Strange behavior of the mlogit effects command in R
I am evaluating a multicomponent logit model and would like to report the marginal effect. I am running into difficulties because when I use a large version of the model I get an error.
Here's an example of reproducibility. The following code with two covariates works great.
library(mlogit)
df = data.frame(c(0,1,1,2,0,1,0), c(1,6,7,4,2,2,1), c(683,276,756,487,776,100,982))
colnames(df) <- c('y', 'col1', 'col3')
df$col2<-df$col1^2
mydata = df
mldata <- mlogit.data(mydata, choice="y", shape="wide")
mlogit.model1 <- mlogit(y ~ 1| col1+col2, data=mldata)
m <- mlogit(y ~ 1| col1+col2, data = mldata)
z <- with(mldata, data.frame(col1 = tapply(col1, index(m)$alt, mean),
col2 = tapply(col2, index(m)$alt, mean) ) )
effects(mlogit.model1, covariate = "col1", data = z)
Now that I have three covariates:
mlogit.model1 <- mlogit(y ~ 1| col1+col2+col3, data=mldata)
m <- mlogit(y ~ 1| col1+col2+col3, data = mldata)
z <- with(mldata, data.frame(col1 = tapply(col1, index(m)$alt, mean),
col2 = tapply(col2, index(m)$alt, mean),
col3 = tapply(col3, index(m)$alt, mean) ) )
effects(mlogit.model1, covariate = "col1", data = z)
The last line displays the following error:
Error in if (rhs% in% c (1, 3)) {: argument has length 0
But if I run
effects(mlogit.model1, covariate = "col3", data = z)
then it works fine to provide marginal effects col3
. Why not give marginal effects col1
?
Note that all columns do not contain NULL
and are the same length. Can anyone explain the reason for this behavior?
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I believe this will help you find a solution.
Link: http://www.talkstats.com/showthread.php/44314-calculate-marginal-effects-using-mlogit-package
> methods(effects)
[1] effects.glm* effects.lm* effects.mlogit*
see '?methods' for accessing help and source code
Note: Non-visible functions are asterisked
Explanation:
Little conversion is needed in the effects.mlogit source code.
On line 16, you must replace "cov.list <- lapply (attr (formula (object)," rhs "), as.character)" with "cov.list <- strsplit (as.character (attr (formula (object) , "rhs")), "+", fixed = TRUE) "
Fix the result:
> effects(mlogit.model1, covariate = "col1", data = z)
0 1 2
-4.135459e-01 4.135459e-01 9.958986e-12
> myeffects(mlogit.model2, covariate = "col1", data = z2)
0 1 2
1.156729129 -1.157014778 0.000285649
code
require(mlogit)
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
}
df = data.frame(c(0,1,1,2,0,1,0), c(1,6,7,4,2,2,1), c(683,276,756,487,776,100,982))
colnames(df) <- c('y', 'col1', 'col3')
df$col2<-df$col1^2
mydata = df
mldata <- mlogit.data(mydata, choice="y", shape="wide")
mlogit.model1 <- mlogit(y ~ 1| col1+col2, data=mldata)
m <- mlogit(y ~ 1| col1+col2, data = mldata)
z <- with(mldata, data.frame(col1 = tapply(col1, index(m)$alt, mean),
col2 = tapply(col2, index(m)$alt, mean) ) )
mldata2 <- mlogit.data(mydata, choice="y", shape="wide")
mlogit.model2 <- mlogit(y ~ 1| col1+col2+col3, data=mldata2)
m2 <- mlogit(y ~ 1| col1+col2+col3, data = mldata2)
z2 <- with(mldata, data.frame(col1 = tapply(col1, index(m2)$alt, mean),
col2 = tapply(col2, index(m2)$alt, mean),
col3 = tapply(col3, index(m2)$alt, mean) ) )
effects(mlogit.model1, covariate = "col1", data = z)
myeffects(mlogit.model2, covariate = "col1", data = z2)
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