tidy.multinom {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, ...)
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 |
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
, also includes columns for conf.low
and
conf.high
.
Other multinom tidiers: glance.multinom
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) }