From 606ef9ad6cae0ddfae6db6b90deb03f81140e46f Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Tue, 10 Nov 2020 05:14:57 +0100 Subject: Digits for summary methods, print.saem.mmkin --- docs/dev/reference/index.html | 2 +- docs/dev/reference/nlme.mmkin.html | 35 +++++++------- docs/dev/reference/saem.html | 95 +++++++++++++++++++++----------------- 3 files changed, 73 insertions(+), 59 deletions(-) (limited to 'docs/dev/reference') diff --git a/docs/dev/reference/index.html b/docs/dev/reference/index.html index ada9fb24..36c10225 100644 --- a/docs/dev/reference/index.html +++ b/docs/dev/reference/index.html @@ -325,7 +325,7 @@ of an mmkin object

-

saem() saemix_model() saemix_data()

+

saem() print(<saem.mmkin>) saemix_model() saemix_data()

Fit nonlinear mixed models with SAEM

diff --git a/docs/dev/reference/nlme.mmkin.html b/docs/dev/reference/nlme.mmkin.html index defef75d..05edbde5 100644 --- a/docs/dev/reference/nlme.mmkin.html +++ b/docs/dev/reference/nlme.mmkin.html @@ -170,7 +170,7 @@ have been obtained by fitting the same model to a list of datasets.

) # S3 method for nlme.mmkin -print(x, ...) +print(x, digits = max(3, getOption("digits") - 3), ...) # S3 method for nlme.mmkin update(object, ...) @@ -241,6 +241,10 @@ parameters taken from the mmkin object are used

x

An nlme.mmkin object to print

+ + digits +

Number of digits to use for printing

+ ...

Update specifications passed to update.nlme

@@ -262,8 +266,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as nlme::intervals(), nlme::anova.lme() and nlme::coef.lme().

See also

-

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

+

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

Examples

ds <- lapply(experimental_data_for_UBA_2019[6:10],
@@ -290,15 +293,15 @@ parms.nlme.mmkin

 #> 
 #> Fixed effects:
 #>  list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) 
-#>    parent_0      log_k1      log_k2    g_qlogis 
-#> 94.17015185 -1.80015278 -4.14738834  0.03239833 
+#> parent_0   log_k1   log_k2 g_qlogis 
+#>  94.1702  -1.8002  -4.1474   0.0324 
 #> 
 #> Random effects:
 #>  Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1)
 #>  Level: ds
 #>  Structure: Diagonal
-#>         parent_0    log_k1  log_k2  g_qlogis Residual
-#> StdDev: 2.488249 0.8447275 1.32965 0.4651789 2.321364
+#>         parent_0 log_k1 log_k2 g_qlogis Residual
+#> StdDev:    2.488 0.8447   1.33   0.4652    2.321
 #> 
plot(f_nlme_dfop)
 
endpoints(f_nlme_dfop)
 
#> $distimes
@@ -321,12 +324,12 @@ parms.nlme.mmkin

 #> Fixed effects:
 #>  list(parent_0 ~ 1, log_k_parent ~ 1) 
 #>     parent_0 log_k_parent 
-#>    75.933480    -3.555983 
+#>       75.933       -3.556 
 #> 
 #> Random effects:
 #>  Formula: parent_0 ~ 1 | ds
-#>            parent_0 Residual
-#> StdDev: 0.002416792 21.63027
+#>         parent_0 Residual
+#> StdDev: 0.002417    21.63
 #> 
  ds_2 <- lapply(experimental_data_for_UBA_2019[6:10],
    function(x) x$data[c("name", "time", "value")])
   m_sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"),
@@ -427,15 +430,15 @@ parms.nlme.mmkin

 #> 
 #> Fixed effects:
 #>  list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) 
-#>    parent_0      log_k1      log_k2    g_qlogis 
-#> 94.04774566 -1.82339808 -4.16715311  0.05685186 
+#> parent_0   log_k1   log_k2 g_qlogis 
+#> 94.04775 -1.82340 -4.16715  0.05685 
 #> 
 #> Random effects:
 #>  Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1)
 #>  Level: ds
 #>  Structure: Diagonal
-#>         parent_0    log_k1   log_k2  g_qlogis Residual
-#> StdDev: 2.473881 0.8499884 1.337185 0.4659005        1
+#>         parent_0 log_k1 log_k2 g_qlogis Residual
+#> StdDev:    2.474   0.85  1.337   0.4659        1
 #> 
 #> Variance function:
 #>  Structure: Constant plus proportion of variance covariate
@@ -462,14 +465,14 @@ parms.nlme.mmkin

