Simulates fitted probabilities for a probit regression model
Gives a simulated sample for fitted probabilities for a binary response regression model with a probit link and noninformative prior.
bprobit.probs(X1,fit)
X1 |
matrix where each row corresponds to a covariate set |
fit |
simulated matrix of draws of the regression vector |
matrix of simulated draws of the fitted probabilities, where a column corresponds to a particular covariate set
Jim Albert
response=c(0,1,0,0,0,1,1,1,1,1) covariate=c(1,2,3,4,5,6,7,8,9,10) X=cbind(1,covariate) m=1000 fit=bayes.probit(response,X,m) x1=c(1,3) x2=c(1,8) X1=rbind(x1,x2) fittedprobs=bprobit.probs(X1,fit$beta)
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