Hi Tal,
That is correct. In the code, I actually call the same exact data.frame that
was used to create the object. I understand I can call predict() without a
data.frame to get this result, but I would like to predict other datasets as
well.
Thanks,
Harold
On Dec 5, 2010, at 7:18 AM, Tal Galili wrote:
> 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
>
> ----------------Contact
> Details:-------------------------------------------------------
> Contact me: tal.gal...@gmail.com | 972-52-7275845
> Read me: www.talgalili.com (Hebrew) | www.biostatistics.co.il (Hebrew) |
> www.r-statistics.com (English)
> ----------------------------------------------------------------------------------------------
>
>
>
>
> 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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