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

       
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