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# Generic synthetic probit regression 24Sept 2010
# Hilbe, Negative Binomial Regression, 2 ed, Cambridge Univ Press
# Hilbe, Logistic Regression Models, Chapman & Hall/CRC
require(MASS) # probit_syn.r
probit_syn <- function(nobs=50000, d = 1, xv = c(1, 0.5, -1.5)) {
p <- length(xv) - 1
X <- cbind(1, matrix(rnorm(nobs * p), ncol = p))
xb <- X %*% xv
pxb <- pnorm(xb)
py <- rbinom(nobs, size = d, prob =pxb)
dpy <- d - py
out <- data.frame(cbind(cbind(py,dpy), X[,-1]))
names(out) <- c("py","dpy", paste("x", 1:p, sep=""))
return(out)
}
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