diagnostics.lm: Regression Diagnostics for Linear Models

In Rkabacoff/qacReg: Multiple Regression and Logistic Regression

Regression Diagnostics for Linear Models

Description

Provides regression diagnostics for a linear models fit with lm or regress.

Usage

## S3 method for class 'lm'
diagnostics(x, alpha = 0.4, span = 0.8, plot = TRUE, ...)

Arguments

x	an object of class "lm"
alpha	numeric; transparency for plot points (default=0.4)
span	numeric; smoothing parameter for loess fit lines (default=0.8)
plot	logical; If TRUE (the default), graphs are printed. Otherwise, they are returned invisibly.
...	not currently used

Details

The diagnostics function is a wrapper for several diagnostic plotting functions:

Normality

Normality of the (studentized) residuals is assessed via a Normal Q-Q plot (qq_plot).

Linearity

Linearity of the explanatory-response relationships are assessed via Component + Residual (partial residual) plots (cr_plots). If there is a single predictor, a scatter plot with linear and loess lines is produced.

Constant variance

Homoscedasticity is evaluated via a Spread-Level plot (spread_plot).

Multicollinearity

Multicollinearity is assessed via variance inflation factors (vif_plot). If there is a single predictor variable, this section is skipped.

Outliers, leverage, and influence

A influence plot identifies outliers and influential observations (influence_plot).

Value

A five component list containing ggplot2 graphs: qqplot, crplots, slplot, vifplot, and influenceplot.

Note

Each function relies heavily on the car package. See the help for individual functions for details.

See Also

diagnostics, vif, qqPlot, outlierTest, influencePlot, crPlots, spreadLevelPlot, ncvTest

Examples

fit <- lm(mpg ~ hp + wt + accel + origin, data = auto_mpg)
diagnostics(fit)

Rkabacoff/qacReg documentation built on Aug. 1, 2022, 11:11 p.m.