glmtoolbox (version 0.1.12)

residuals2: Residuals for Linear and Generalized Linear Models

Description

Computes residuals for a fitted linear or generalized linear model.

Usage

residuals2(object, type, standardized = FALSE, plot.it = FALSE, identify, ...)

Value

A vector with the observed residuals type type.

Arguments

object

a object of the class lm or glm.

type

an (optional) character string giving the type of residuals which should be returned. The available options for LMs are: (1) externally studentized ("external"); (2) internally studentized ("internal") (default). The available options for GLMs are: (1) "pearson"; (2) "deviance" (default); (3) "quantile".

standardized

an (optional) logical switch indicating if the residuals should be standardized by dividing by the square root of \((1-h)\), where \(h\) is a measure of leverage. As default, standardized is set to FALSE.

plot.it

an (optional) logical switch indicating if a plot of the residuals versus the fitted values is required. As default, plot.it is set to FALSE.

identify

an (optional) integer value indicating the number of individuals to identify on the plot of residuals. This is only appropriate when plot.it=TRUE.

...

further arguments passed to or from other methods

References

Atkinson A.C. (1985) Plots, Transformations and Regression. Oxford University Press, Oxford.

Davison A.C., Gigli A. (1989) Deviance Residuals and Normal Scores Plots. Biometrika 76, 211-221.

Dunn P.K., Smyth G.K. (1996) Randomized Quantile Residuals. Journal of Computational and Graphical Statistics 5, 236-244.

Pierce D.A., Schafer D.W. (1986) Residuals in Generalized Linear Models. Journal of the American Statistical Association 81, 977-986.

Examples

###### Example 1: Species richness in plots
data(richness)
fit1 <- lm(Species ~ Biomass + pH, data=richness)
residuals2(fit1, type="external", plot.it=TRUE, col="red", pch=20, col.lab="blue",
           col.axis="blue", col.main="black", family="mono", cex=0.8)

###### Example 2: Lesions of Aucuba mosaic virus
data(aucuba)
fit2 <- glm(lesions ~ time, family=poisson, data=aucuba)
residuals2(fit2, type="quantile", plot.it=TRUE, col="red", pch=20, col.lab="blue",
           col.axis="blue",col.main="black",family="mono",cex=0.8)