在R
中,stargazer
包提供了将函数应用于系数,标准错误等的可能性:
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)
如何应用一个函数,其中我乘以的因子取决于变量,如该变量的标准偏差?
答案 0 :(得分:0)
这可能与您的预期完全不同,但您可以转换系数,并为stargazer
提供自定义list
个系数。例如,如果您想报告系数乘以每个变量的标准偏差,则示例的以下扩展可以起作用:
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))
您可能需要考虑原始问题之外的一些关于在stargazer
中输出系数的问题。你应该在乘以标准偏差时报告截距吗?您的标准错误和推断是否与此转换相同?