 #> Fixed effects:
 #>  list(parent_0 ~ 1, log_k_parent_sink ~ 1, log_k_parent_A1 ~ 1,      log_k_A1_sink ~ 1) 
 #>          parent_0 log_k_parent_sink   log_k_parent_A1     log_k_A1_sink 
-#>         87.975536         -3.669816         -4.164127         -4.645073 
+#>            87.976            -3.670            -4.164            -4.645 
 #> 
 #> Random effects:
 #>  Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1, log_k_parent_A1 ~ 1,      log_k_A1_sink ~ 1)
 #>  Level: ds
 #>  Structure: Diagonal
 #>         parent_0 log_k_parent_sink log_k_parent_A1 log_k_A1_sink Residual
-#> StdDev: 3.992214           1.77702        1.054733     0.4821383 6.482585
+#> StdDev:    3.992             1.777           1.055        0.4821    6.483
 #> 
 #> Variance function:
 #>  Structure: Different standard deviations per stratum
diff --git a/docs/dev/reference/saem.html b/docs/dev/reference/saem.html
index f1b4c421..f9cdf1c8 100644
--- a/docs/dev/reference/saem.html
+++ b/docs/dev/reference/saem.html
@@ -164,6 +164,9 @@ Expectation Maximisation algorithm (SAEM).

   ...
 )
 
+# S3 method for saem.mmkin
+print(x, digits = max(3, getOption("digits") - 3), ...)
+
 saemix_model(object, cores = 1, verbose = FALSE, ...)
 
 saemix_data(object, verbose = FALSE, ...)
@@ -201,6 +204,14 @@ used.

Should we suppress any plotting that is done by the saemix function?

+ + x +

An saem.mmkin object to print

+ + + digits +

Number of digits to use for printing

+

Value

@@ -230,27 +241,27 @@ 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 Nov 9 17:18:28 2020" +#> [1] "Tue Nov 10 05:12:21 2020" #> .... #> Minimisation finished -#> [1] "Mon Nov 9 17:18:30 2020"
+#> [1] "Tue Nov 10 05:12:23 2020"
f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE) f_saem_sfo <- saem(f_mmkin_parent["SFO", ])
#> Running main SAEM algorithm -#> [1] "Mon Nov 9 17:18:31 2020" +#> [1] "Tue Nov 10 05:12:24 2020" #> .... #> Minimisation finished -#> [1] "Mon Nov 9 17:18:33 2020"
f_saem_fomc <- saem(f_mmkin_parent["FOMC", ]) +#> [1] "Tue Nov 10 05:12:26 2020"
f_saem_fomc <- saem(f_mmkin_parent["FOMC", ])
#> Running main SAEM algorithm -#> [1] "Mon Nov 9 17:18:33 2020" +#> [1] "Tue Nov 10 05:12:26 2020" #> .... #> Minimisation finished -#> [1] "Mon Nov 9 17:18:35 2020"
f_saem_dfop <- saem(f_mmkin_parent["DFOP", ]) +#> [1] "Tue Nov 10 05:12:28 2020"
f_saem_dfop <- saem(f_mmkin_parent["DFOP", ])
#> Running main SAEM algorithm -#> [1] "Mon Nov 9 17:18:36 2020" +#> [1] "Tue Nov 10 05:12:29 2020" #> .... #> Minimisation finished -#> [1] "Mon Nov 9 17:18:39 2020"
+#> [1] "Tue Nov 10 05:12:32 2020"
# The returned saem.mmkin object contains an SaemixObject, therefore we can use # functions from saemix library(saemix) @@ -296,10 +307,10 @@ using mmkin.

f_mmkin_parent_tc <- update(f_mmkin_parent, error_model = "tc") f_saem_fomc_tc <- saem(f_mmkin_parent_tc["FOMC", ])
#> Running main SAEM algorithm -#> [1] "Mon Nov 9 17:18:41 2020" +#> [1] "Tue Nov 10 05:12:34 2020" #> .... #> Minimisation finished -#> [1] "Mon Nov 9 17:18:46 2020"
compare.saemix(list(f_saem_fomc$so, f_saem_fomc_tc$so)) +#> [1] "Tue Nov 10 05:12:39 2020"
compare.saemix(list(f_saem_fomc$so, f_saem_fomc_tc$so))
#> Likelihoods computed by importance sampling
#> AIC BIC #> 1 467.7644 465.0305 #> 2 469.4862 466.3617
@@ -319,20 +330,20 @@ using mmkin.

