Tidying methods for multinomial logistic regression models

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

# 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

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)
}
#> Loading required package: nnet
#> 
#> brthwt> bwt <- with(birthwt, {
#> brthwt+ race <- factor(race, labels = c("white", "black", "other"))
#> brthwt+ ptd <- factor(ptl > 0)
#> brthwt+ ftv <- factor(ftv)
#> brthwt+ levels(ftv)[-(1:2)] <- "2+"
#> brthwt+ data.frame(low = factor(low), age, lwt, race, smoke = (smoke > 0),
#> brthwt+            ptd, ht = (ht > 0), ui = (ui > 0), ftv)
#> brthwt+ })
#> 
#> brthwt> options(contrasts = c("contr.treatment", "contr.poly"))
#> 
#> brthwt> glm(low ~ ., binomial, bwt)
#> 
#> Call:  glm(formula = low ~ ., family = binomial, data = bwt)
#> 
#> Coefficients:
#> (Intercept)          age          lwt    raceblack    raceother    smokeTRUE  
#>     0.82302     -0.03723     -0.01565      1.19241      0.74068      0.75553  
#>     ptdTRUE       htTRUE       uiTRUE         ftv1        ftv2+  
#>     1.34376      1.91317      0.68020     -0.43638      0.17901  
#> 
#> Degrees of Freedom: 188 Total (i.e. Null);  178 Residual
#> Null Deviance:	    234.7 
#> Residual Deviance: 195.5 	AIC: 217.5
#> # weights:  12 (11 variable)
#> initial  value 131.004817 
#> iter  10 value 98.029803
#> final  value 97.737759 
#> converged
#> # weights:  12 (6 variable)
#> initial  value 35.155593 
#> iter  10 value 14.156582
#> iter  20 value 14.031881
#> iter  30 value 14.025659
#> iter  40 value 14.021414
#> iter  50 value 14.019824
#> iter  60 value 14.019278
#> iter  70 value 14.018601
#> iter  80 value 14.018282
#> iter  80 value 14.018282
#> iter  90 value 14.017126
#> final  value 14.015374 
#> converged
#> # A tibble: 1 x 3
#>     edf deviance   AIC
#>   <dbl>    <dbl> <dbl>
#> 1     6     28.0  40.0