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 = 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. 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.

See also

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

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 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
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 x 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 x 4 #> edf deviance AIC nobs #> <dbl> <dbl> <dbl> <int> #> 1 11 195. 217. 189
#* 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)
#> # 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 x 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 x 4 #> edf deviance AIC nobs #> <dbl> <dbl> <dbl> <int> #> 1 6 28.0 40.0 32