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 class 'multinom' tidy(x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, ...)
x |
A |
conf.int |
Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to |
conf.level |
The confidence level to use for the confidence interval
if |
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 |
... |
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
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. |
Other multinom tidiers:
glance.multinom()
if (requireNamespace("nnet", quietly = TRUE)) {
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)
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