plot.glmfm {fit.models}

Comparison Diagnostic Plots for Generalized Linear Models

Description

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

1. Deviance Residuals vs. Predicted Values,

2. Response vs. Fitted Values,

3. Normal QQ Plot of Pearson Residuals,

4. Normal QQ Plot of Deviance Residuals,

5. Pearson Residuals vs. Mahalanobis Distance,

6. Sqrt Deviance Residuals vs. Predicted Values.

Usage

## S3 method for class 'glmfm'
plot(x, which.plots = 1:6, ...)

Arguments

x

a glmfm object.

which.plots

either "ask" (character string) or an integer vector specifying which plots to draw. In the later case, the plot numbers are given above.

...

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

sideBySideQQPlot for 4 and 5 and sideBySideScatterPlot 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)