mfxboot function for marginal effects for probit regressions?

问题

Data: Data

Code:

#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)

Question:

When I run the mfxboot function, I get the following error message:

Error in bootvals[i, ] <- pdf1 * coef(x1) : number of items to replace is not a multiple of replacement length

Any idea as to why that happens? And Any suggestion of how to go around this issue?

Thanks.

回答1:

I am not able to reproduce your error. Perhaps you should add sessionInfo() output to your question. A suggested enhancement to the mfxboot function follows below nonetheless.

My suggestion would be to refactor the mfxboot function into two functions -- one that returns the marginal effects given a glm object, and the second which bootstraps it.

You can do this easily using the Boot function in the car package since that is a nice front-end for bootstrapping glm objects.

Here is some code that demonstrates this process, which is much cleaner to read:

Step 1: Estimate a probit model

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)

Step 2: Write a function that returns the marginal effects

The following function takes as input a glm object of the binomial family and computes appropriate marginal effects for logit and probit links.

#================================================
# 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)

Step 3: Bootstrap the marginal effects

The following code uses the Boot function from the car package to bootstrap the marginal effects. Note how the interface of Boot is optimized for statistics derived from lm and glm objects.

#================================================
# compute bootstrap std. err. for the marginal effects
#================================================
margEffBootE = Boot(object = glmE, f = fnMargEffBin, 
     labels = names(coef(glmE))[-1], R = 100)
summary(margEffBootE)

Here is the output:

> 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

来源：https://stackoverflow.com/questions/26489859/mfxboot-function-for-marginal-effects-for-probit-regressions

标签

marginal-effects