Diversion ratios for nested logit model
Usage
# S3 method for class 'choicer_nl'
diversion_ratios(object, ...)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[, nest := rep(c(1L, 1L, 2L, 2L), N)]
#> id alt nest
#> <int> <int> <int>
#> 1: 1 1 1
#> 2: 1 2 1
#> 3: 1 3 2
#> 4: 1 4 2
#> 5: 2 1 1
#> ---
#> 196: 49 4 2
#> 197: 50 1 1
#> 198: 50 2 1
#> 199: 50 3 2
#> 200: 50 4 2
dt[, `:=`(x1 = rnorm(.N), x2 = rnorm(.N))]
#> id alt nest x1 x2
#> <int> <int> <int> <num> <num>
#> 1: 1 1 1 1.3709584 -2.0009292
#> 2: 1 2 1 -0.5646982 0.3337772
#> 3: 1 3 2 0.3631284 1.1713251
#> 4: 1 4 2 0.6328626 2.0595392
#> 5: 2 1 1 0.4042683 -1.3768616
#> ---
#> 196: 49 4 2 1.0857749 1.0965134
#> 197: 50 1 1 0.4037749 0.4420131
#> 198: 50 2 1 0.5864875 0.2410163
#> 199: 50 3 2 1.8152284 -0.2556077
#> 200: 50 4 2 0.1288214 0.9310329
dt[, choice := 0L]
#> id alt nest x1 x2 choice
#> <int> <int> <int> <num> <num> <int>
#> 1: 1 1 1 1.3709584 -2.0009292 0
#> 2: 1 2 1 -0.5646982 0.3337772 0
#> 3: 1 3 2 0.3631284 1.1713251 0
#> 4: 1 4 2 0.6328626 2.0595392 0
#> 5: 2 1 1 0.4042683 -1.3768616 0
#> ---
#> 196: 49 4 2 1.0857749 1.0965134 0
#> 197: 50 1 1 0.4037749 0.4420131 0
#> 198: 50 2 1 0.5864875 0.2410163 0
#> 199: 50 3 2 1.8152284 -0.2556077 0
#> 200: 50 4 2 0.1288214 0.9310329 0
dt[, choice := sample(c(1L, rep(0L, J - 1))), by = id]
#> id alt nest x1 x2 choice
#> <int> <int> <int> <num> <num> <int>
#> 1: 1 1 1 1.3709584 -2.0009292 0
#> 2: 1 2 1 -0.5646982 0.3337772 0
#> 3: 1 3 2 0.3631284 1.1713251 0
#> 4: 1 4 2 0.6328626 2.0595392 1
#> 5: 2 1 1 0.4042683 -1.3768616 0
#> ---
#> 196: 49 4 2 1.0857749 1.0965134 1
#> 197: 50 1 1 0.4037749 0.4420131 0
#> 198: 50 2 1 0.5864875 0.2410163 0
#> 199: 50 3 2 1.8152284 -0.2556077 0
#> 200: 50 4 2 0.1288214 0.9310329 1
fit <- run_nestlogit(dt, "id", "alt", "choice", c("x1", "x2"), "nest")
#> Optimization run time 0h:0m:0.01s
diversion_ratios(fit)
#> 1 2 3 4
#> 1 0.0000000 -0.8981740 0.45017345 0.4192658
#> 2 -1.0621318 0.0000000 0.49007051 0.4562837
#> 3 1.4249183 1.3117490 0.00000000 0.1244504
#> 4 0.6372135 0.5864251 0.05975604 0.0000000
# }