linkLin: Checking linearity of link function

In glmxdiag: A Collection of Graphic Tools for GLM Diagnostics and some Extensions

Description

According to Lovison (2014), if the link function is correctly specified then there is a linear relationship between the working response variable z and the linear predictor eta. This plot suggests if the link function is appropriate.

Usage

linkLin(model, smooth = TRUE, xlab, ylab, main, pch, lcol, lwd, ...)

Arguments

model	a model supported by glmxdiag.
smooth	logical, whether to plot a smoothing spline.
xlab	title for the x axis.
ylab	title for the y axis.
main	an overall title for the plot.
pch	type of points.
lcol	color of the smoothing line.
lwd	size of the smoothing line.
...	further arguments passed to plot

Details

The assumption behind the output plot is that the model is correctly specified. In the y-axis there is the working response variable while on the x-axis there is the linear predictor: the more their relationship is linear the more appropriate the link function should be.

Value

Doesn't return a value, called for side effects.

Author(s)

Giuseppe Reale

Examples

## Simulate the data


set.seed(5)
n.obs <- 100
x <- rnorm(n.obs)
shape <- 25
mu <- exp(1 + .5* x)
y <- rgamma(n.obs, rate = shape / mu, shape = shape)

true.mod <- glm(y ~ x, family = Gamma(link = 'log'))
wrong1 <- glm(y ~ x, family = Gamma(link = 'inverse'))
wrong2 <- glm(y ~ x, family = Gamma(link = 'identity'))

par(mfrow = c(2, 2))
linkLin(true.mod)
linkLin(wrong1)
linkLin(wrong2)
par(mfrow = c(1, 1))

glmxdiag documentation built on Jan. 10, 2022, 9:07 a.m.