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Prepare inputs for multinomial logit estimation

Source: R/mnlogit_utils.R
prepare_mnl_data.Rd

Prepares and validates inputs for multinomial logit estimation routine.

Usage

prepare_mnl_data(
  data,
  id_col,
  alt_col,
  choice_col,
  covariate_cols,
  weights = NULL,
  outside_opt_label = NULL,
  include_outside_option = FALSE,
  weights_col = NULL,
  cluster_col = NULL
)

Arguments

data

Data frame containing choice data.

id_col

Name of the column identifying choice situations (individuals).

alt_col

Name of the column identifying alternatives.

choice_col

Name of the column indicating chosen alternative (1 = chosen, 0 = not chosen).

covariate_cols

Vector of names of columns to be used as covariates.

weights

Optional vector of weights for each choice situation. If NULL, equal weights are used. All weights must be finite and strictly positive.

outside_opt_label

Label for the outside option (if any). If NULL, no outside option is assumed.

include_outside_option

Logical indicating whether to include an outside option in the model.

weights_col

Optional name of a column in data holding per-row weights. The column must be constant within each id_col (one weight per choice situation) and is collapsed accordingly. Mutually exclusive with weights. All weights must be finite and strictly positive.

cluster_col

Optional name of a column in data holding cluster labels for cluster-robust standard errors (e.g. a person id when the same decision maker contributes several choice situations). Must be constant within each id_col. Collapsed to one label per choice situation and returned as cluster; used by se_method = "cluster" and vcov(fit, type = "cluster").

Value

A list containing:

  • X: Design matrix (sum(M) x K).

  • alt_idx: Integer vector of alternative indices.

  • choice_idx: Integer vector of chosen alternative indices.

  • M: Integer vector with number of alternatives per choice situation.

  • N: Number of choice situations.

  • weights: Vector of weights.

  • cluster: Vector of cluster labels (or NULL).

  • situation_ids: Choice-situation ids in prepared (sorted) order.

  • include_outside_option: Logical flag.

  • alt_mapping: Data.table mapping alternatives to summary statistics.

  • dropped_cols: Names of columns dropped due to collinearity, if any.

Examples

library(data.table)
set.seed(42)
N <- 50; J <- 3
dt <- data.table(id = rep(1:N, each = J), alt = rep(1:J, N))
dt[, `:=`(x1 = rnorm(.N), x2 = rnorm(.N))]
#>         id   alt         x1          x2
#>      <int> <int>      <num>       <num>
#>   1:     1     1  1.3709584 -0.04069848
#>   2:     1     2 -0.5646982 -1.55154482
#>   3:     1     3  0.3631284  1.16716955
#>   4:     2     1  0.6328626 -0.27364570
#>   5:     2     2  0.4042683 -0.46784532
#>  ---                                   
#> 146:    49     2  1.1133860 -0.47733551
#> 147:    49     3 -0.4809928 -0.16626149
#> 148:    50     1 -0.4331690  0.86256338
#> 149:    50     2  0.6968626  0.09734049
#> 150:    50     3 -1.0563684 -1.62561674
dt[, choice := 0L]
#>         id   alt         x1          x2 choice
#>      <int> <int>      <num>       <num>  <int>
#>   1:     1     1  1.3709584 -0.04069848      0
#>   2:     1     2 -0.5646982 -1.55154482      0
#>   3:     1     3  0.3631284  1.16716955      0
#>   4:     2     1  0.6328626 -0.27364570      0
#>   5:     2     2  0.4042683 -0.46784532      0
#>  ---                                          
#> 146:    49     2  1.1133860 -0.47733551      0
#> 147:    49     3 -0.4809928 -0.16626149      0
#> 148:    50     1 -0.4331690  0.86256338      0
#> 149:    50     2  0.6968626  0.09734049      0
#> 150:    50     3 -1.0563684 -1.62561674      0
dt[, choice := sample(c(1L, rep(0L, J - 1))), by = id]
#>         id   alt         x1          x2 choice
#>      <int> <int>      <num>       <num>  <int>
#>   1:     1     1  1.3709584 -0.04069848      0
#>   2:     1     2 -0.5646982 -1.55154482      0
#>   3:     1     3  0.3631284  1.16716955      1
#>   4:     2     1  0.6328626 -0.27364570      0
#>   5:     2     2  0.4042683 -0.46784532      0
#>  ---                                          
#> 146:    49     2  1.1133860 -0.47733551      0
#> 147:    49     3 -0.4809928 -0.16626149      0
#> 148:    50     1 -0.4331690  0.86256338      1
#> 149:    50     2  0.6968626  0.09734049      0
#> 150:    50     3 -1.0563684 -1.62561674      0
input <- prepare_mnl_data(dt, "id", "alt", "choice", c("x1", "x2"))
str(input$X)
#>  num [1:150, 1:2] 1.371 -0.565 0.363 0.633 0.404 ...
#>  - attr(*, "dimnames")=List of 2
#>   ..$ : NULL
#>   ..$ : chr [1:2] "x1" "x2"
input$alt_mapping
#> Key: <alt_int, alt>
#>    alt_int   alt N_OBS N_CHOICES TAKE_RATE MKT_SHARE
#>      <int> <int> <int>     <int>     <num>     <num>
#> 1:       1     1    50        17      0.34      0.34
#> 2:       2     2    50        21      0.42      0.42
#> 3:       3     3    50        12      0.24      0.24