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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, ...)
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.
tidy.multinom
returns one row for each coefficient at each
level of the response variable, with six columns:
The response level
The term in the model being estimated and tested
The estimated coefficient
The standard error from the linear model
Wald z-statistic
two-sided p-value
If conf.int = TRUE, also includes columns for conf.low and conf.high.
Other multinom tidiers: glance.multinom
# NOT RUN {
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)
}
# }
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