I am having difficulty fitting a mgcv::gamm model that includes both a random
smooth term (i.e. 'fs' smooth) and autoregressive errors. Standard smooth
terms with a factor interaction using the 'by=' option work fine. Both on my
actual data and a toy example (below) I am getting the same error so am
inclined to wonder if this is either a bug or a model that gamm is simply
unable to fit?
Any insight or suggestions would be much appreciated.
M
Example:
library('mgcv')
set.seed(1)
df = data.frame(index=rep(1:10,5), x=runif(50,0,1), subject =
as.factor(sort(rep(1:5,10))))
# random intercept
m1 = gamm(x~s(index), random=list(subject=~1), data=df, method = 'REML')
#factor interaction, random intercept, AR errors
m2 = gamm(x~s(index, by=subject), random=list(subject=~1),
correlation=corAR1(form=~index|subject), data=df, method = 'REML')
#factor interaction, random intercept and slope, AR errors
m3 = gamm(x~s(index, by=subject), random=list(subject=~index),
correlation=corAR1(form=~index|subject), data=df, method = 'REML')
#random smooth on its own works ok
m4 = gamm(x~s(index, subject, bs='fs'), data=df, method = 'REML')
#combination of 'fs' smooth and AR term generates the error: "Error in
matrix(0, size.cg[i], size.cg[i]) : object 'size.cg' not found"
#the grouping term ( |subject ) is redundant in corAR1 as the mgcv
documentation indicates that gamm will assume the grouping
#from the random smooth term; the same error happens irrespective of whether
the grouping is included in corAR1 or not.
m5 = gamm(x~s(index, subject, bs='fs'),
correlation=corAR1(form=~index|subject), data=df, method = 'REML')______________________________________________
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