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These methods tidy the coefficients of mnl and nl models generated
by the functions of the mlogit package.
# S3 method for mlogit
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)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.
an object returned from mlogit::mlogit().
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.
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. Two exceptions here are:
tidy() methods will warn when supplied an exponentiate argument if
it will be ignored.
augment() methods will warn when supplied a newdata argument if it
will be ignored.
tidy(), mlogit::mlogit()
Other mlogit tidiers:
augment.mlogit(),
glance.mlogit()
if (FALSE) { # rlang::is_installed("mlogit")
# load libraries for models and data
library(mlogit)
data("Fishing", package = "mlogit")
Fish <- dfidx(Fishing, varying = 2:9, shape = "wide", choice = "mode")
# fit model
m <- mlogit(mode ~ price + catch | income, data = Fish)
# summarize model fit with tidiers
tidy(m)
augment(m)
glance(m)
}
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