This method wraps tidy.lm().

# S3 method for lmRob
tidy(x, ...)

Arguments

x

A lmRob object returned from robust::lmRob().

...

Arguments passed on to tidy.lm

x

An lm object created by stats::lm().

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.

quick

Logical indiciating if the only the term and estimate columns should be returned. Often useful to avoid time consuming covariance and standard error calculations. Defaults to FALSE.

exponentiate

Logical indicating whether or not to exponentiate the the coefficient estimates. This is typical for logistic and multinomial regressions, but a bad idea if there is no log or logit link. Defaults to FALSE.

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 6 #> .rownames mpg wt .fitted .se.fit .resid #> * <chr> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Mazda RX4 21 2.62 22.7 1.12 -1.68 #> 2 Mazda RX4 Wag 21 2.88 21.4 1.00 -0.431 #> 3 Datsun 710 22.8 2.32 24.2 1.32 -1.36 #> 4 Hornet 4 Drive 21.4 3.22 19.8 0.957 1.64 #> 5 Hornet Sportabout 18.7 3.44 18.7 1.00 0.0445 #> 6 Valiant 18.1 3.46 18.6 1.01 -0.457 #> 7 Duster 360 14.3 3.57 18.0 1.06 -3.72 #> 8 Merc 240D 24.4 3.19 19.9 0.955 4.52 #> 9 Merc 230 22.8 3.15 20.1 0.955 2.72 #> 10 Merc 280 19.2 3.44 18.7 1.00 0.545 #> # ... with 22 more rows
#> # A tibble: 1 x 4 #> r.squared deviance sigma df.residual #> <dbl> <dbl> <dbl> <int> #> 1 0.567 136. 2.95 30
gm <- glmRob(am ~ wt, data = mtcars, family = "binomial") glance(gm)
#> # A tibble: 1 x 3 #> deviance null.deviance df.residual #> <dbl> <dbl> <int> #> 1 19.2 44.4 30