Hi Harold,
I think the error stems from the data.frame you are entering into the
predict function.
Your data.frame must have the EXACT column name as the one used for the
creation of the model object.
Is that the case in your code?
Best,
Tal
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On Sat, Dec 4, 2010 at 9:30 AM, Harold Pimentel <haroldpimen...@gmail.com>wrote:
> Hi all,
>
> I recently wrote some code to store a number of polynomial models in a list
> and return this list. The model is returned fine, but when I make a
> subsequent call to predict(), I have an error. The code for polyModel
> selection is listed at the end of the email. Here is an example of the
> error:
>
> > poly.fit <- polyModelSelection(x,y,10,F)
> > for (d in 1:4) {
> + lm.model <- poly.fit$models[[d]]
> + curve(predict(lm.model,data.frame(x)),add=T,lty=d+1)
> + }
> Error: variable 'poly(x, d, raw = T)' was fitted with type "nmatrix.1" but
> type "nmatrix.10" was supplied
>
> Does anyone have any ideas?
>
>
>
> Thanks,
>
> Harold
>
>
>
> ############################################################################
>
> polyModelSelection <- function(x,y,maxd,add.dim=F) {
> dim.mult <- 0
> if (add.dim) {
> dim.mult = 1
> }
> bestD <- 1
> bestError <- 1
> loss <- 1
> lm.models <- vector("list",maxd)
> for (d in 1:maxd) {
> lm.mod <- lm(y ~ 0 + poly(x,d,raw=T))
> lm.models[[d]] <- lm.mod
> loss[d] <- modelError(lm.mod,data.frame(x),y)+d*dim.mult
> if (d == 1){
> bestError <- loss[d]
> }
> if (loss[d] < bestError) {
> bestError <- loss[d]
> bestD <- d
> }
> # print(loss[d])
> }
> list(loss=loss,models=lm.models,best=c(bestD))
> }
>
> modelError <- function(model,x,y) {
> sum((y-predict(model,x))^2)
> }
> ______________________________________________
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>
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