数据：Data
代码：
#function that calculates ‘the average of the sample marginal effects’.
mfxboot <- function(modform,dist,data,boot=1000,digits=3){
x <- glm(modform, family=binomial(link=dist),data)
# get marginal effects
pdf <- ifelse(dist=="probit",
mean(dnorm(predict(x, type = "link"))),
mean(dlogis(predict(x, type = "link"))))
marginal.effects <- pdf*coef(x)
# start bootstrap
bootvals <- matrix(rep(NA,boot*length(coef(x))), nrow=boot)
set.seed(1111)
for(i in 1:boot){
samp1 <- data[sample(1:dim(data)[1],replace=T,dim(data)[1]),]
x1 <- glm(modform, family=binomial(link=dist),samp1)
pdf1 <- ifelse(dist=="probit",
mean(dnorm(predict(x, type = "link"))),
mean(dlogis(predict(x, type = "link"))))
bootvals[i,] <- pdf1*coef(x1)
}
res <- cbind(marginal.effects,apply(bootvals,2,sd),marginal.effects/apply(bootvals,2,sd))
if(names(x$coefficients[1])=="(Intercept)"){
res1 <- res[2:nrow(res),]
res2 <- matrix(as.numeric(sprintf(paste("%.",paste(digits,"f",sep=""),sep=""),res1)),nrow=dim(res1)[1])
rownames(res2) <- rownames(res1)
} else {
res2 <- matrix(as.numeric(sprintf(paste("%.",paste(digits,"f",sep=""),sep="")),nrow=dim(res)[1]))
rownames(res2) <- rownames(res)
}
colnames(res2) <- c("marginal.effect","standard.error","z.ratio")
return(res2)
}
## Regression
probit_enae = glm(emploi ~ genre + filiere + satisfaction + competence + anglais, family=binomial(link="probit"),
data=ENAE_Probit.df)
summary(probit_enae) #Summary output of the regression
confint(probit_enae) #Gives the 95% confidence interval for the estimated coefficients
## Running the mfxboot for Marginal effects
mfx_enae = mfxboot(emploi ~ genre + filiere + satisfaction + competence + anglais,"probit",ENAE_Probit.df)
问题：
当我运行mfxboot函数时，收到以下错误消息：
引导错误[i，]＆lt; - pdf1 * coef（x1）： 要替换的项目数量不是替换长度的倍数
知道为什么会这样吗？以及如何解决这个问题的任何建议？
感谢。
答案 0 :(得分：5)
我无法重现您的错误。也许您应该在问题中添加sessionInfo()输出。尽管如此，对mfxboot函数的建议增强仍然如下。
我的建议是将mfxboot函数重构为两个函数 - 一个
给出glm对象的第二个返回边际效应
引导它。
您可以使用car包中的Boot功能轻松完成此操作
这是引导glm对象的一个很好的前端。
以下是一些演示此过程的代码，可以更清晰地阅读：
library(car)
#================================================
# read in data, and estimate a probit model
#================================================
dfE = read.csv("ENAE_Probit.csv")
formE = emploi ~ genre +
filiere + satisfaction + competence + anglais
glmE = glm(formula = formE,
family = binomial(link = "probit"),
data = dfE)
以下函数将glm系列的binomial对象作为输入，并为logit和probit链接计算适当的边际效应。
#================================================
# function: compute marginal effects for logit and probit models
# NOTE: this assumes that an intercept has been included by default
#================================================
fnMargEffBin = function(objBinGLM) {
stopifnot(objBinGLM$family$family == "binomial")
vMargEff = switch(objBinGLM$family$link,
probit = colMeans(outer(dnorm(predict(objBinGLM,
type = "link")),
coef(objBinGLM))[, -1]),
logit = colMeans(outer(dlogis(predict(objBinGLM,
type = "link")),
coef(objBinGLM))[, -1])
)
return(vMargEff)
}
# test the function
fnMargEffBin(glmE)
以下代码使用Boot包中的car函数来引导边际效应。请注意Boot的界面如何针对从lm和glm对象派生的统计信息进行优化。
#================================================
# compute bootstrap std. err. for the marginal effects
#================================================
margEffBootE = Boot(object = glmE, f = fnMargEffBin,
labels = names(coef(glmE))[-1], R = 100)
summary(margEffBootE)
这是输出：
> summary(margEffBootE)
R original bootBias bootSE bootMed
genre 100 0.070733 0.00654606 0.042162 0.074563
filiere 100 0.043173 0.00060356 0.014064 0.043486
satisfaction 100 0.050773 -0.00110501 0.037737 0.048310
competence 100 -0.020144 0.00407027 0.034194 -0.014987
anglais 100 -0.018906 -0.00170887 0.033522 -0.019164