standardized |
Should the residuals be standardized? This option
diff --git a/docs/dev/reference/saem.html b/docs/dev/reference/saem.html
index a832e911..59589378 100644
--- a/docs/dev/reference/saem.html
+++ b/docs/dev/reference/saem.html
@@ -162,7 +162,6 @@ Expectation Maximisation algorithm (SAEM).
control = list(displayProgress = FALSE, print = FALSE, save = FALSE, save.graphs =
FALSE),
verbose = FALSE,
- suppressPlot = TRUE,
quiet = FALSE,
...
)
@@ -219,11 +218,6 @@ automatic choice is not desired |
verbose |
Should we print information about created objects of
type saemix::SaemixModel and saemix::SaemixData? |
-
-
quiet |
@@ -267,36 +261,33 @@ 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 11 12:42:01 2021"
+#> [1] "Mon Jan 25 14:41:42 2021"
#> ....
#> Minimisation finished
-#> [1] "Mon Jan 11 12:42:03 2021"
+#> [1] "Mon Jan 25 14:41:43 2021"
#> Running main SAEM algorithm
-#> [1] "Mon Jan 11 12:42:04 2021"
+#> [1] "Mon Jan 25 14:41:45 2021"
#> ....
#> Minimisation finished
-#> [1] "Mon Jan 11 12:42:06 2021"
f_saem_fomc <- saem(f_mmkin_parent["FOMC", ])
+#> [1] "Mon Jan 25 14:41:46 2021"
f_saem_fomc <- saem(f_mmkin_parent["FOMC", ])
#> Running main SAEM algorithm
-#> [1] "Mon Jan 11 12:42:06 2021"
+#> [1] "Mon Jan 25 14:41:47 2021"
#> ....
#> Minimisation finished
-#> [1] "Mon Jan 11 12:42:08 2021"
f_saem_dfop <- saem(f_mmkin_parent["DFOP", ])
+#> [1] "Mon Jan 25 14:41:49 2021"
f_saem_dfop <- saem(f_mmkin_parent["DFOP", ])
#> Running main SAEM algorithm
-#> [1] "Mon Jan 11 12:42:09 2021"
+#> [1] "Mon Jan 25 14:41:49 2021"
#> ....
#> Minimisation finished
-#> [1] "Mon Jan 11 12:42:12 2021"
+#> [1] "Mon Jan 25 14:41:52 2021"
#> Package saemix, version 3.1.9000
#> please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr
#> Likelihoods computed by importance sampling
#> AIC BIC
-#> 1 624.2484 622.2956
-#> 2 467.7096 464.9757
-#> 3 495.4373 491.9222
#> Error in compare.saemix(list(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so)): 'compare.saemix' requires at least two models.
#> Plotting convergence plots
#> Plotting individual fits
#> Simulating data using nsim = 1000 simulated datasets
@@ -333,13 +324,11 @@ using
mmkin.
f_mmkin_parent_tc <- update(f_mmkin_parent, error_model
= "tc")
f_saem_fomc_tc <- saem(f_mmkin_parent_tc["FOMC",
])
#> Running main SAEM algorithm
-#> [1] "Mon Jan 11 12:42:14 2021"
+#> [1] "Mon Jan 25 14:41:55 2021"
#> ....
#> Minimisation finished
-#> [1] "Mon Jan 11 12:42:19 2021"
#> Likelihoods computed by importance sampling
#> AIC BIC
-#> 1 467.7096 464.9757
-#> 2 469.5208 466.3963
+#> [1] "Mon Jan 25 14:42:00 2021"
#> Error in compare.saemix(list(f_saem_fomc$so, f_saem_fomc_tc$so)): 'compare.saemix' requires at least two models.
#> Temporary DLL for differentials generated and loaded
#> Running main SAEM algorithm
-#> [1] "Mon Jan 11 12:42:22 2021"
+#> [1] "Mon Jan 25 14:42:02 2021"
#> ....
#> Minimisation finished
-#> [1] "Mon Jan 11 12:42:27 2021"
f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ])
+#> [1] "Mon Jan 25 14:42:07 2021"
f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ])
#> Running main SAEM algorithm
-#> [1] "Mon Jan 11 12:42:28 2021"
+#> [1] "Mon Jan 25 14:42:08 2021"
#> ....
#> Minimisation finished
-#> [1] "Mon Jan 11 12:42:37 2021"
# We can use print, plot and summary methods to check the results
+#> [1] "Mon Jan 25 14:42:17 2021"
#> Kinetic nonlinear mixed-effects model fit by SAEM
#> Structural model:
@@ -408,8 +397,8 @@ using
mmkin.
#> saemix version used for fitting: 3.1.9000
#> mkin version used for pre-fitting: 0.9.50.4
#> R version used for fitting: 4.0.3
-#> Date of fit: Mon Jan 11 12:42:38 2021
-#> Date of summary: Mon Jan 11 12:42:38 2021
+#> Date of fit: Mon Jan 25 14:42:18 2021
+#> Date of summary: Mon Jan 25 14:42:18 2021
#>
#> Equations:
#> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -424,7 +413,7 @@ using
mmkin.
#>
#> Model predictions using solution type analytical
#>
-#> Fitted in 10.092 s using 300, 100 iterations
+#> Fitted in 9.954 s using 300, 100 iterations
#>
#> Variance model: Constant variance
#>
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