From d25974f643ee46b7cd5ccd8331dd7bb0b14ab27a Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Wed, 26 Oct 2022 09:36:44 +0200 Subject: Don't test parhist and llhist on travis, docs --- docs/dev/reference/saem.html | 133 +++++++++++++++++++++++++++++-------------- 1 file changed, 89 insertions(+), 44 deletions(-) (limited to 'docs/dev/reference/saem.html') diff --git a/docs/dev/reference/saem.html b/docs/dev/reference/saem.html index ce3d428c..c8a7504f 100644 --- a/docs/dev/reference/saem.html +++ b/docs/dev/reference/saem.html @@ -46,11 +46,14 @@ Expectation Maximisation algorithm (SAEM).">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models
  • - Example evaluation of FOCUS Example Dataset Z + Short demo of the multistart method
  • Performance benefit by using compiled model definitions in mkin
    • +
  • + Example evaluation of FOCUS Example Dataset Z +
  • Calculation of time weighted average concentrations with mkin
    • @@ -101,6 +104,10 @@ Expectation Maximisation algorithm (SAEM).

    test_log_parms = TRUE, conf.level = 0.6, solution_type = "auto", + covariance.model = "auto", + covariates = NULL, + covariate_models = NULL, + no_random_effect = NULL, nbiter.saemix = c(300, 100), control = list(displayProgress = FALSE, print = FALSE, nbiter.saemix = nbiter.saemix, save = FALSE, save.graphs = FALSE), @@ -118,13 +125,17 @@ Expectation Maximisation algorithm (SAEM).

    solution_type = "auto", transformations = c("mkin", "saemix"), degparms_start = numeric(), + covariance.model = "auto", + no_random_effect = NULL, + covariates = NULL, + covariate_models = NULL, test_log_parms = FALSE, conf.level = 0.6, verbose = FALSE, ... ) -saemix_data(object, verbose = FALSE, ...) +saemix_data(object, covariates = NULL, verbose = FALSE, ...) # S3 method for saem.mmkin parms(object, ci = FALSE, ...) @@ -171,6 +182,29 @@ for parameter that are tested if requested by 'test_log_parms'.

    automatic choice is not desired

    +
    covariance.model
    +

    Will be passed to saemix::SaemixModel(). Per +default, uncorrelated random effects are specified for all degradation +parameters.

    + + +
    covariates
    +

    A data frame with covariate data for use in +'covariate_models', with dataset names as row names.

    + + +
    covariate_models
    +

    A list containing linear model formulas with one explanatory +variable, i.e. of the type 'parameter ~ covariate'. Covariates must be available +in the 'covariates' data frame.

    + + +
    no_random_effect
    +

    Character vector of degradation parameters for +which there should be no variability over the groups. Only used +if the covariance model is not explicitly specified.

    + +
    nbiter.saemix

    Convenience option to increase the number of iterations

    @@ -249,40 +283,43 @@ using mmkin.

    f_saem_sfo <- saem(f_mmkin_parent["SFO", ]) f_saem_fomc <- saem(f_mmkin_parent["FOMC", ]) f_saem_dfop <- saem(f_mmkin_parent["DFOP", ]) -illparms(f_saem_dfop) -#> [1] "sd(g_qlogis)" -update(f_saem_dfop, covariance.model = diag(c(1, 1, 1, 0))) -#> 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 +anova(f_saem_sfo, f_saem_fomc, f_saem_dfop) +#> Data: 90 observations of 1 variable(s) grouped in 5 datasets #> -#> Data: -#> 90 observations of 1 variable(s) grouped in 5 datasets +#> npar AIC BIC Lik +#> f_saem_sfo 5 624.26 622.31 -307.13 +#> f_saem_fomc 7 467.87 465.13 -226.93 +#> f_saem_dfop 9 493.98 490.47 -237.99 +anova(f_saem_sfo, f_saem_dfop, test = TRUE) +#> Data: 90 observations of 1 variable(s) grouped in 5 datasets #> -#> Likelihood computed by importance sampling -#> AIC BIC logLik -#> 490.6 487.5 -237.3 +#> npar AIC BIC Lik Chisq Df Pr(>Chisq) +#> f_saem_sfo 5 624.26 622.31 -307.13 +#> f_saem_dfop 9 493.98 490.47 -237.99 138.28 4 < 2.2e-16 *** +#> --- +#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 +illparms(f_saem_dfop) +#> [1] "sd(g_qlogis)" +f_saem_dfop_red <- update(f_saem_dfop, no_random_effect = "g_qlogis") +anova(f_saem_dfop, f_saem_dfop_red, test = TRUE) +#> Data: 90 observations of 1 variable(s) grouped in 5 datasets #> -#> Fitted parameters: -#> estimate lower upper -#> parent_0 93.902 91.3695 96.4339 -#> log_k1 -2.936 -3.9950 -1.8762 -#> log_k2 -3.091 -4.9290 -1.2523 -#> g_qlogis -0.366 -0.6484 -0.0836 -#> a.1 2.385 2.0033 2.7664 -#> SD.parent_0 2.476 0.3890 4.5623 -#> SD.log_k1 1.195 0.4381 1.9517 -#> SD.log_k2 2.092 0.7906 3.3932 -AIC(f_saem_dfop) -#> [1] 493.9811 +#> npar AIC BIC Lik Chisq Df Pr(>Chisq) +#> f_saem_dfop_red 8 490.64 487.52 -237.32 +#> f_saem_dfop 9 493.98 490.47 -237.99 0 1 1 +anova(f_saem_sfo, f_saem_fomc, f_saem_dfop) +#> Data: 90 observations of 1 variable(s) grouped in 5 datasets +#> +#> npar AIC BIC Lik +#> f_saem_sfo 5 624.26 622.31 -307.13 +#> f_saem_fomc 7 467.87 465.13 -226.93 +#> f_saem_dfop 9 493.98 490.47 -237.99 # The returned saem.mmkin object contains an SaemixObject, therefore we can use # functions from saemix library(saemix) #> Loading required package: npde -#> Package saemix, version 3.1 +#> Package saemix, version 3.2 #> please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr #> #> Attaching package: ‘saemix’ @@ -308,11 +345,12 @@ using mmkin.

