Print summary for nested logit model
Usage
# S3 method for class 'summary.choicer_nl'
print(x, ...)Examples
# \donttest{
library(data.table)
set.seed(42)
N <- 50; J <- 4
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 -2.0009292
#> 2: 1 2 -0.5646982 0.3337772
#> 3: 1 3 0.3631284 1.1713251
#> 4: 1 4 0.6328626 2.0595392
#> 5: 2 1 0.4042683 -1.3768616
#> ---
#> 196: 49 4 1.0857749 1.0965134
#> 197: 50 1 0.4037749 0.4420131
#> 198: 50 2 0.5864875 0.2410163
#> 199: 50 3 1.8152284 -0.2556077
#> 200: 50 4 0.1288214 0.9310329
dt[, nest := ifelse(alt <= 2, "A", "B")]
#> id alt x1 x2 nest
#> <int> <int> <num> <num> <char>
#> 1: 1 1 1.3709584 -2.0009292 A
#> 2: 1 2 -0.5646982 0.3337772 A
#> 3: 1 3 0.3631284 1.1713251 B
#> 4: 1 4 0.6328626 2.0595392 B
#> 5: 2 1 0.4042683 -1.3768616 A
#> ---
#> 196: 49 4 1.0857749 1.0965134 B
#> 197: 50 1 0.4037749 0.4420131 A
#> 198: 50 2 0.5864875 0.2410163 A
#> 199: 50 3 1.8152284 -0.2556077 B
#> 200: 50 4 0.1288214 0.9310329 B
dt[, choice := 0L]
#> id alt x1 x2 nest choice
#> <int> <int> <num> <num> <char> <int>
#> 1: 1 1 1.3709584 -2.0009292 A 0
#> 2: 1 2 -0.5646982 0.3337772 A 0
#> 3: 1 3 0.3631284 1.1713251 B 0
#> 4: 1 4 0.6328626 2.0595392 B 0
#> 5: 2 1 0.4042683 -1.3768616 A 0
#> ---
#> 196: 49 4 1.0857749 1.0965134 B 0
#> 197: 50 1 0.4037749 0.4420131 A 0
#> 198: 50 2 0.5864875 0.2410163 A 0
#> 199: 50 3 1.8152284 -0.2556077 B 0
#> 200: 50 4 0.1288214 0.9310329 B 0
dt[, choice := sample(c(1L, rep(0L, J - 1))), by = id]
#> id alt x1 x2 nest choice
#> <int> <int> <num> <num> <char> <int>
#> 1: 1 1 1.3709584 -2.0009292 A 0
#> 2: 1 2 -0.5646982 0.3337772 A 0
#> 3: 1 3 0.3631284 1.1713251 B 0
#> 4: 1 4 0.6328626 2.0595392 B 1
#> 5: 2 1 0.4042683 -1.3768616 A 0
#> ---
#> 196: 49 4 1.0857749 1.0965134 B 1
#> 197: 50 1 0.4037749 0.4420131 A 0
#> 198: 50 2 0.5864875 0.2410163 A 0
#> 199: 50 3 1.8152284 -0.2556077 B 0
#> 200: 50 4 0.1288214 0.9310329 B 1
fit <- run_nestlogit(
data = dt, id_col = "id", alt_col = "alt", choice_col = "choice",
covariate_cols = c("x1", "x2"), nest_col = "nest"
)
#> Optimization run time 0h:0m:0s
print(summary(fit))
#> Nested Logit (NL) model
#>
#> Parameter Estimate Std.Error z-value Pr(>|z|)
#> x1 0.368065 0.337997 1.0890 2.76e-01
#> x2 0.658149 0.371606 1.7711 7.65e-02
#> Lambda_1 158.730739 9738.672884 0.0163 9.87e-01
#> Lambda_2 2.096862 1.990404 1.0535 2.92e-01
#> ASC_2 13.362917 829.498427 0.0161 9.87e-01
#> ASC_3 116.246669 7172.645868 0.0162 9.87e-01
#> ASC_4 114.565365 7172.746411 0.0160 9.87e-01
#> ---
#> Signif. codes: '***' 0.001 '**' 0.01 '*' 0.05
#>
#> Std. Errors: Analytical Hessian
#> Log-likelihood: -63.8964
#> AIC: 141.793 | BIC: 155.177
#> McFadden R2: 0.078 (adj: -0.023) | Hit rate: 0.360
#> N: 50 | Parameters: 7
#> Optimization time: 0 s
#> Convergence: 3 ( NLOPT_FTOL_REACHED: Optimization stopped because ftol_rel or ftol_abs (above) was reached. )
# }