Dear All,

I have a question concerning the mlogit function.

In my experimental design, I collect 80 trials for each participant. The subjects are asked to provide the answer by choosing among 4 different alternatives.

Now I would like to model the choices of my participants as a function of nine continuous predictors, however I can’t understand how the data frame must be re-shaped. None of the examples in the vignette and in other articles seem to fit my dataset.

Does anybody know what would be the proper way to arrange the dataset?

Many thanks in advance
Giovanni

Here is an extract of the dataset:

Subject     Trial     Choice        X1       X2       X3       X4       X5         X6       X7       X8       X9

Subj1              1          1              0.00       0.62       0.04       -0.84      0.12       -0.06      0.58       -0.05      0.04
Subj1              2          2             0.00       0.62       0.04       -0.84      0.12       -0.06      0.58       -0.05      0.04
Subj1              3          4              0.02       -0.01      0.02       -0.23      0.01       0.30       0.12       -0.01      0.01
Subj1              4          3              0.01       -0.02      0.03       -0.24      0.03       0.37       0.12       -0.02      0.03
Subj1              5          3              0.01       -0.02      0.03       -0.24      0.03       0.37       0.12       -0.02      0.03
…                    …        …           …        …        …        …        …        …        …        …        …
Subj1              80        3              0.01       -0.02      0.03       -0.24      0.03       0.37       0.12       -0.02      0.03
Subj2              1          3              0.01       -0.04      0.03       -0.22      0.03       0.33       0.10       -0.02      0.03
Subj2              2          2              0.02       0.01       0.02       -0.12      0.01       0.26       0.07       -0.01      0.01
Subj2              3          1              0.03       -0.01      0.02       -0.12      0.02       0.23       0.07       -0.02      0.02
Subj2              4          2              -0.02      0.00       0.00       -0.19      -0.01      0.35       0.06       -0.04      0.01
Subj2              5          2              0.01       -0.01      -0.01      -0.10      0.00       0.27       0.05       -0.01      0.00
…                    …        …           …        …        …        …        …        …        …        …        …
Subj2              80        2              0.01       -0.01      -0.01      -0.10      0.00       0.27       0.05       -0.01      0.00
Subj3              1          4              0.02       0.31       0.01       -0.20      0.01       0.04       0.12       -0.01      0.01
…                    …        …           …        …        …        …        …        …        …        …        …

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