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/saem.html | 95 ++++++++++++++++++++++++-------------------- 1 file changed, 53 insertions(+), 42 deletions(-) (limited to 'docs/dev/reference/saem.html') 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