marginal effects of mlogit in R

Question

I am new to R, and I don\'t understand yet completely the logic of its calculations...

I cannot overcome my problem with the help from previous posts either.

Answer 1

Ok, seems like I have kind of solution with the help of:

http://www.talkstats.com/showthread.php/44314-calculate-marginal-effects-using-mlogit-package

and

How can I view the source code for a function?

apparently it was enough to see for the:

> getAnywhere(effects.mlogit)
A single object matching ‘effects.mlogit’ was found
It was found in the following places
  registered S3 method for effects from namespace mlogit
  namespace:mlogit
with value

function (object, covariate = NULL, type = c("aa", "ar", "rr", 
    "ra"), data = NULL, ...) 
{
    type <- match.arg(type)
    if (is.null(data)) {
        P <- predict(object, returnData = TRUE)
        data <- attr(P, "data")
        attr(P, "data") <- NULL
    }
    else P <- predict(object, data)
    newdata <- data
    J <- length(P)
    alt.levels <- names(P)
    pVar <- substr(type, 1, 1)
    xVar <- substr(type, 2, 2)
    cov.list <- lapply(attr(formula(object), "rhs"), as.character)
    rhs <- sapply(cov.list, function(x) length(na.omit(match(x, 
        covariate))) > 0)
    rhs <- (1:length(cov.list))[rhs]
    eps <- 1e-05
    if (rhs %in% c(1, 3)) {
        if (rhs == 3) {
            theCoef <- paste(alt.levels, covariate, sep = ":")
            theCoef <- coef(object)[theCoef]
        }
        else theCoef <- coef(object)[covariate]
        me <- c()
        for (l in 1:J) {
            newdata[l, covariate] <- data[l, covariate] + eps
            newP <- predict(object, newdata)
            me <- rbind(me, (newP - P)/eps)
            newdata <- data
        }
        if (pVar == "r") 
            me <- t(t(me)/P)
        if (xVar == "r") 
            me <- me * matrix(rep(data[[covariate]], J), J)
        dimnames(me) <- list(alt.levels, alt.levels)
    }
    if (rhs == 2) {
        newdata[, covariate] <- data[, covariate] + eps
        newP <- predict(object, newdata)
        me <- (newP - P)/eps
        if (pVar == "r") 
            me <- me/P
        if (xVar == "r") 
            me <- me * data[[covariate]]
        names(me) <- alt.levels
    }
    me
}

Then to copy the function and modyfy its 16 line:

myeffects<-function (object, covariate = NULL, type = c("aa", "ar", "rr", 
                                             "ra"), data = NULL, ...) 
{
    type <- match.arg(type)
    if (is.null(data)) {
        P <- predict(object, returnData = TRUE)
        data <- attr(P, "data")
        attr(P, "data") <- NULL
    }
    else P <- predict(object, data)
    newdata <- data
    J <- length(P)
    alt.levels <- names(P)
    pVar <- substr(type, 1, 1)
    xVar <- substr(type, 2, 2)
    cov.list <- strsplit(as.character(attr(formula(object), "rhs")), " + ", fixed = TRUE)
    rhs <- sapply(cov.list, function(x) length(na.omit(match(x, 
                                                             covariate))) > 0)
    rhs <- (1:length(cov.list))[rhs]
    eps <- 1e-05
    if (rhs %in% c(1, 3)) {
        if (rhs == 3) {
            theCoef <- paste(alt.levels, covariate, sep = ":")
            theCoef <- coef(object)[theCoef]
        }
        else theCoef <- coef(object)[covariate]
        me <- c()
        for (l in 1:J) {
            newdata[l, covariate] <- data[l, covariate] + eps
            newP <- predict(object, newdata)
            me <- rbind(me, (newP - P)/eps)
            newdata <- data
        }
        if (pVar == "r") 
            me <- t(t(me)/P)
        if (xVar == "r") 
            me <- me * matrix(rep(data[[covariate]], J), J)
        dimnames(me) <- list(alt.levels, alt.levels)
    }
    if (rhs == 2) {
        newdata[, covariate] <- data[, covariate] + eps
        newP <- predict(object, newdata)
        me <- (newP - P)/eps
        if (pVar == "r") 
            me <- me/P
        if (xVar == "r") 
            me <- me * data[[covariate]]
        names(me) <- alt.levels
    }
    me
}

Now the results are as of:

 > myeffects(mlogit.data2,covariate="RSVS",data=zz)
            2             0             1             4             5             6 
 3.612318e-03  5.368693e-04 -4.903995e-08 -5.382731e-03  1.238768e-03 -5.175053e-06