R: Tidying methods for multinomial logistic regression models

tidy.multinom {broom}

R Documentation

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 = TRUE, ...)

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`. Additionally, if you pass `newdata = my_tibble` to an `augment()` method that does not accept a `newdata` argument, it will use the default value for the `data` argument.

Value

tidy.multinom returns one row for each coefficient at each level of the response variable, with six columns:

`y.value`	The response level
`term`	The term in the model being estimated and tested
`estimate`	The estimated coefficient
`std.error`	The standard error from the linear model
`statistic`	Wald z-statistic
`p.value`	two-sided p-value

If conf.int = TRUE, also includes columns for conf.low and conf.high.

Examples


if (require(nnet) & require(MASS)){
  library(nnet)
  library(MASS)
  
  example(birthwt)
  bwt.mu <- multinom(low ~ ., bwt)
  tidy(bwt.mu)
  glance(bwt.mu)

  #* This model is a truly terrible model
  #* but it should show you what the output looks
  #* like in a multinomial logistic regression

  fit.gear <- multinom(gear ~ mpg + factor(am), data = mtcars)
  tidy(fit.gear)
  glance(fit.gear)
}