tidy.multinom: Tidying methods for multinomial logistic regression models

In broom: Convert Statistical Objects into Tidy Tibbles

Tidying methods for multinomial logistic regression models

Description

These methods tidy the coefficients of multinomial logistic regression models generated by multinom of the nnet package.

Usage

## S3 method for class 'multinom'
tidy(x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, ...)

Arguments

x	A multinom object returned from nnet::multinom().
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.
exponentiate	Logical indicating whether or not to exponentiate the the coefficient estimates. This is typical for logistic and multinomial regressions, but a bad idea if there is no log or logit link. Defaults to FALSE.
...	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.
y.value	The response level.

See Also

tidy(), nnet::multinom()

Other multinom tidiers: glance.multinom()

Examples

# load libraries for models and data
library(nnet)
library(MASS)

example(birthwt)

bwt.mu <- multinom(low ~ ., bwt)

tidy(bwt.mu)
glance(bwt.mu)

# or, for output from a multinomial logistic regression
fit.gear <- multinom(gear ~ mpg + factor(am), data = mtcars)
tidy(fit.gear)
glance(fit.gear)

broom documentation built on Sept. 30, 2024, 9:29 a.m.