R/nnet-tidiers.R
tidy.multinom.Rd
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, ...)
A multinom
object returned from nnet::multinom()
.
Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to FALSE
.
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.
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.
Other multinom tidiers:
glance.multinom()
A tibble::tibble()
with columns:
Upper bound on the confidence interval for the estimate.
Lower bound on the confidence interval for the estimate.
The estimated value of the regression term.
The two-sided p-value associated with the observed statistic.
The value of a T-statistic to use in a hypothesis that the regression term is non-zero.
The standard error of the regression term.
The name of the regression term.
The response level.
# feel free to ignore the following line—it allows {broom} to supply
# examples without requiring the model-supplying package to be installed.
if (requireNamespace("nnet", quietly = TRUE)) {
if (requireNamespace("MASS", quietly = TRUE)) {
# 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)
}
}
#>
#> 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 × 4
#> edf deviance AIC nobs
#> <dbl> <dbl> <dbl> <int>
#> 1 6 28.0 40.0 32