R Mlogit Model, Computationally Singular

R mlogit model, computationally singular

The model is unable to interpret your dishId as the alternative index (alt.var) because you have different keypairs for different choices. For example, you have "TS" and "RS" as alternative index keys for the first choice in your .csv file but you have "RR" and "RS" as keys for choice 3634. Additionally, you did also not specify the names of the alternatives (alt.levels). As a result of the fact that alt.levels is not filled in, mlogit.data will automatically try to detect the alternatives based upon the alternative index, which it cannot correctly interpret. This is basically where everything goes wrong: The 'food' and 'plate' variables are not interpreted as alternatives but they are considered as individual specific variables that eventually end up causing singularity issues.

You have two options to fix the issue. You can give the actual alternatives as input to mlogit.data through the alt.levels parameter:

TM <- mlogit.data(raw, choice = "selected", shape = "long", alt.levels = c("food","plate"),chid.var = "individuals",drop.index=TRUE)
model1 <- mlogit(selected ~ food + plate | sex + age +hand, data = TM)

Alternatively, you could opt to make your index keys consistent so that you can give them as input via alt.var. mlogit.data will now be able to correctly guess what your alternatives are:

raw[,3] <- rep(1:2,nrow(raw)/2) # use 1 and 2 as unique alternative keys for all choices
TM <- mlogit.data(raw, choice = "selected", shape = "long", alt.var="dishId", chid.var = "individuals")
model2 <- model <- mlogit(selected ~ food + plate | sex + age +hand, data = TM)

We verify that both models are indeed identical. The results of model 1:

> summary(model1)

Call:
mlogit(formula = selected ~ food + plate | sex + age + hand, 
    data = TM, method = "nr", print.level = 0)

Frequencies of alternatives:
   food   plate 
0.42847 0.57153 

nr method
4 iterations, 0h:0m:0s 
g'(-H)^-1g = 0.00423 
successive function values within tolerance limits 

Coefficients :
                    Estimate Std. Error t-value  Pr(>|t|)    
plate:(intercept) -0.0969627  0.0764117 -1.2689 0.2044589    
foodCirc           1.0374881  0.0339559 30.5540 < 2.2e-16 ***
plateCirc         -0.0064866  0.0524547 -0.1237 0.9015835    
plate:sexmale     -0.0811157  0.0416113 -1.9494 0.0512512 .  
plate:age16-34     0.1622542  0.0469167  3.4583 0.0005435 ***
plate:age35-54     0.0312484  0.0555634  0.5624 0.5738492    
plate:age55-74     0.0556696  0.0836248  0.6657 0.5055987    
plate:age75+       0.1057646  0.2453797  0.4310 0.6664508    
plate:handright   -0.0177260  0.0539510 -0.3286 0.7424902    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Log-Likelihood: -8284.6
McFadden R^2:  0.097398 
Likelihood ratio test : chisq = 1787.9 (p.value = < 2.22e-16)

Versus the results of model 2. Note that the alternatives are correctly identified, but the names are not explicitly added to the model:

> summary(model2)

Call:
mlogit(formula = selected ~ food + plate | sex + age + hand, 
    data = TM, method = "nr", print.level = 0)

Frequencies of alternatives:
      1       2 
0.42847 0.57153 

nr method
4 iterations, 0h:0m:0s 
g'(-H)^-1g = 0.00423 
successive function values within tolerance limits 

Coefficients :
                Estimate Std. Error t-value  Pr(>|t|)    
2:(intercept) -0.0969627  0.0764117 -1.2689 0.2044589    
foodCirc       1.0374881  0.0339559 30.5540 < 2.2e-16 ***
plateCirc     -0.0064866  0.0524547 -0.1237 0.9015835    
2:sexmale     -0.0811157  0.0416113 -1.9494 0.0512512 .  
2:age16-34     0.1622542  0.0469167  3.4583 0.0005435 ***
2:age35-54     0.0312484  0.0555634  0.5624 0.5738492    
2:age55-74     0.0556696  0.0836248  0.6657 0.5055987    
2:age75+       0.1057646  0.2453797  0.4310 0.6664508    
2:handright   -0.0177260  0.0539510 -0.3286 0.7424902    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Log-Likelihood: -8284.6
McFadden R^2:  0.097398 
Likelihood ratio test : chisq = 1787.9 (p.value = < 2.22e-16)

system is computationally singular error in mlogit R-package

mlogit doesn't follow the same model command-line formulas as other models.

Try

mlogit(Resp ~ 1|A+B+C+D , ml.data)

and compare to

multinom(Resp ~ A+B+C+D , dat)

in package nnet. Also, check out ?mFormula and this answer

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