inzplot.lm: inzplot method

In iNZightVIT/iNZightRegression: Tools for Exploring Regression Models with 'iNZight'

inzplot method

Description

inzplot method

Diagnostic Plots for Regression Models

Usage

## S3 method for class 'glm'
inzplot(x, ..., env = parent.frame())

## S3 method for class '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()
)

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

Value

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

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(s)

Tom Elliott

Examples

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

iNZightVIT/iNZightRegression documentation built on April 8, 2024, 10:25 a.m.