Tidying methods for logit models

These methods tidy the coefficients of mnl and nl models generated by the functions of the mlogit package.

Usage

# S3 method for 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 pass conf.lvel = 0.9, all computation will proceed using conf.level = 0.95. Two exceptions here are:

tidy() methods will warn when supplied an exponentiate argument if it will be ignored.
augment() methods will warn when supplied a newdata argument if it will be ignored.

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

Usage

Arguments

See also

Value

Examples