In R
, the stargazer
package offers the possibility to apply functions to the coefficients, standard errors, etc:
dat <- read.dta("http://www.ats.ucla.edu/stat/stata/dae/nb_data.dta")
dat <- within(dat, {
prog <- factor(prog, levels = 1:3, labels = c("General", "Academic", "Vocational"))
id <- factor(id)
})
m1 <- glm.nb(daysabs ~ math + prog, data = dat)
transform_coef <- function(x) (exp(x) - 1)
stargazer(m1, apply.coef=transform_coef)
How can I apply a function where the factor with which I multiply depends on the variable, like the standard deviation of that variable?
This may not be exactly what you hoped for, but you can transform the coefficients, and give stargazer
a custom list
of coefficients. For example, if you would like to report the coefficient times the standard deviation of each variable, the following extension of your example could work:
library(foreign)
library(stargazer)
library(MASS)
dat <- read.dta("http://www.ats.ucla.edu/stat/stata/dae/nb_data.dta")
dat <- within(dat, {
prog <- factor(prog, levels = 1:3, labels = c("General", "Academic", "Vocational"))
id <- factor(id)
})
m1 <- glm.nb(daysabs ~ math + prog, data = dat)
# Store coefficients (and other coefficient stats)
s1 <- summary(m1)$coefficients
# Calculate standard deviations (using zero for the constant)
math.sd <- sd(dat$math)
acad.sd <- sd(as.numeric(dat$prog == "Academic"))
voc.sd <- sd(as.numeric(dat$prog == "Vocational"))
int.sd <- 0
# Append standard deviations to stored coefficients
StdDev <- c(int.sd, math.sd, acad.sd, voc.sd)
s1 <- cbind(s1, StdDev)
# Store custom list
new.coef <- s1[ , "Estimate"] * s1[ , "StdDev"]
# Output
stargazer(m1, coef = list(new.coef))
You may want to consider a couple of issues outside your original question about outputting coefficients in stargazer
. Should you report the intercept when multiplying times the standard deviation? Will your standard errors and inference be the same with this transformation?