I was trying to estimate the mixed-effects logit in R using mlogit package. I would be grateful if someone could point out where is my mistake.
My original data has a panel structure - each individual makes choice in 6 different scenarios. Each time he has 4 options (3 brands and non getting anything). That is, there are 24 rows for each individual in the dataset.
I have few individual specific demographics. There are also few alternative-specific characteristics: one is price (it is different for each alternative in each scenario), others are just dummy variables that are alternative specific. These dummies indicate specific brands of the same product. The coefficient for each dummy varies according to the demographic characteristics.
I have read the Yves Croissant's pdf on the estimation of multinomial logit models in R, and was able to adapt the data to the format suitable for mlogit estimation.
data.logit = mlogit.data(data, shape = "long",
alt.var = "alt_id",
choice = "pick",
id.var = "idnum",
sep = "")
me.logit = mlogit (pick ~ price | gender + age + educ + income,
data.logit,
reflevel = 4,
rpar = c("brand1:(intercept)" = "n",
"brand2:(intercept)" = "n",
"brand3:(intercept)" = "n"),
R = 500,
halton = NA,
panel = TRUE)
summary(me.logit)
The results I get do not make sense to me. For two brands the alternative specific constants (coefficients for the dummies) are negative, the coefficient for the price is, obviously, negative too. Then the WTP for those brands becomes negative which is definitely wrong.
I think that there may be a mistake in the way I assigned parameters when using mlogit.data
.