Tidy a(n) lmrob object

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.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.

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

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 × 5
#>    cbind(success, total - success…¹ [,""] logdose block .fitted .resid[,1]
#>                               <int> <int>   <dbl> <fct>   <dbl>      <dbl>
#>  1                               10    25    1.52 B1     -0.726      10.7 
#>  2                               16    26    1.64 B1     -0.972      17.0 
#>  3                                8    42    1.76 B1     -1.22        9.22
#>  4                                6    36    1.88 B1     -1.46        7.46
#>  5                                9    26    2    B1     -1.71       10.7 
#>  6                                9    33    2.12 B1     -1.96       11.0 
#>  7                                1    31    2.24 B1     -2.20        3.20
#>  8                                2    26    2.36 B1     -2.45        4.45
#>  9                               17    21    1.52 B2     -0.491      17.5 
#> 10                               10    30    1.64 B2     -0.737      10.7 
#> # ℹ 14 more rows
#> # ℹ abbreviated name: ¹​`cbind(success, total - success)`[,"success"]
#> # ℹ 1 more variable: .resid[2] <dbl>