R mlogit() function: Error in if (abs(x - oldx) < ftol) { : missing value where TRUE/FALSE needed

Question

I am having trouble with mlogit() function. I am trying to predict which variables in a given set are the most preferred amongst people who took our survey. I am trying to predict the optimal combination of variables to create the most preferred option. Basically, we are measuring "Name", "Logo Size", "Design", "Theme","Flavor", and "Color".

To do this, we have a large data set and are trying to run it through mlogit.data() and mlogit(), although we keep getting the same error:

Error in if (abs(x - oldx) < ftol) { : missing value where TRUE/FALSE needed

None of my data is negative or missing, so this is very confusing. My syntax is:

#Process data in mlogit.data()

data2 <- 
  mlogit.data(data=data, choice="Choice", 
              shape="long", varying=5:10, 
              alt.levels=paste("pos",1:3))

#Make character columns factors and "choice" column (the one we are 
#measuring) a numeric.

data2$Name <- as.factor(data2$Name)
data2$Logo.Size <- as.factor(data2$Logo.Size)
data2$Design <- as.factor(data2$Design)
data2$Theme <- as.factor(data2$Theme)
data2$Color <- as.factor(data2$Color)
data2$Choice <- as.numeric(as.character(data2$Choice))

##### RUN MODEL ##### 
m1 <- mlogit(Choice ~ 0 + Name + Logo.Size + Design + Theme + Flavor 
+ Color, data = data2)

m1

Does it look like there is a problem with my syntax, or is it likely my data that is the problem?

Answer 1

In a panel setting, it is potentially the case that one or more of your choice cards does not have a TRUE value. One fix would be to drop choice cards that are missing a choice.

## Use data.table
library(data.table)

## Drop choice cards that received no choice
data.table[, full := sum(Choice), by=Choice_id]
data.table.full <- data.table[full!=0,]

This is an issue specific to mlogit(). For example, STATA's mixed logit approach ignores missing response variables, R views this as more of an issue that needs to be addressed.

Answer 2

I had similar issue, but eventually figured out. In my case, it is due to missing value of the covariates not the choice response.