broom (version 0.7.5)

tidy.lmrob: Tidy a(n) lmrob object

Description

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.

Usage

# S3 method for lmrob
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

Arguments

x

A lmrob object returned from robustbase::lmrob().

conf.int

Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to FALSE.

conf.level

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.level = 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.

Details

For tidiers for robust models from the MASS package see tidy.rlm().

See Also

robustbase::lmrob()

Other robustbase tidiers: augment.glmrob(), augment.lmrob(), glance.lmrob(), tidy.glmrob()

Examples

# NOT RUN {
library(robustbase)
# From the robustbase::lmrob examples:
data(coleman)
set.seed(0)

m <- robustbase::lmrob(Y ~ ., data = coleman)
tidy(m)
augment(m)
glance(m)

# From the robustbase::glmrob examples:
data(carrots)
Rfit <- glmrob(cbind(success, total - success) ~ logdose + block,
  family = binomial, data = carrots, method = "Mqle",
  control = glmrobMqle.control(tcc = 1.2)
)
tidy(Rfit)
augment(Rfit)
# }