plot.glmfm: Comparison Diagnostic Plots for Generalized Linear Models

In fit.models: fit.models

Description

Produces a set of comparison diagnostic plots. The plot options are

(2)

Deviance Residuals vs. Predicted Values,

(3)

Response vs. Fitted Values,

(4)

Normal QQ Plot of Modified Pearson Residuals,

(5)

Normal QQ Plot of Modified Deviance Residuals,

(6)

Modified Pearson Residuals vs. Leverage,

(7)

Scale-Location.

Usage

## S3 method for class 'glmfm'
plot(x, which.plots = c(2, 5, 7, 6), ...)

Arguments

x	a glmfm object.
which.plots	either "ask", "all", or a vector of integer values specifying which plots to draw. In the latter case, use the plot numbers given in the description above (or in the "ask" menu). Any other values will be silently ignored.
...	other parameters to be passed through to plotting functions.

Value

x is invisibly returned.

Side Effects

The selected plots are drawn on a graphics device.

See Also

qqPlot.lmfm for (4) and (5) and scatterPlot.lmfm for the others.

Examples

# From ?glm:
# A Gamma example, from McCullagh & Nelder (1989, pp. 300-2)

clotting <- data.frame(
    u = c(5,10,15,20,30,40,60,80,100),
    lot1 = c(118,58,42,35,27,25,21,19,18),
    lot2 = c(69,35,26,21,18,16,13,12,12))

lot1 <- glm(lot1 ~ log(u), data = clotting, family = Gamma)
lot2 <- glm(lot2 ~ log(u), data = clotting, family = Gamma)

fm <- fit.models(lot1, lot2)
plot(fm)

Example output

fit.models documentation built on May 2, 2019, 4:44 p.m.