diag_data: Create a tibble with useful diagnostic measures

In curso-r/ggilberto:

Description

Uses broom::augment and some other useful measures

Usage

diag_data(model)

Arguments

model

a previously fitted model of class lm or glm

Value

A tibble of class diag_lm or diag_glm (depending on the class of the model object) with the following columns:

• .rownames: the row labels taken from the dataset

• the variables (response and predictors) used to fit the model

• .fitted: predicted value

• .se.fit: standard-error

• .resid: residuals

• .hat: h-measure

• .sigma:

• .cooksd: Cook's distance

• .std.resid: standardized residuals

• .w: weights

• .eta:

• .pearson: person residuals

• .z: z-measure

See Also

Other glm diagnostic functions: plot.diag_glm, plot.diag_lm

Examples

fit_lm <- lm(mpg ~ cyl, data = mtcars)
diag_data(fit_lm)

fit_glm <- glm(mpg ~ cyl, family = Gamma(link = "log"), data = mtcars)
diag_data(fit_glm)

curso-r/ggilberto documentation built on May 20, 2019, 2:39 p.m.