Dataset 7 A1 14 8.3 10.40535 -2.105348 1.884 -1.117496 -#> Dataset 7 A1 30 14.4 16.83722 -2.437216 1.884 -1.293648 -#> Dataset 7 A1 30 13.7 16.83722 -3.137216 1.884 -1.665200 -#> Dataset 7 A1 60 22.1 22.15018 -0.050179 1.884 -0.026635 -#> Dataset 7 A1 60 22.3 22.15018 0.149821 1.884 0.079523 -#> Dataset 7 A1 90 27.5 24.36286 3.137143 1.884 1.665161 -#> Dataset 7 A1 90 25.4 24.36286 1.037143 1.884 0.550504 -#> Dataset 7 A1 120 28.0 25.64064 2.359361 1.884 1.252323 -#> Dataset 7 A1 120 26.6 25.64064 0.959361 1.884 0.509218 -#> Dataset 7 A1 180 25.8 27.25486 -1.454858 1.884 -0.772223 -#> Dataset 7 A1 180 25.3 27.25486 -1.954858 1.884 -1.037617 -#> Dataset 8 parent 0 91.9 91.72652 0.173479 1.884 0.092081 -#> Dataset 8 parent 0 90.8 91.72652 -0.926521 1.884 -0.491787 -#> Dataset 8 parent 1 64.9 67.22810 -2.328104 1.884 -1.235732 -#> Dataset 8 parent 1 66.2 67.22810 -1.028104 1.884 -0.545706 -#> Dataset 8 parent 3 43.5 41.46375 2.036251 1.884 1.080820 -#> Dataset 8 parent 3 44.1 41.46375 2.636251 1.884 1.399293 -#> Dataset 8 parent 8 18.3 19.83597 -1.535968 1.884 -0.815275 -#> Dataset 8 parent 8 18.1 19.83597 -1.735968 1.884 -0.921433 -#> Dataset 8 parent 14 10.2 10.34793 -0.147927 1.884 -0.078518 -#> Dataset 8 parent 14 10.8 10.34793 0.452073 1.884 0.239956 -#> Dataset 8 parent 27 4.9 2.67641 2.223595 1.884 1.180260 -#> Dataset 8 parent 27 3.3 2.67641 0.623595 1.884 0.330997 -#> Dataset 8 parent 48 1.6 0.30218 1.297822 1.884 0.688870 -#> Dataset 8 parent 48 1.5 0.30218 1.197822 1.884 0.635791 -#> Dataset 8 parent 70 1.1 0.03075 1.069248 1.884 0.567545 -#> Dataset 8 parent 70 0.9 0.03075 0.869248 1.884 0.461388 -#> Dataset 8 A1 1 9.6 7.74066 1.859342 1.884 0.986918 -#> Dataset 8 A1 1 7.7 7.74066 -0.040658 1.884 -0.021581 -#> Dataset 8 A1 3 15.0 15.37549 -0.375495 1.884 -0.199309 -#> Dataset 8 A1 3 15.1 15.37549 -0.275495 1.884 -0.146230 -#> Dataset 8 A1 8 21.2 19.95900 1.241003 1.884 0.658711 -#> Dataset 8 A1 8 21.1 19.95900 1.141003 1.884 0.605632 -#> Dataset 8 A1 14 19.7 19.92898 -0.228978 1.884 -0.121539 -#> Dataset 8 A1 14 18.9 19.92898 -1.028978 1.884 -0.546170 -#> Dataset 8 A1 27 17.5 16.34046 1.159536 1.884 0.615469 -#> Dataset 8 A1 27 15.9 16.34046 -0.440464 1.884 -0.233793 -#> Dataset 8 A1 48 9.5 10.12131 -0.621313 1.884 -0.329786 -#> Dataset 8 A1 48 9.8 10.12131 -0.321313 1.884 -0.170550 -#> Dataset 8 A1 70 6.2 5.84753 0.352469 1.884 0.187087 -#> Dataset 8 A1 70 6.1 5.84753 0.252469 1.884 0.134008 -#> Dataset 9 parent 0 99.8 98.23600 1.564002 1.884 0.830155 -#> Dataset 9 parent 0 98.3 98.23600 0.064002 1.884 0.033972 -#> Dataset 9 parent 1 77.1 79.68007 -2.580074 1.884 -1.369475 -#> Dataset 9 parent 1 77.2 79.68007 -2.480074 1.884 -1.316396 -#> Dataset 9 parent 3 59.0 55.81142 3.188584 1.884 1.692465 -#> Dataset 9 parent 3 58.1 55.81142 2.288584 1.884 1.214755 -#> Dataset 9 parent 8 27.4 31.81995 -4.419948 1.884 -2.346060 -#> Dataset 9 parent 8 29.2 31.81995 -2.619948 1.884 -1.390640 -#> Dataset 9 parent 14 19.1 22.78328 -3.683282 1.884 -1.955046 -#> Dataset 9 parent 14 29.6 22.78328 6.816718 1.884 3.618240 -#> Dataset 9 parent 27 10.1 14.15172 -4.051720 1.884 -2.150609 -#> Dataset 9 parent 27 18.2 14.15172 4.048280 1.884 2.148783 -#> Dataset 9 parent 48 4.5 6.86094 -2.360941 1.884 -1.253162 -#> Dataset 9 parent 48 9.1 6.86094 2.239059 1.884 1.188468 -#> Dataset 9 parent 70 2.3 3.21580 -0.915798 1.884 -0.486096 -#> Dataset 9 parent 70 2.9 3.21580 -0.315798 1.884 -0.167622 -#> Dataset 9 parent 91 2.0 1.56010 0.439897 1.884 0.233492 -#> Dataset 9 parent 91 1.8 1.56010 0.239897 1.884 0.127335 -#> Dataset 9 parent 120 2.0 0.57458 1.425424 1.884 0.756600 -#> Dataset 9 parent 120 2.2 0.57458 1.625424 1.884 0.862757 -#> Dataset 9 A1 1 4.2 4.01796 0.182037 1.884 0.096623 -#> Dataset 9 A1 1 3.9 4.01796 -0.117963 1.884 -0.062613 -#> Dataset 9 A1 3 7.4 9.08527 -1.685270 1.884 -0.894523 -#> Dataset 9 A1 3 7.9 9.08527 -1.185270 1.884 -0.629129 -#> Dataset 9 A1 8 14.5 13.75054 0.749457 1.884 0.397804 -#> Dataset 9 A1 8 13.7 13.75054 -0.050543 1.884 -0.026827 -#> Dataset 9 A1 14 14.2 14.91180 -0.711804 1.884 -0.377818 -#> Dataset 9 A1 14 12.2 14.91180 -2.711804 1.884 -1.439396 -#> Dataset 9 A1 27 13.7 14.97813 -1.278129 1.884 -0.678417 -#> Dataset 9 A1 27 13.2 14.97813 -1.778129 1.884 -0.943812 -#> Dataset 9 A1 48 13.6 13.75574 -0.155745 1.884 -0.082668 -#> Dataset 9 A1 48 15.4 13.75574 1.644255 1.884 0.872753 -#> Dataset 9 A1 70 10.4 11.92861 -1.528608 1.884 -0.811369 -#> Dataset 9 A1 70 11.6 11.92861 -0.328608 1.884 -0.174422 -#> Dataset 9 A1 91 10.0 10.14395 -0.143947 1.884 -0.076405 -#> Dataset 9 A1 91 9.5 10.14395 -0.643947 1.884 -0.341800 -#> Dataset 9 A1 120 9.1 7.93869 1.161307 1.884 0.616409 -#> Dataset 9 A1 120 9.0 7.93869 1.061307 1.884 0.563330 -#> Dataset 10 parent 0 96.1 93.65914 2.440862 1.884 1.295583 -#> Dataset 10 parent 0 94.3 93.65914 0.640862 1.884 0.340163 -#> Dataset 10 parent 8 73.9 77.83065 -3.930647 1.884 -2.086344 -#> Dataset 10 parent 8 73.9 77.83065 -3.930647 1.884 -2.086344 -#> Dataset 10 parent 14 69.4 70.15862 -0.758619 1.884 -0.402667 -#> Dataset 10 parent 14 73.1 70.15862 2.941381 1.884 1.561253 -#> Dataset 10 parent 21 65.6 64.00840 1.591600 1.884 0.844804 -#> Dataset 10 parent 21 65.3 64.00840 1.291600 1.884 0.685567 -#> Dataset 10 parent 41 55.9 54.71192 1.188076 1.884 0.630618 -#> Dataset 10 parent 41 54.4 54.71192 -0.311924 1.884 -0.165566 -#> Dataset 10 parent 63 47.0 49.66775 -2.667747 1.884 -1.416011 -#> Dataset 10 parent 63 49.3 49.66775 -0.367747 1.884 -0.195196 -#> Dataset 10 parent 91 44.7 45.17119 -0.471186 1.884 -0.250101 -#> Dataset 10 parent 91 46.7 45.17119 1.528814 1.884 0.811478 -#> Dataset 10 parent 120 42.1 41.20430 0.895699 1.884 0.475427 -#> Dataset 10 parent 120 41.3 41.20430 0.095699 1.884 0.050796 -#> Dataset 10 A1 8 3.3 4.00920 -0.709204 1.884 -0.376438 -#> Dataset 10 A1 8 3.4 4.00920 -0.609204 1.884 -0.323359 -#> Dataset 10 A1 14 3.9 5.94267 -2.042668 1.884 -1.084226 -#> Dataset 10 A1 14 2.9 5.94267 -3.042668 1.884 -1.615015 -#> Dataset 10 A1 21 6.4 7.48222 -1.082219 1.884 -0.574430 -#> Dataset 10 A1 21 7.2 7.48222 -0.282219 1.884 -0.149799 -#> Dataset 10 A1 41 9.1 9.76246 -0.662460 1.884 -0.351626 -#> Dataset 10 A1 41 8.5 9.76246 -1.262460 1.884 -0.670100 -#> Dataset 10 A1 63 11.7 10.93972 0.760278 1.884 0.403547 -#> Dataset 10 A1 63 12.0 10.93972 1.060278 1.884 0.562784 -#> Dataset 10 A1 91 13.3 11.93666 1.363337 1.884 0.723645 -#> Dataset 10 A1 91 13.2 11.93666 1.263337 1.884 0.670566 -#> Dataset 10 A1 120 14.3 12.78218 1.517817 1.884 0.805641 -#> Dataset 10 A1 120 12.1 12.78218 -0.682183 1.884 -0.362095
+
#> Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'print': object 'f_saem_dfop_sfo' not found
plot(f_saem_dfop_sfo) +
#> Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'plot': object 'f_saem_dfop_sfo' not found
summary(f_saem_dfop_sfo, data = TRUE) +
#> Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'f_saem_dfop_sfo' not found
# 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)) -- cgit v1.2.1