broom (version 1.0.4)

tidy.mlogit: Tidying methods for logit models

Description

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, ...)

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.

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.

See Also

tidy(), mlogit::mlogit()

Other mlogit tidiers: augment.mlogit(), glance.mlogit()

Examples

if (FALSE) { # rlang::is_installed("mlogit")

# 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)
augment(m)
glance(m)
}