Not sure whether the question is more about doing the sampling of alternatives or the estimation of MNL models after sampling of alternatives. To my knowledge, there are no R packages that do sampling of alternatives (the former) so far, but the latter is possible with existing packages such as mlogit. I believe the reason is that the sampling process varies depending on how your data is organized, but it is relatively easy to do with a bit of your own code. Below is code adapted from what I used for this paper.
library(tidyverse)
# create artificial data
set.seed(6)
# data frame of choser id and chosen alt_id
id_alt <- data.frame(
id = 1:1000,
alt_chosen = sample(1:30, 1)
)
# data frame for universal choice set, with an alt-specific attributes (alt_x2)
alts <- data.frame(
alt_id = 1:30,
alt_x2 = runif(30)
)
# conduct sampling of 9 non-chosen alternatives
id_alt <- id_alt %>%
mutate(.alts_all =list(alts$alt_id),
# use weights to avoid including chosen alternative in sample
.alts_wtg = map2(.alts_all, alt_chosen, ~ifelse(.x==.y, 0, 1)),
.alts_nonch = map2(.alts_all, .alts_wtg, ~sample(.x, size=9, prob=.y)),
# combine chosen & sampled non-chosen alts
alt_id = map2(alt_chosen, .alts_nonch, c)
)
# unnest above data.frame to create a long format data frame
# with rows varying by choser id and alt_id
id_alt_lf <- id_alt %>%
select(-starts_with(".")) %>%
unnest(alt_id)
# join long format df with alts to get alt-specific attributes
id_alt_lf <- id_alt_lf %>%
left_join(alts, by="alt_id") %>%
mutate(chosen=ifelse(alt_chosen==alt_id, 1, 0))
require(mlogit)
# convert to mlogit data frame before estimating
id_alt_mldf <- mlogit.data(id_alt_lf,
choice="chosen",
chid.var="id",
alt.var="alt_id", shape="long")
mlogit( chosen ~ 0 + alt_x2, id_alt_mldf) %>%
summary()
It is, of course, possible without using the purrr::map functions, by using apply variants or looping through each row of id_alt.
