broom (version 0.5.6)

tidy.multinom: 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 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.

See Also

tidy(), nnet::multinom()

Other multinom tidiers: glance.multinom()

Examples

# NOT RUN {
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)
}

# }