diff options
Diffstat (limited to 'vignettes')
| -rw-r--r-- | vignettes/web_only/multistart.html | 22 | ||||
| -rw-r--r-- | vignettes/web_only/multistart.rmd | 13 |
2 files changed, 19 insertions, 16 deletions
diff --git a/vignettes/web_only/multistart.html b/vignettes/web_only/multistart.html index 88c64a28..c65bf376 100644 --- a/vignettes/web_only/multistart.html +++ b/vignettes/web_only/multistart.html @@ -403,18 +403,20 @@ runs:</p> <p><img 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" width="672" /></p> <p>We can use the <code>anova</code> method to compare the models.</p> <pre class="r"><code>anova(f_saem_full, best(f_saem_full_multi), - f_saem_reduced, best(f_saem_reduced_multi))</code></pre> + f_saem_reduced, best(f_saem_reduced_multi), test = TRUE)</code></pre> <pre><code>## Data: 155 observations of 1 variable(s) grouped in 6 datasets ## -## npar AIC BIC Lik -## f_saem_reduced 9 663.73 661.86 -322.86 -## best(f_saem_reduced_multi) 9 663.69 661.82 -322.85 -## f_saem_full 10 669.77 667.69 -324.89 -## best(f_saem_full_multi) 10 665.56 663.48 -322.78</code></pre> -<p>The reduced model gives the lowest information criteria and similar -likelihoods as the best variant of the full model. The multistart method -leads to a much lower improvement of the likelihood for the reduced -model, indicating that it is fitted more efficiently.</p> +## npar AIC BIC Lik Chisq Df Pr(>Chisq) +## f_saem_reduced 9 663.73 661.86 -322.86 +## best(f_saem_reduced_multi) 9 663.69 661.82 -322.85 0.0361 0 +## f_saem_full 10 669.77 667.69 -324.89 0.0000 1 1 +## best(f_saem_full_multi) 10 665.56 663.48 -322.78 4.2060 0</code></pre> +<p>The reduced model results in lower AIC and BIC values, so it is +clearly preferable. Using multiple starting values gives a large +improvement in case of the full model, because it is less well-defined, +which impedes convergence. For the reduced model, using multiple +starting values only results in a small improvement of the model +fit.</p> diff --git a/vignettes/web_only/multistart.rmd b/vignettes/web_only/multistart.rmd index 2a9b3599..34ed1ad4 100644 --- a/vignettes/web_only/multistart.rmd +++ b/vignettes/web_only/multistart.rmd @@ -67,11 +67,12 @@ We can use the `anova` method to compare the models. ```{r} anova(f_saem_full, best(f_saem_full_multi), - f_saem_reduced, best(f_saem_reduced_multi)) + f_saem_reduced, best(f_saem_reduced_multi), test = TRUE) ``` -The reduced model gives the lowest information criteria and similar -likelihoods as the best variant of the full model. The multistart method -leads to a much lower improvement of the likelihood for the reduced model, -indicating that it converges faster. - +The reduced model results in lower AIC and BIC values, so it +is clearly preferable. Using multiple starting values gives +a large improvement in case of the full model, because it is +less well-defined, which impedes convergence. For the reduced +model, using multiple starting values only results in a small +improvement of the model fit. |