iNZightRegression (version 1.3.5)

inzplot: inzplot method

Description

inzplot method

Diagnostic Plots for Regression Models

Usage

# S3 method for glm
inzplot(x, ..., env = parent.frame())
# S3 method for lm
inzplot(
  x,
  which = c("residual", "scale", "leverage", "cooks", "normal", "hist"),
  show.bootstraps = nrow(x$model) < 1e+05,
  label.id = 3L,
  col.smooth = "orangered",
  col.bs = "lightgreen",
  cook.levels = c(0.5, 1),
  col.cook = "pink",
  ...,
  bs.fits = NULL,
  env = parent.frame()
)

Value

A ggplot object with a plot method that will show the plot in the graphics device

Arguments

x

a regression model

...

additional arguments

env

the environment for evaluating things (e.g., bootstraps)

which

the type of plot to draw

show.bootstraps

logical, if TRUE bootstrap smoothers will be shown (defaults to TRUE if fewer than 100,000 observations)

label.id

integer for the number of extreme points to label (with row id)

col.smooth

the colour of smoothers

col.bs

the colour of bootstrap (smoothers)

cook.levels

levels of the Cook's distance at which to draw contours.

col.cook

the colour of Cook's distance contours

bs.fits

a list of bootstrapped datasets

Functions

• inzplot(glm): Method for GLMs

Plot types

There are several plot types available:

• residual versus fitted

• scale-location

• residual versus leverage

• Cook's distance

• normal Q-Q

• histogram array

• forest plot

Author

Tom Elliott

Examples

iris_fit <- lm(Sepal.Width ~ Sepal.Length, data = iris)
inzplot(iris_fit)
inzplot(iris_fit, which = "residual", show.bootstraps = FALSE)