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 = 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.

tidy(), nnet::multinom()

Other multinom tidiers: glance.multinom()

## 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.

## Examples


# load libraries for models and data
library(nnet)
library(MASS)

example(birthwt)
#>
#> 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
#>     0.82302     -0.03723     -0.01565      1.19241      0.74068
#>   smokeTRUE      ptdTRUE       htTRUE       uiTRUE         ftv1
#>     0.75553      1.34376      1.91317      0.68020     -0.43638
#>       ftv2+
#>     0.17901
#>
#> Degrees of Freedom: 188 Total (i.e. Null);  178 Residual
#> Null Deviance:	    234.7
#> Residual Deviance: 195.5 	AIC: 217.5

bwt.mu <- multinom(low ~ ., bwt)
#> # weights:  12 (11 variable)
#> initial  value 131.004817
#> iter  10 value 98.029803
#> final  value 97.737759
#> converged

tidy(bwt.mu)
#> # A tibble: 11 × 6
#>    y.level term        estimate std.error statistic p.value
#>    <chr>   <chr>          <dbl>     <dbl>     <dbl>   <dbl>
#>  1 1       (Intercept)   0.823    1.24        0.661 0.508
#>  2 1       age          -0.0372   0.0387     -0.962 0.336
#>  3 1       lwt          -0.0157   0.00708    -2.21  0.0271
#>  4 1       raceblack     1.19     0.536       2.22  0.0261
#>  5 1       raceother     0.741    0.462       1.60  0.109
#>  6 1       smokeTRUE     0.756    0.425       1.78  0.0755
#>  7 1       ptdTRUE       1.34     0.481       2.80  0.00518
#>  8 1       htTRUE        1.91     0.721       2.65  0.00794
#>  9 1       uiTRUE        0.680    0.464       1.46  0.143
#> 10 1       ftv1         -0.436    0.479      -0.910 0.363
#> 11 1       ftv2+         0.179    0.456       0.392 0.695
glance(bwt.mu)
#> # A tibble: 1 × 4
#>     edf deviance   AIC  nobs
#>   <dbl>    <dbl> <dbl> <int>
#> 1    11     195.  217.   189

# or, for output from a multinomial logistic regression
fit.gear <- multinom(gear ~ mpg + factor(am), data = mtcars)
#> # 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
tidy(fit.gear)
#> # A tibble: 6 × 6
#>   y.level term        estimate std.error statistic  p.value
#>   <chr>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>
#> 1 4       (Intercept)  -11.2       5.32     -2.10  3.60e- 2
#> 2 4       mpg            0.525     0.268     1.96  5.02e- 2
#> 3 4       factor(am)1   11.9      66.9       0.178 8.59e- 1
#> 4 5       (Intercept)  -18.4      67.9      -0.271 7.87e- 1
#> 5 5       mpg            0.366     0.292     1.25  2.10e- 1
#> 6 5       factor(am)1   22.4       2.17     10.3   4.54e-25
glance(fit.gear)
#> # A tibble: 1 × 4
#>     edf deviance   AIC  nobs
#>   <dbl>    <dbl> <dbl> <int>
#> 1     6     28.0  40.0    32