R: Tidying methods for multinomial logistic regression models

multinom_tidiers {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, ...)

    ## S3 method for class 'multinom'
    glance(x, ...)

### Arguments

`x` | A model object of class `multinom`
`conf.int` | whether to include a confidence interval
`conf.level` | confidence level of the interval, used only if `conf.int=TRUE`
`exponentiate` | whether to exponentiate the coefficient estimates and confidence intervals (typical for multinomial logistic regression)
`...` | extra arguments, not used

### Details

If `conf.int=TRUE`, the confidence interval is computed with the `confint` function.

While `tidy` and `glance` are supported for "multinom" objects, `augment` is not.

### Value

All tidying methods return a `data.frame` without rownames. The structure depends on the method chosen.

`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`, it also includes columns for `conf.low` and `conf.high`, computed with `confint`.

`glance.multinom` returns a

`glance.multinom` returns a one-row data.frame with the columns

`edf` | The effective degrees of freedom
`deviance` | deviance
`AIC` | the Akaike Information Criterion

### Examples

    if (require(nnet) & require(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)
    }

[Package broom version 0.4.4 Index]