itsadug (version 2.0)

compareML: Function for comparing two GAMM models.

Description

Function for comparing two GAMM models.

Usage

compareML(model1, model2, signif.stars = TRUE,
  print.output = getOption("itsadug_print"))

Arguments

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.

print.output

Logical: whether or not to print the output. By default controlled globally in the package options: If the function infoMessages is set to TRUE, the output will be automatically printed. Could be a

Value

• Optionally returns the Chi-Square test table.

Notes

If no output is provided in the command window, set info messages to TRUE: infoMessages("on") and try again. For suppressing the output and all warnings, set infoMessages to FALSE (infoMessages("on") ) and use the function suppressWarnings to suppress warning messages.

Details

As an Chi-Square 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 (non-smooth) 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 Chi-Square test will result in a very low p-value 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.

See Also

For models without AR1 model or random effects AIC can be used.

Other Testing for significance: plot_diff2, plot_diff, report_stats, wald_gam

Examples

data(simdat)

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)$table
cml
# alternative way:
infoMessages('off')
cml <- compareML(m1, m2, print.output=FALSE)$table
infoMessages('on')

# Use suppressWarnings to also suppress warnings:
suppressWarnings(cml <- compareML(m1, m2, print.output=FALSE)$table)