Re: [R] Problem storing lm() model in a list
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.comwrote:
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$models[[d]]
+ curve(predict(lm.model,data.frame(x)),add=T,lty=d+1)
+ }
Error: variable 'poly(x, d, raw = T)' was fitted with type nmatrix.1 but
type nmatrix.10 was supplied
Does anyone have any ideas?
Thanks,
Harold
polyModelSelection - function(x,y,maxd,add.dim=F) {
dim.mult - 0
if (add.dim) {
dim.mult = 1
}
bestD - 1
bestError - 1
loss - 1
lm.models - vector(list,maxd)
for (d in 1:maxd) {
lm.mod - lm(y ~ 0 + poly(x,d,raw=T))
lm.models[[d]] - lm.mod
loss[d] - modelError(lm.mod,data.frame(x),y)+d*dim.mult
if (d == 1){
bestError - loss[d]
}
if (loss[d] bestError) {
bestError - loss[d]
bestD - d
}
# print(loss[d])
}
list(loss=loss,models=lm.models,best=c(bestD))
}
modelError - function(model,x,y) {
sum((y-predict(model,x))^2)
}
__
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Re: [R] Problem storing lm() model in a list
Your problem arises because predict() is using
the value d=10 whenever it evaluates poly(x,d,raw=TRUE).
The details are that this is the value that d in your
function polyModelSelection had when the function finished.
That value is stored in the environment
environment(poly.fit$models[[i]]$terms)
for i in 1:10 (which is the same environment for all
10 entries). It contains the following objects
objects(envir=environment(poly.fit$models[[1]]$terms))
[1] add.dim bestD bestError d
[5] dim.mult formula lm.modlm.models
[9] loss maxd x y
and predict will look there for objects named in
the formula but not found in its newdata= argument.
Use get(d, envir=...) to see that d is 10 in all
of them.
I don't know the best way to avoid the problem.
One way is to put a literal value for degree into your
call to poly(), which also has the advantage that your
printouts show what it is. In the following I used
substitute() to put the explicit value of degree into
your formula and used do.call when calling lm so the
formula itself (not the name formula) is put into
the call component of the lm object. This seems kludgy
to me, but I think it gets the job done.
polyModelSelection - function (x, y, maxd, add.dim = F)
{
dim.mult - 0
if (add.dim) {
dim.mult = 1
}
bestD - 1
bestError - 1
loss - 1
lm.models - vector(list, maxd)
for (d in 1:maxd) {
# next assignment added
formula - substitute(y ~ 0 + poly(x, degree = d, raw = TRUE),
list(d = d))
# change lm(...) to do.call(lm, list(...))
lm.mod - do.call(lm, list(formula))
lm.models[[d]] - lm.mod
loss[d] - modelError(lm.mod, data.frame(x), y) + d *
dim.mult
if (d == 1) {
bestError - loss[d]
}
if (loss[d] bestError) {
bestError - loss[d]
bestD - d
}
}
list(loss = loss, models = lm.models, best = c(bestD))
}
Another way around the problem might be to replace
the environment stored in those terms compononents
with emptyenv(). Then none of the variables in the
function polyModelSelection could be used. You could
do that with
for(d in seq_along(poly.fit$models)) {
environment(poly.fit$models[[1]]$terms) - emptyenv()
}
I like the first was better because I can easily see what
d is.
This sort of issue arises whenever we use functions
that use nonstandard evaluation rules, like the modelling
functions, subset(), library(), and help(). They are
easy to call from the top level command line but harder
to deal with when called with variables for arguments.
Bill Dunlap
Spotfire, TIBCO Software
wdunlap tibco.com
-Original Message-
From: r-help-boun...@r-project.org
[mailto:r-help-boun...@r-project.org] On Behalf Of Harold Pimentel
Sent: Friday, December 03, 2010 11:31 PM
To: r-help@r-project.org
Subject: [R] Problem storing lm() model in a list
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)
}
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] Problem storing lm() model in a list
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)
}
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
[[alternative HTML version deleted]]
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
[R] Problem storing lm() model in a list
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)
}
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
