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 cross 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, ...)

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. 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

Examples

library(robust) m <- lmRob(mpg ~ wt, data = mtcars) tidy(m)
#> # A tibble: 2 x 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
#> # A tibble: 32 x 5 #> .rownames mpg wt .fitted .resid #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 Mazda RX4 21 2.62 22.7 1.68 #> 2 Mazda RX4 Wag 21 2.88 21.4 0.431 #> 3 Datsun 710 22.8 2.32 24.2 1.36 #> 4 Hornet 4 Drive 21.4 3.22 19.8 -1.64 #> 5 Hornet Sportabout 18.7 3.44 18.7 -0.0445 #> 6 Valiant 18.1 3.46 18.6 0.457 #> 7 Duster 360 14.3 3.57 18.0 3.72 #> 8 Merc 240D 24.4 3.19 19.9 -4.52 #> 9 Merc 230 22.8 3.15 20.1 -2.72 #> 10 Merc 280 19.2 3.44 18.7 -0.545 #> # … with 22 more rows
#> # A tibble: 1 x 5 #> r.squared deviance sigma df.residual nobs #> <dbl> <dbl> <dbl> <int> <int> #> 1 0.567 136. 2.95 30 32