On 06/23/2010 06:30 AM, Samuel Okoye wrote:
Thank you ver much.
Is there is a function in R which is doing penalized cubic
regression, say spl.plr(), that if I have weeks = 1:9 I can use
somthing like pp<- spl.plr(weeks,c(1,3,5,7)) and for 8 and 9 will be
linear? Is rcs() library(Design) doing this?
rcs in Design (which you should replace with rms before long) does
unpenalized cubic splines that are linear in the tails. You can also
penalize such fits using the lrm and ols functions in rms/Design. Also
see the pspline function in the survival package, the splines package,
and others.
Frank
Many thanks, Samuel
--- On Tue, 22/6/10, Joris Meys<jorism...@gmail.com> wrote:
From: Joris Meys<jorism...@gmail.com> Subject: Re: [R] glm To:
"Samuel Okoye"<samu...@yahoo.com> Cc: r-help@r-project.org Date:
Tuesday, 22 June, 2010, 9:50
On Tue, Jun 22, 2010 at 1:00 AM, Samuel Okoye<samu...@yahoo.com>
wrote:
Hi,
I have the following data
data1<- data.frame(count = c(0,1,1,2,4,5,13,16,14), weeks = 1:9,
treat=c(rep("1mg",3),rep("5mg",3),rep("10mg",3))) and I am using
library(splines)
to fit
glm.m<-
glm(count~weeks)+as.factor(treat),family=poisson,data=data1)
and I am interested in predicting the count variale for the weeks
10, 11 and 12 with treat 10mg and 15mg.
bad luck for you.
newdat<-data.frame( weeks=rep(10:12,each=2),
treat=rep(c("5mg","10mg"),times=3) )
preds<- predict(glm.m,type="response",newdata=newdat,se.fit=T)
cbind(newdat,preds)
gives as expected : Warning message: In bs(weeks, degree = 3L, knots
= numeric(0), Boundary.knots = c(1L, : some 'x' values beyond
boundary knots may cause ill-conditioned bases
weeks treat fit se.fit residual.scale 1 10 5mg
5.934881 5.205426 1 2 10 10mg 12.041639 9.514347
1 3 11 5mg 4.345165 6.924663 1 4 11 10mg
8.816168 15.805171 1 5 12 5mg 2.781063 8.123436
1 6 12 10mg 5.642667 18.221007 1
Watch the standard errors on the predicted values. No, you shouldn't
predict outside your data space, especially when using splines. And
when interested in 15mg, well, you shouldn't put treatment as a
factor to start with.
Cheers Joris
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