Dear Friends,
My problem is related to how to measure probabilities from a probit model by
changing one independent variable keeping the others constant.
A simple toy example is like this
Range for my variables is defined as follows
y=0 or 1, x1 = -10 to 10, x2=-40 to 100, x3 = -5 to 5
Model
output <- glim(y ~ x1+x2+x3 -1, family=binomial(link="probit"))
outcoef <- output$coef
xbeta <- as.matrix(cbind(x1, x2, x3)
predprob <- pnorm(xbeta%*%outcoef)
now I have the predicted probabilities for y=1 as defined above. My problem is
as follows
Keep X2 at 20 and X3 at 2. Then compute the predicted probability (predprob)
for the entire range of X1 ie from -10 to 10 with an increment of 1.
Therefore i need the predicted probabilities when x1=-10, x1=-9....,x1=9, x1=10
keeping the other constant.
Can somebody give me some direction on how this can be programmed.
Thanks in advance for your help
Sincerely
Anup
---------------------------------
Got a little couch potato?
Check out fun summer activities for kids.
[[alternative HTML version deleted]]
______________________________________________
R-help@stat.math.ethz.ch 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.
