The calculation of the optimal loss smoothness requires calculating the number of effective degrees of freedom. In .getErrParam we currently do
# Fixing dimension d to EDF of Gaussian fit.
# First use anova to find degrees of freedom of parametric terms in equation for location
# Then find EDF of smooth terms in equation for location.
# unique() needed for "adaptive" smooths
anv <- anova( gFit )
d <- sum( anv$pTerms.df[ !grepl("\\.1", rownames(anv$pTerms.table)) ] )
d <- d + sum( unique(pen.edf(gFit)[!grepl("s\\.1|te\\.1|ti\\.1|t2\\.1", names(pen.edf(gFit)))]) )
Using unique() avoids counting the edf of the same effect multiple times when using adaptive smooths and tensor product smooth, but might be problematic when using by-factor smooths, for example.
The calculation of the optimal loss smoothness requires calculating the number of effective degrees of freedom. In .getErrParam we currently do
Using
unique()avoids counting the edf of the same effect multiple times when using adaptive smooths and tensor product smooth, but might be problematic when using by-factor smooths, for example.