Print summary for mixed logit model
Usage
# S3 method for class 'summary.choicer_mxl'
print(x, ...)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
print(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. )
# }