compareML  R Documentation 
Function for comparing two GAMM models.
compareML( model1, model2, signif.stars = TRUE, suggest.report = FALSE, print.output = TRUE )
model1 
First model. 
model2 
Second model. 
signif.stars 
Logical (default = TRUE). Whether or not to display stars indicating the level of significance on 95% confidence level. 
suggest.report 
Logical (default = FALSE). Whether or not to
present a suggestion on how one could report the information. If

print.output 
Logical: whether or not to print the output.
By default set to true, even if the the messages are not allowed by
a global package option using the function 
As an ChiSquare test is performed on two times the difference in minimized smoothing parameter selection score (GCV, fREML, REML, ML), and the difference in degrees of freedom specified in the model. The degrees of freedom of the model terms are the sum of 1) the number of estimated smoothing parameters for the model, 2) number of parametric (nonsmooth) model terms including the intercept, and 3) the sum of the penalty null space dimensions of each smooth object.
This method is preferred over other functions such as AIC
for
models that include an AR1 model or random effects (especially nonlinear
random smooths using bs='fs'
). CompareML also reports the AIC
difference, but that value should be treated with care.
Note that the ChiSquare test will result in a very low pvalue when the difference in degrees of freedom approaches zero. Use common sense to determine if the difference between the two models is meaningful. A warning is presented when the difference in score is smaller than 5.
The order of the two models is not important. Model comparison is only implemented for the methods GCV, fREML, REML, and ML.
Optionally returns the ChiSquare test table.
For suppressing the output and all warnings, set infoMessages to FALSE
(infoMessages('off')
), set the argument print.output
to FALSE,
and use the function
suppressWarnings
to suppress warning messages.
Jacolien van Rij. With many thanks to Simon N. Wood for his feedback.
For models without AR1 model or random effects AIC
can be used.
Other Testing for significance:
plot_diff2()
,
plot_diff()
,
report_stats()
,
wald_gam()
data(simdat) ## Not run: infoMessages('on') # some arbitrary models: m1 < bam(Y~Group + s(Time, by=Group), method='fREML', data=simdat) m2 < bam(Y~Group + s(Time), method='fREML', data=simdat) compareML(m1, m2) # exclude significance stars: compareML(m1, m2, signif.stars=FALSE) m3 < bam(Y~Group + s(Time, by=Group, k=25), method='fREML', data=simdat) compareML(m1, m3) # do not print output, but save table for later use: cml < compareML(m1, m2, print.output=FALSE)$table cml # Use suppressWarnings to also suppress warnings: suppressWarnings(cml < compareML(m1, m2, print.output=FALSE)$table) ## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.