These methods tidy the coefficients of mnl and nl models generated
by the functions of the mlogit
package.
Usage
# S3 method for class 'mlogit'
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)
Arguments
- x
an object returned from
mlogit::mlogit()
.- conf.int
Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to
FALSE
.- conf.level
The confidence level to use for the confidence interval if
conf.int = TRUE
. Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.- ...
Additional arguments. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in
...
, where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you passconf.lvel = 0.9
, all computation will proceed usingconf.level = 0.95
. Two exceptions here are:
See also
Other mlogit tidiers:
augment.mlogit()
,
glance.mlogit()
Value
A tibble::tibble()
with columns:
- conf.high
Upper bound on the confidence interval for the estimate.
- conf.low
Lower bound on the confidence interval for the estimate.
- estimate
The estimated value of the regression term.
- p.value
The two-sided p-value associated with the observed statistic.
- statistic
The value of a T-statistic to use in a hypothesis that the regression term is non-zero.
- std.error
The standard error of the regression term.
- term
The name of the regression term.
Examples
# load libraries for models and data
library(mlogit)
data("Fishing", package = "mlogit")
Fish <- dfidx(Fishing, varying = 2:9, shape = "wide", choice = "mode")
# fit model
m <- mlogit(mode ~ price + catch | income, data = Fish)
# summarize model fit with tidiers
tidy(m)
#> # A tibble: 8 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept):boat 0.527 0.223 2.37 1.79e- 2
#> 2 (Intercept):charter 1.69 0.224 7.56 3.95e-14
#> 3 (Intercept):pier 0.778 0.220 3.53 4.18e- 4
#> 4 price -0.0251 0.00173 -14.5 0
#> 5 catch 0.358 0.110 3.26 1.12e- 3
#> 6 income:boat 0.0000894 0.0000501 1.79 7.40e- 2
#> 7 income:charter -0.0000333 0.0000503 -0.661 5.08e- 1
#> 8 income:pier -0.000128 0.0000506 -2.52 1.18e- 2
augment(m)
#> # A tibble: 4,728 × 9
#> id alternative chosen price catch income .probability .fitted
#> <int> <fct> <lgl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 beach FALSE 158. 0.0678 7083. 0.125 -3.94
#> 2 1 boat FALSE 158. 0.260 7083. 0.427 -2.71
#> 3 1 charter TRUE 183. 0.539 7083. 0.339 -2.94
#> 4 1 pier FALSE 158. 0.0503 7083. 0.109 -4.07
#> 5 2 beach FALSE 15.1 0.105 1250. 0.116 -0.342
#> 6 2 boat FALSE 10.5 0.157 1250. 0.251 0.431
#> 7 2 charter TRUE 34.5 0.467 1250. 0.423 0.952
#> 8 2 pier FALSE 15.1 0.0451 1250. 0.210 0.255
#> 9 3 beach FALSE 162. 0.533 3750. 0.00689 -3.87
#> 10 3 boat TRUE 24.3 0.241 3750. 0.465 0.338
#> # ℹ 4,718 more rows
#> # ℹ 1 more variable: .resid <dbl>
glance(m)
#> # A tibble: 1 × 6
#> logLik rho2 rho20 AIC BIC nobs
#> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 -1215. 0.189 0.258 2446. NA 1182