Glance accepts a model object and returns a tibble::tibble() with exactly one row of model summaries. The summaries are typically goodness of fit measures, p-values for hypothesis tests on residuals, or model convergence information.

Glance never returns information from the original call to the modelling function. This includes the name of the modelling function or any arguments passed to the modelling function.

Glance does not calculate summary measures. Rather, it farms out these computations to appropriate methods and gathers the results together. Sometimes a goodness of fit measure will be undefined. In these cases the measure will be reported as NA.

# S3 method for glmRob
glance(x, ...)

Arguments

x

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

...

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.

See also

Value

A tibble::tibble() with exactly one row and columns:

deviance

Deviance of the model.

df.residual

Residual degrees of freedom for the model.

null.deviance

Deviance of the null model.

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