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 lmRob
tidy(x, ...)A lmRob object returned from robust::lmRob().
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.
For tidiers for robust models from the MASS package see
tidy.rlm().
Other robust tidiers:
augment.lmRob(),
glance.glmRob(),
glance.lmRob(),
tidy.glmRob()
# feel free to ignore the following line—it allows {broom} to supply
# examples without requiring the model-supplying package to be installed.
if (requireNamespace("robust", quietly = TRUE)) {
# load modeling library
library(robust)
# fit model
m <- lmRob(mpg ~ wt, data = mtcars)
# summarize model fit with tidiers
tidy(m)
augment(m)
glance(m)
}
#> # A tibble: 1 × 5
#> r.squared deviance sigma df.residual nobs
#> <dbl> <dbl> <dbl> <int> <int>
#> 1 0.567 136. 2.95 30 32