I'm having trouble with rpar argument in the mlogit function (package mlogit).
My dataset looks like this:
> head(scan.s)
year id.scan day weather sealvl wave lact repro nb.gr HS nb.pv pup act
1 2011 1 4 2 0.30 3 1 0 0.6666667 7.600000 3 0 R
2 2011 1 4 2 0.30 3 1 0 0.6666667 7.600000 3 0 R
3 2011 1 4 2 0.30 3 1 0 0.6666667 7.600000 3 1 R
4 2011 2 4 2 0.35 3 1 0 0.6666667 8.100000 2 0 R
5 2011 2 4 2 0.35 3 1 0 0.6666667 8.100000 2 1 R
6 2011 3 4 2 0.40 3 1 0 0.6666667 8.633333 2 0 R
> str(scan.s)
'data.frame': 10140 obs. of 13 variables:
$ year : int 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 ...
$ id.scan: Factor w/ 280 levels "1","2","3","4",..: 1 1 1 2 2 3 3 4 4 5 ...
$ day : int 4 4 4 4 4 4 4 4 4 4 ...
$ weather: Factor w/ 3 levels "1","2","3": 2 2 2 2 2 2 2 2 2 2 ...
$ sealvl : num 0.3 0.3 0.3 0.35 0.35 0.4 0.4 0.5 0.5 0.6 ...
$ wave : Factor w/ 4 levels "1","2","3","4": 3 3 3 3 3 3 3 3 3 3 ...
$ lact : int 1 1 1 1 1 1 1 1 1 1 ...
$ repro : int 0 0 0 0 0 0 0 0 0 0 ...
$ nb.gr : num 0.667 0.667 0.667 0.667 0.667 ...
$ HS : num 7.6 7.6 7.6 8.1 8.1 ...
$ nb.pv : int 3 3 3 2 2 2 2 2 2 2 ...
$ pup : int 0 0 1 0 1 0 1 0 1 0 ...
$ act : Factor w/ 5 levels "A","C","D","G",..: 5 5 5 5 5 5 5 5 5 5 ...
Then I used mlogit.data to transform my dataset in long shape:
> scan.l<- mlogit.data(scan, varying = NULL, choice = "act", shape = "wide")
There is no variable varying across choices.
year id.scan day weather sealvl wave lact repro nb.gr HS nb.pv pup act chid alt
1.A 2011 1 4 2 0.3 3 1 0 0.6666667 7.6 3 0 FALSE 1 A
1.C 2011 1 4 2 0.3 3 1 0 0.6666667 7.6 3 0 FALSE 1 C
1.D 2011 1 4 2 0.3 3 1 0 0.6666667 7.6 3 0 FALSE 1 D
1.G 2011 1 4 2 0.3 3 1 0 0.6666667 7.6 3 0 FALSE 1 G
1.R 2011 1 4 2 0.3 3 1 0 0.6666667 7.6 3 0 TRUE 1 R
2.A 2011 1 4 2 0.3 3 1 0 0.6666667 7.6 3 0 FALSE 2 A
> str(scan.l)
Classes ‘mlogit.data’ and 'data.frame': 50700 obs. of 15 variables:
$ year : int 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 ...
$ id.scan: Factor w/ 280 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
$ day : int 4 4 4 4 4 4 4 4 4 4 ...
$ weather: Factor w/ 3 levels "1","2","3": 2 2 2 2 2 2 2 2 2 2 ...
$ sealvl : num 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 ...
$ wave : Factor w/ 4 levels "1","2","3","4": 3 3 3 3 3 3 3 3 3 3 ...
$ lact : int 1 1 1 1 1 1 1 1 1 1 ...
$ repro : int 0 0 0 0 0 0 0 0 0 0 ...
$ nb.gr : num 0.667 0.667 0.667 0.667 0.667 ...
$ HS : num 7.6 7.6 7.6 7.6 7.6 7.6 7.6 7.6 7.6 7.6 ...
$ nb.pv : int 3 3 3 3 3 3 3 3 3 3 ...
$ pup : int 0 0 0 0 0 0 0 0 0 0 ...
$ act : logi FALSE FALSE FALSE FALSE TRUE FALSE ...
$ chid : num 1 1 1 1 1 2 2 2 2 2 ...
$ alt : chr "A" "C" "D" "G" ...
- attr(*, "index")='data.frame': 50700 obs. of 2 variables:
..$ chid: Factor w/ 10140 levels "1","2","3","4",..: 1 1 1 1 1 2 2 2 2 2 ...
..$ alt : Factor w/ 5 levels "A","C","D","G",..: 1 2 3 4 5 1 2 3 4 5 ...
- attr(*, "choice")= chr "act"
Then I ran the model:
mod1 <- mlogit(act ~ 1| nb.gr+nb.pv+sealvl+lact+repro+HS+day+id.scan,data = na.omit(scan.l), rpar=id.scan, format="long", reflevel="R", R=100, halton=NA, print.level=0)
The random parameter here is a factor and I am supposed to specify a distribution for rpar but is it relevant for a factor? (I tried to provide a distribution without any change).
And then I get this:
Error in coef(eval(callst, parent.frame())) :
error in evaluating the argument 'object' in selecting a method for function'coef' : Error in solve.default(H, g[!fixed]) : Lapack routine dgesv: system is exactly singular
There is a way to use "HS" and "day" instead, both numerical. But then I get another error:
Error in names (sup.coef) <- names.sup.coef: Attribute 'names' [1] must be the same length as the vector [0]
traceback() did not provide any insight about what happened.
I searched for explanations with those errors and found that there could be a problem between one outcome and the random effect so I tried to subset my dataset with every combination of 3 outcomes with the same result. I found nothing relevant about the second error. Maybe it has something to do with the transformation with mlogit.data. I checked the dataset provided with the mlogit package and could not figure out what I did different.
I would be grateful if someone could explain what is happening here. Thanks!
