% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/margex.R
\docType{data}
\name{margex}
\alias{margex}
\title{Artificial data for margins, copied from Stata}
\format{A data frame with 3000 observations on the following 11 variables.
 \describe{
   \item{\samp{y}}{A numeric vector}
   \item{\samp{outcome}}{A binary numeric vector with values (0,1)}
   \item{\samp{sex}}{A factor with two levels}
   \item{\samp{group}}{A factor with three levels}
   \item{\samp{age}}{A numeric vector}
   \item{\samp{distance}}{A numeric vector}
   \item{\samp{ycn}}{A numeric vector}
   \item{\samp{yc}}{A numeric vector}
   \item{\samp{treatment}}{A factor with two levels}
   \item{\samp{agegroup}}{A factor with three levels}
   \item{\samp{arm}}{A factor with three levels}
 }}
\source{
\url{http://www.stata-press.com/data/r14/margex.dta}
}
\usage{
margex
}
\description{
The dataset is identical to the one provided by Stata and available from \code{webuse::webuse("margex")} with categorical variables explicitly encoded as factors.
}
\examples{
\donttest{

# Examples from Stata's help files
# Also available from: webuse::webuse("margex")
data("margex")

# A simple case after regress
# . regress y i.sex i.group
# . margins sex
m1 <- lm(y ~ factor(sex) + factor(group), data = margex)
prediction(m1, at = list(sex = c("male", "female")))

# A simple case after logistic
# . logistic outcome i.sex i.group
# . margins sex
m2 <- glm(outcome ~ sex + group, binomial(), data = margex)
prediction(m2, at = list(sex = c("male", "female")))

# Average response versus response at average
# . margins sex
prediction(m2, at = list(sex = c("male", "female")))
# . margins sex, atmeans
## TODO

# Multiple margins from one margins command
# . margins sex group
prediction(m2, at = list(sex = c("male", "female")))
prediction(m2, at = list(group = c("1", "2", "3")))

# Margins with interaction terms
# . logistic outcome i.sex i.group sex#group
# . margins sex group
m3 <- glm(outcome ~ sex * group, binomial(), data = margex)
prediction(m3, at = list(sex = c("male", "female")))
prediction(m3, at = list(group = c("1", "2", "3")))

# Margins with continuous variables
# . logistic outcome i.sex i.group sex#group age
# . margins sex group
m4 <- glm(outcome ~ sex * group + age, binomial(), data = margex)
prediction(m4, at = list(sex = c("male", "female")))
prediction(m4, at = list(group = c("1", "2", "3")))

# Margins of continuous variables
# . margins, at(age=40)
prediction(m4, at = list(age = 40))
# . margins, at(age=(30 35 40 45 50))
prediction(m4, at = list(age = c(30, 35, 40, 45, 50)))

# Margins of interactions
# . margins sex#group
prediction(m4, at = list(sex = c("male", "female"), group = c("1", "2", "3")))

}
}
\seealso{
\code{\link{prediction}}
}
\keyword{datasets}
