| multinom_tidiers {broom} | R Documentation |
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 = TRUE, ...) ## S3 method for class 'multinom' glance(x, ...)
x |
A model object of class |
conf.int |
whether to include a confidence interval |
conf.level |
confidence level of the interval, used only if
|
exponentiate |
whether to exponentiate the coefficient estimates and confidence intervals (typical for multinomial logistic regression) |
... |
extra arguments, not used |
If conf.int=TRUE, the confidence interval is computed with
the confint function.
While tidy and glance are supported for "multinom" objects,
augment is not.
All tidying methods return a data.frame without rownames.
The structure depends on the method chosen.
tidy.multinom returns one row for each coefficient at each
level of the response variable, with six columns:
y.value |
The response level |
term |
The term in the model being estimated and tested |
estimate |
The estimated coefficient |
std.error |
The standard error from the linear model |
statistic |
Wald z-statistic |
p.value |
two-sided p-value |
If conf.int=TRUE, it also includes columns for conf.low and
conf.high, computed with confint.
glance.multinom returns a
glance.multinom returns a one-row data.frame with the columns
edf |
The effective degrees of freedom |
deviance |
deviance |
AIC |
the Akaike Information Criterion |
if (require(nnet) & require(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)
}