Say I have a formula Y ~ 1 + X, where X is a categorical variable. A
previous thread showed how to evaluate this model using the mle package
from "stats4" (see below). But, the user had to create the data matrix,
X, including the column of one's for the regression constant. Is there a
way to nest the linear formula in the code below, so the data matrix
doesn't explicitly have to be created by the user?
Y <- c(0,0,1,0,0,1,1,0,0,0,0,1,1,0,1,1,0,1,1,0,1)
X <-
cbind(matrix(1,21,1),matrix(c(-48.5,24.4,82.8,-24.6,-31.6,91.0,52.1,-87.7,-17.0,-51.5,
-90.7,65.5,-44.0,-7.0,51.6,32.4,-61.8,34.0,27.9,-72.9,49.9), 21,1))
log.lo.like <- function(beta,Y,X) {
Fbetax <- 1/(1+exp(-beta%*%t(X)))
loglbeta <- -log(prod(Fbetax^Y*(1-Fbetax)^(1-Y)))
}
#####Using MLE#####
ll <- eval(function(beta0=0,beta1=0)
log.lo.like (c(beta0,beta1),Y,X),
list(X=X,Y=Y))
summary(mle(ll))
####Comparison using glm#####
glm(Y~X-1,family=binomial)
Thanks,
Stephen Collins, MPP | Analyst
Global Strategy | Aon Benfield
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