Hi Alice,
This is a bug in gam.side, which was assuming that any interaction
smooth that needed identifiability constraints would be penalized.
thanks for reporting it - fixed for next version.
In the meantime have you considered using `ti' terms instead of `te'?
These provide a more satisfactory way of building
models with main effects and interactions. A ti smooth is basically
built as an interaction with the main effects completely removed, and
therefore does not require the imposition of further side conditions to
enforce identifiability (using `ti' would mean including the main
effect of x3 in your model).
best,
Simon
On 23/07/13 16:02, alice.jones wrote:
Hi,
I have been trying to fit an un-penalised gam in mgcv (in order to get more
reliable p-values for hypothesis testing), but I am struggling to get the
model to fit sucessfully when I add in a te() interaction. The model I am
trying to fit is:
gam(count~ s(x1, bs = "ts", k = 4, fx = TRUE) +
s(x2, bs = "ts", k = 4, fx = TRUE) +
te(x2, x3, bs = c("ts", "cc"), fx = TRUE) +
log(offset(y)),
knots = list(x3=c(0,360)), family = negbin(c(1,10)))
The error message I get is:
"Error in sm[[i]]$S[[j]] : attempt to select less than one element"
I can fit this model sucessfully if I don't specify the 'fx=TRUE' argument
(i.e. I can sucesfully fit the penalised model). It also works when I only
include the two main terms x1 and x2, but do specify fx = TRUE, and it works
fine when I only specify the main term x1 and the te smooth for x2 and x3
and specify fx = TRUE (i.e. without a spearate specification of the main
term, x2, that is also included in the interaction). But.... when I have
both main terms x1 and x2, as well as an interaction between x2 and x3,
without penalisation, I get the error.
I have played around with other data and with different covariate
specification, but it seems that any time I specify a main term that is also
included in the te interaction, within an un-penalised model, I get this
same error message.
Any help would be much appreciated, as I am trying to compare nested models
(i.e. the full model with the interaction term against the model that just
contains the two main terms). I understand that the most appropriate way to
do this is to use an un-penalised model for p-value estimation.
Thanks,
Alice
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