# tidy.multinom: Tidying methods for multinomial logistic regression models In broom: Convert Statistical Objects into Tidy Tibbles

 tidy.multinom 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 = 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.

`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 Aug. 30, 2022, 1:07 a.m.