From 59566e315c5d05a0aa4a46ab80c5fda33c5c2f1e Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Sat, 29 Oct 2022 00:36:22 +0200 Subject: Update multistart docs --- docs/dev/articles/web_only/multistart.html | 10 +++++----- .../figure-html/unnamed-chunk-3-1.png | Bin 59606 -> 59586 bytes .../figure-html/unnamed-chunk-4-1.png | Bin 55167 -> 55154 bytes .../figure-html/unnamed-chunk-5-1.png | Bin 21474 -> 21455 bytes .../figure-html/unnamed-chunk-6-1.png | Bin 54481 -> 54302 bytes 5 files changed, 5 insertions(+), 5 deletions(-) (limited to 'docs/dev/articles/web_only') diff --git a/docs/dev/articles/web_only/multistart.html b/docs/dev/articles/web_only/multistart.html index 275fc520..0c08905a 100644 --- a/docs/dev/articles/web_only/multistart.html +++ b/docs/dev/articles/web_only/multistart.html @@ -109,7 +109,7 @@

Short demo of the multistart method

Johannes Ranke

-

Last change 26 September 2022 (rebuilt 2022-10-26)

+

Last change 26 September 2022 (rebuilt 2022-10-28)

Source: vignettes/web_only/multistart.rmd @@ -139,7 +139,7 @@

We see that not all variability parameters are identifiable. The illparms function tells us that the confidence interval for the standard deviation of ‘log_k2’ includes zero. We check this assessment using multiple runs with different starting values.

 f_saem_full_multi <- multistart(f_saem_full, n = 16, cores = 16)
-parhist(f_saem_full_multi)
+parplot(f_saem_full_multi)

This confirms that the variance of k2 is the most problematic parameter, so we reduce the parameter distribution model by removing the intersoil variability for k2.

@@ -148,7 +148,7 @@
 
## character(0)

 
 f_saem_reduced_multi <- multistart(f_saem_reduced, n = 16, cores = 16)
-parhist(f_saem_reduced_multi, lpos = "topright")

+parplot(f_saem_reduced_multi, lpos = "topright")

The results confirm that all remaining parameters can be determined with sufficient certainty.

We can also analyse the log-likelihoods obtained in the multiple runs:

@@ -157,7 +157,7 @@

The parameter histograms can be further improved by excluding the result with the low likelihood.

-parhist(f_saem_reduced_multi, lpos = "topright", llmin = -326, ylim = c(0.5, 2))
+parplot(f_saem_reduced_multi, lpos = "topright", llmin = -326, ylim = c(0.5, 2))

We can use the anova method to compare the models, including a likelihood ratio test if the models are nested.

@@ -165,7 +165,7 @@
 
## Data: 155 observations of 1 variable(s) grouped in 6 datasets
 ## 
 ##                            npar    AIC    BIC     Lik Chisq Df Pr(>Chisq)
-## best(f_saem_reduced_multi)    9 663.81 661.93 -322.90                    
+## best(f_saem_reduced_multi)    9 663.64 661.77 -322.82                    
 ## f_saem_full                  10 668.27 666.19 -324.13     0  1          1

 
While AIC and BIC are lower for the reduced model, the likelihood ratio test does not indicate a significant difference between the fits.
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