    f_mmkin_parent_tc <- update(f_mmkin_parent, error_model = "tc") f_saem_fomc_tc <- saem(f_mmkin_parent_tc["FOMC", ]) -compare.saemix(f_saem_fomc$so, f_saem_fomc_tc$so) -#> Likelihoods calculated by importance sampling -#> AIC BIC -#> 1 467.8664 465.1324 -#> 2 469.8018 466.6773 +anova(f_saem_fomc, f_saem_fomc_tc, test = TRUE) +#> Data: 90 observations of 1 variable(s) grouped in 5 datasets +#> +#> npar AIC BIC Lik Chisq Df Pr(>Chisq) +#> f_saem_fomc 7 467.87 465.13 -226.93 +#> f_saem_fomc_tc 8 469.80 466.68 -226.90 0.0645 1 0.7995 sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"), A1 = mkinsub("SFO")) @@ -370,11 +408,11 @@ using mmkin.

    plot(f_saem_dfop_sfo) summary(f_saem_dfop_sfo, data = TRUE) -#> saemix version used for fitting: 3.1 +#> saemix version used for fitting: 3.2 #> mkin version used for pre-fitting: 1.1.2 #> R version used for fitting: 4.2.1 -#> Date of fit: Fri Sep 16 10:30:47 2022 -#> Date of summary: Fri Sep 16 10:30:47 2022 +#> Date of fit: Wed Oct 26 09:20:37 2022 +#> Date of summary: Wed Oct 26 09:20:37 2022 #> #> Equations: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -389,7 +427,7 @@ using mmkin.

    #> #> Model predictions using solution type analytical #> -#> Fitted in 9.651 s +#> Fitted in 8.902 s #> Using 300, 100 iterations and 10 chains #> #> Variance model: Constant variance @@ -410,13 +448,20 @@ using mmkin.

    #> 842 836.9 -408 #> #> Optimised parameters: -#> est. lower upper -#> parent_0 93.7701 91.1458 96.3945 -#> log_k_A1 -5.8116 -7.5998 -4.0234 -#> f_parent_qlogis -0.9608 -1.3654 -0.5562 -#> log_k1 -2.5841 -3.6876 -1.4805 -#> log_k2 -3.5228 -5.3254 -1.7203 -#> g_qlogis -0.1027 -0.8719 0.6665 +#> est. lower upper +#> parent_0 93.7701 91.1458 96.3945 +#> log_k_A1 -5.8116 -7.5998 -4.0234 +#> f_parent_qlogis -0.9608 -1.3654 -0.5562 +#> log_k1 -2.5841 -3.6876 -1.4805 +#> log_k2 -3.5228 -5.3254 -1.7203 +#> g_qlogis -0.1027 -0.8719 0.6665 +#> a.1 1.8856 1.6676 2.1037 +#> SD.parent_0 2.7682 0.7668 4.7695 +#> SD.log_k_A1 1.7447 0.4047 3.0848 +#> SD.f_parent_qlogis 0.4525 0.1620 0.7431 +#> SD.log_k1 1.2423 0.4560 2.0285 +#> SD.log_k2 2.0390 0.7601 3.3180 +#> SD.g_qlogis 0.4439 -0.3069 1.1947 #> #> Correlation: #> parnt_0 lg_k_A1 f_prnt_ log_k1 log_k2 -- cgit v1.2.1