diagnostics.glm: Regression Diagnostics for Generalized Linear Models

In Rkabacoff/qacReg: Multiple Regression and Logistic Regression

Regression Diagnostics for Generalized Linear Models

Description

Provides regression diagnostics for a generalized linear model fit with glm or regress. Currently, binary logistic regression models are supported.

Usage

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

Arguments

x	an object of class c("glm")
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:

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.

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 three component list containing ggplot2 graphs: crplots, vifplot, and influenceplot.

Note

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

See Also

diagnostics, vif, outlierTest, influencePlot, crPlots

Examples

fit <- glm(caesarian ~ age + bp + delivery.time, family = binomial, data = caesarian)
diagnostics(fit)

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