Dear R-help list,
Im using the mgcv package to plot predictions based on the gam function.
I predict the chance of being a (frequent) participant at theater plays vs.
not being a participant by age.
Because my outcome variable is dichotomous, I use the binomial family with
logit link function.
Dataset in attachment, code to read it in R:
data <- read.spss("pas_r.sav")
attach(data)
In a first step I use gam to model my data and predict to calculate and
plot the predicted values, this all works fine.
My code looks like this:
test <- gam(participant ~ s(age,fx=FALSE,bs='cr'), family=binomial(logit))
summary(test)
plot(test, shade=TRUE)
gam.check(test)
test.pred <- predict(test,newdata=data,se.fit=TRUE,type='response',
na.action=na.omit)
I1<-order(age)
plot(age[I1], test.pred$fit[I1],lty=1, type="l")
lines(age[I1],test.pred$fit[I1]+2*M1pred$se[I1],lty=2)
lines(age[I1],test.pred$fit[I1]-2*M1pred$se[I1],lty=2)
plot(age[I1], test.pred$fit[I1] , type="l")
I a second step, I want to calculate a similar model, but only for
respondents with a certain characteristic. For example, in this case, only
for male respondents.
I use a code that looks like this:
participant_male <- participant[gender=="male"]
age_male <- age[gender=="male"]
test2<-gam(participant_male ~ s(age_male, fx=FALSE, bs="cr"),
family=binomial(logit), na.action=na.omit)
summary(test2)
plot(test2, shade=TRUE)
I get a nice smoother function in this plot, like I expected.
Then, when I want to plot the predicted values, I use a code that looks like
this:
Test2.pred <- predict(test5,se.fit=TRUE, type="response")
I1<-order(age_male)
plot(age_male[I1], test2.pred$fit[I1],lty=1)
This last plot, of the predictions, is not what I expect. Its just a random
scatterplot, not what I would expect from the smoother plot. Does anybody
know what I did wrong?
Thanks in advance,
Jef Vlegels
Jef Vlegels
Ghent University - Department of Sociology
Korte Meer 3, B-9000 Gent, Belgium
Tel: 09 264 8343
www.psw.UGent.be/sociologie
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