Summary for mixed logit model

Computes coefficient summary with delta-method transformation for variance parameters (Cholesky to covariance scale) and log-normal mean parameters. Triggers lazy Hessian computation if standard errors have not been computed yet.

Usage

# S3 method for class 'choicer_mxl'
summary(object, gof = TRUE, ...)

Arguments

object

A choicer_mxl object.

gof

Logical; compute goodness-of-fit measures (McFadden R-squared, hit rate) for the summary footer. Involves an in-sample prediction pass (for mixed logit, a full simulation over draws); set to FALSE to skip.

...

Additional arguments (ignored).

Value

A summary.choicer_mxl object (includes a gof element with goodness-of-fit measures from gof; its fields are NA when the model was fitted with keep_data = FALSE).

Examples

# \donttest{
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), w1 = rnorm(.N))]
#>         id   alt         x1          w1
#>      <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          w1 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          w1 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
fit <- run_mxlogit(
  data = dt, id_col = "id", alt_col = "alt", choice_col = "choice",
  covariate_cols = "x1", random_var_cols = "w1", S = 50L
)
#> Optimization run time 0h:0m:0.01s
summary(fit)
#> Mixed Logit (MXL) model
#> 
#> Parameter    Estimate  Std.Error  z-value  Pr(>|z|)  
#> x1          -0.094081   0.227507  -0.4135  6.79e-01  
#> Sigma_11     2.247294   3.037056   0.7400  4.59e-01  
#> ASC_2        0.283261   0.418283   0.6772  4.98e-01  
#> ASC_3       -0.679845   0.565037  -1.2032  2.29e-01  
#> ---
#> Signif. codes:  '***' 0.001 '**' 0.01 '*' 0.05
#> 
#> Random coefficient covariance (Sigma):
#>          w1
#> w1 2.247294
#> 
#> Std. Errors: Analytical Hessian 
#> Log-likelihood: -52.191 
#> AIC: 112.382  | BIC: 120.03 
#> McFadden R2: 0.050 (adj: -0.023) | Hit rate: 0.420 
#> N: 50  | Parameters: 4 
#> Optimization time: 0.01 s
#> Convergence: 1 ( NLOPT_SUCCESS: Generic success return value. )
# }