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, conf.int = FALSE, conf.level = 0.95, ...)A lmrob object returned from robustbase::lmrob().
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. 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 robustbase tidiers:
augment.glmrob(),
augment.lmrob(),
glance.lmrob(),
tidy.glmrob()
if (requireNamespace("robustbase", quietly = TRUE)) {
# load libraries for models and data
library(robustbase)
data(coleman)
set.seed(0)
m <- lmrob(Y ~ ., data = coleman)
tidy(m)
augment(m)
glance(m)
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)
}
#> # A tibble: 24 × 4
#> `cbind(success, total - success)`[,"success"] [,""] logdose block .fitted
#> <int> <int> <dbl> <fct> <dbl>
#> 1 10 25 1.52 B1 -0.726
#> 2 16 26 1.64 B1 -0.972
#> 3 8 42 1.76 B1 -1.22
#> 4 6 36 1.88 B1 -1.46
#> 5 9 26 2 B1 -1.71
#> 6 9 33 2.12 B1 -1.96
#> 7 1 31 2.24 B1 -2.20
#> 8 2 26 2.36 B1 -2.45
#> 9 17 21 1.52 B2 -0.491
#> 10 10 30 1.64 B2 -0.737
#> # … with 14 more rows