Sorry, this relates to ?"mgcv-FAQ" number 5, unfortunately. 'ti' terms
were introduced as a much better and cleaner way of allowing smooth main
effects and interactions where the interactions are based on 'te' terms:
this required some re-engineering of the tensor product smooth objects
(the offending 'iter' element of the smooth is how 'ti' terms are
distinguished from normal 'te' terms internally). I'm afraid this won't
work in the current mgcv without re-fitting the original model.
Simon
On 26/10/13 10:33, Andrew Crane-Droesch wrote:
Dear List,
I am trying to reproduce a figure that I made for an analysis that I did
a few months ago. Between when I first made the figure and now, I've
upgraded to R 3.0.2 and upgraded my operating system (ubuntu 13.04). My
codebase, which once works, is throwing an error when I try to use
predict.gam on a model object that I saved:
1> prediction = predict(MI,se.fit=TRUE,newdata=rhc)
Error in if (object$inter) X[[i]] <- PredictMat(object$margin[[i]], dat, :
argument is of length zero
traceback() gives me the following:
1> traceback()
6: Predict.matrix.tensor.smooth(object, dk$data)
5: Predict.matrix(object, dk$data)
4: Predict.matrix3(object, data)
3: PredictMat(object$smooth[[k]], data)
2: predict.gam(MI, se.fit = TRUE, newdata = rhc)
1: predict(MI, se.fit = TRUE, newdata = rhc)
the final function being called looks like this:
1> Predict.matrix.tensor.smooth
function (object, data)
{
m <- length(object$margin)
X <- list()
for (i in 1:m) {
term <- object$margin[[i]]$term
dat <- list()
for (j in 1:length(term)) dat[[term[j]]] <- data[[term[j]]]
if (object$inter)
X[[i]] <- PredictMat(object$margin[[i]], dat, n =
length(dat[[1]]))
else X[[i]] <- Predict.matrix(object$margin[[i]], dat)
}
mxp <- length(object$XP)
if (mxp > 0)
for (i in 1:mxp) if (!is.null(object$XP[[i]]))
X[[i]] <- X[[i]] %*% object$XP[[i]]
T <- tensor.prod.model.matrix(X)
T
}
Unfortunately, I can't say that I understand how that function is
working, beyond that it takes a fitted model object and makes a piece of
the model matrix.
Any ideas about what this could stem from? Where to start looking to
fix it? I could probably do the entire analysis from scratch, but it is
quite complex and I'd prefer to save the time.
Apologies for non-reproducible code -- the data is big and the script is
long.
Thanks for any assistance,
Andrew
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--
Simon Wood, Mathematical Science, University of Bath BA2 7AY UK
+44 (0)1225 386603 http://people.bath.ac.uk/sw283
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