# solutions written for mkin this took around four minutes f_saem_sfo_sfo <- saem(f_mmkin["SFO-SFO", ])
#> Running main SAEM algorithm -#> [1] "Mon Nov 9 17:18:48 2020" +#> [1] "Tue Nov 10 05:12:42 2020" #> .... #> Minimisation finished -#> [1] "Mon Nov 9 17:18:53 2020"
f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ]) +#> [1] "Tue Nov 10 05:12:47 2020"
f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ])
#> Running main SAEM algorithm -#> [1] "Mon Nov 9 17:18:54 2020" +#> [1] "Tue Nov 10 05:12:48 2020" #> .... #> Minimisation finished -#> [1] "Mon Nov 9 17:19:03 2020"
summary(f_saem_dfop_sfo, data = FALSE) +#> [1] "Tue Nov 10 05:12:57 2020"
summary(f_saem_dfop_sfo, data = FALSE)
#> saemix version used for fitting: 3.1.9000 #> mkin version used for pre-fitting: 0.9.50.4 #> R version used for fitting: 4.0.3 -#> Date of fit: Mon Nov 9 17:19:04 2020 -#> Date of summary: Mon Nov 9 17:19:04 2020 +#> Date of fit: Tue Nov 10 05:12:58 2020 +#> Date of summary: Tue Nov 10 05:12:58 2020 #> #> Equations: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -347,7 +358,7 @@ using mmkin.

#> #> Model predictions using solution type analytical #> -#> Fitted in 9.941 s using 300, 100 iterations +#> Fitted in 10.382 s using 300, 100 iterations #> #> Variance model: Constant variance #> @@ -363,17 +374,17 @@ using mmkin.

#> Results: #> #> Likelihood computed by importance sampling -#> AIC BIC logLik -#> 841.3208 836.2435 -407.6604 +#> AIC BIC logLik +#> 841.3 836.2 -407.7 #> #> Optimised, transformed parameters with symmetric confidence intervals: -#> est. lower upper -#> parent_0 93.7514328489 91.113651 96.3892150 -#> log_k_A1 -6.1262333211 -8.432492 -3.8199749 -#> f_parent_qlogis -0.9739851652 -1.371984 -0.5759863 -#> log_k1 -2.4818388836 -3.746899 -1.2167788 -#> log_k2 -3.6138616567 -5.294149 -1.9335743 -#> g_qlogis -0.0004613666 -1.063179 1.0622564 +#> est. lower upper +#> parent_0 93.7514328 91.114 96.389 +#> log_k_A1 -6.1262333 -8.432 -3.820 +#> f_parent_qlogis -0.9739852 -1.372 -0.576 +#> log_k1 -2.4818389 -3.747 -1.217 +#> log_k2 -3.6138617 -5.294 -1.934 +#> g_qlogis -0.0004614 -1.063 1.062 #> #> Correlation: #> prnt_0 lg__A1 f_prn_ log_k1 log_k2 @@ -384,26 +395,26 @@ using mmkin.

#> g_qlogis -0.068 -0.016 0.011 -0.181 -0.181 #> #> Random effects: -#> est. lower upper -#> SD.parent_0 2.7857084 0.7825105 4.7889063 -#> SD.log_k_A1 2.1412505 0.4425207 3.8399803 -#> SD.f_parent_qlogis 0.4463087 0.1609059 0.7317116 -#> SD.log_k1 1.4097204 0.5240566 2.2953842 -#> SD.log_k2 1.8739067 0.6979362 3.0498773 -#> SD.g_qlogis 0.4559301 -0.8149852 1.7268453 +#> est. lower upper +#> SD.parent_0 2.7857 0.7825 4.7889 +#> SD.log_k_A1 2.1413 0.4425 3.8400 +#> SD.f_parent_qlogis 0.4463 0.1609 0.7317 +#> SD.log_k1 1.4097 0.5241 2.2954 +#> SD.log_k2 1.8739 0.6979 3.0499 +#> SD.g_qlogis 0.4559 -0.8150 1.7268 #> #> Variance model: -#> est. lower upper -#> a.1 1.882757 1.665681 2.099832 +#> est. lower upper +#> a.1 1.883 1.666 2.1 #> #> Backtransformed parameters with asymmetric confidence intervals: -#> est. lower upper -#> parent_0 93.751432849 9.111365e+01 96.38921497 -#> k_A1 0.002184795 2.176784e-04 0.02192835 -#> f_parent_to_A1 0.274086887 2.022995e-01 0.35985666 -#> k1 0.083589373 2.359079e-02 0.29618269 -#> k2 0.026947583 5.020885e-03 0.14463032 -#> g 0.499884658 2.567024e-01 0.74312150 +#> est. lower upper +#> parent_0 93.751433 9.111e+01 96.38921 +#> k_A1 0.002185 2.177e-04 0.02193 +#> f_parent_to_A1 0.274087 2.023e-01 0.35986 +#> k1 0.083589 2.359e-02 0.29618 +#> k2 0.026948 5.021e-03 0.14463 +#> g 0.499885 2.567e-01 0.74312 #> #> Resulting formation fractions: #> ff -- cgit v1.2.1