Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.
# S3 method for fitdistr
tidy(x, ...)A tibble::tibble() with columns:
The estimated value of the regression term.
The standard error of the regression term.
The name of the regression term.
A fitdistr object returned by MASS::fitdistr().
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(), MASS::fitdistr()
Other fitdistr tidiers:
glance.fitdistr()
# load libraries for models and data
library(MASS)
# generate data
set.seed(2015)
x <- rnorm(100, 5, 2)
# fit models
fit <- fitdistr(x, dnorm, list(mean = 3, sd = 1))
# summarize model fit with tidiers
tidy(fit)
glance(fit)
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