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, ...)

Arguments

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. 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

robust::lmRob()

Other robust tidiers: augment.lmRob(), glance.glmRob(), glance.lmRob(), tidy.glmRob()

Examples

# load modeling library
library(robust)

# fit model
m <- lmRob(mpg ~ wt, data = mtcars)

# summarize model fit with tidiers
tidy(m)
#> # A tibble: 2 × 5
#>   term        estimate std.error statistic  p.value
#>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>
#> 1 (Intercept)    35.6       3.58      9.93 5.37e-11
#> 2 wt             -4.91      1.09     -4.49 9.67e- 5
augment(m)
#> # A tibble: 32 × 4
#>    .rownames           mpg    wt .fitted
#>    <chr>             <dbl> <dbl>   <dbl>
#>  1 Mazda RX4          21    2.62    22.7
#>  2 Mazda RX4 Wag      21    2.88    21.4
#>  3 Datsun 710         22.8  2.32    24.2
#>  4 Hornet 4 Drive     21.4  3.22    19.8
#>  5 Hornet Sportabout  18.7  3.44    18.7
#>  6 Valiant            18.1  3.46    18.6
#>  7 Duster 360         14.3  3.57    18.0
#>  8 Merc 240D          24.4  3.19    19.9
#>  9 Merc 230           22.8  3.15    20.1
#> 10 Merc 280           19.2  3.44    18.7
#> # ℹ 22 more rows
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