It is nice to see worked examples, but there were some errors that
relate to not including dataframe references. I've paste in code that
runs without error on my machine:
On Sep 16, 2009, at 12:39 PM, Staffan Roos wrote:
Thanks Simon,
I wasn't aware that "predict.gam" could be used in this situation. I
followed you advice and managed to extract the data I needed.
For people interested, I add the code with comments I used here:
#############################################
# Full code for extracting partial predictions
# Start the package mgcv
library(mgcv)
# Simulate some data (this is from Simon Wood's example in the
# package "mgcv" manual)
n<-200
sig <- 2
dat <- gamSim(1,n=n,scale=sig)
# Check the data
dat
## It looks alright :-)
# Run the GAM
b<-gam(y~s(x0)+s(I(x1^2))+s(x2)+offset(x3),data=dat)
# Plot the partial predictions (You may need to rescale your window to
# see the non-linearity
par(mfrow=c(1,3))
plot(b, scale=0)
### Clearly, the variables x0 and x2 are non-linear!
# I would like to extract the "solid line" in the graphs and the
# associated standard errors from the plots. Thus, I use "predict"
# and change to a data.frame:
c<-data.frame(predict(b,type="terms",se.fit=TRUE)$fit)
names(c)<-c("pp_s.x0.","pp_s.I.x1.2..","pp_s.x2.")
d<-data.frame(predict(b,type="terms",se.fit=TRUE)$se.fit)
names(d)<-c("se_s.x0.","se_s.I.x1.2..","se_s.x2.")
# Merge the three datasets:
f=cbind(dat,c,d)
#Restrict the number of variables to the ones I am interested in:
f<-f[,c("x0","pp_s.x0.", "se_s.x0.")]
names(f)
### This data, i.e. "f", can now be exported or used for further
### calculations within R
#plot the data
par(mfrow=c(1,1))
plot(pp_s.x0.~x0,type="p",pch=2,cex=0.50, data=f)
sequence=order(f$x0)
lines(f$x0[sequence],f$pp_s.x0.[sequence])
lines
rug(jitter(f$x0))
##########################################
Staffan,
Take a look at ?predict.gam. You need to use type="terms" with
se=TRUE to
get
the quantities that you need.
Simon
ps. with binary data, method="REML" or method="ML" for the gam fit
are
often
somewhat more reliable than the default.
On Monday 14 September 2009 19:30, Staffan Roos wrote:
Dear package mgcv users,
I am using package mgcv to describe presence of a migratory bird
species
as
a function of several variables, including year, day number (i.e.
day-of-the-year), duration of survey, latitude and longitude. Thus,
the
"global model" is:
global_model<-gam(present ~ as.factor(year) + s(dayno, k=5) +
s(duration,
k=5) + s(x, k=5) + s(y, k=5), family = binomial(link = logit), data =
presence, gamma =1.4)
The model works fine, suggesting that all the variables have strong,
non-linear, influences on the probability of detecting the species.
My
main
interest is in the effect "dayno" has on presence, given the
inclusion of
the other explanatory variables. Thus, I would like to extract the
values
of the partial prediction of "dayno" and its associated 2 standard
errors
above and below the main effect, as shown by the code
"plot(global_model)".
I have tried to extract the values by using
"fitted.values(global_model,dayno)", but when plotted, the figure
doesn't
look like the partial prediction for "dayno". Instead, it gives me
a very
scattered figure ("shotgun effect").
Has anyone tried to extract the partial predictions? If so, please
could
you advise me how to do this?
Staffan
P.S.. For comparison, please have a look at Simon Woods paper in R
News,
1(2):20-25, June 2001, especially the figures showing the partial
predictions of Mackerel egg densities. Using those graphs as an
example, I
would like to extract the partial predictions for e.g.
"s(b.depth)", given
the inclusion of the other variables.
David Winsemius, MD
Heritage Laboratories
West Hartford, CT
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