residuals.manyglm: Residuals for MANYGLM, MANYANY, GLM1PATH Fits

In mvabund: Statistical Methods for Analysing Multivariate Abundance Data

Residuals for MANYGLM, MANYANY, GLM1PATH Fits

Description

Obtains Dunn-Smyth residuals from a fitted manyglm, manyany or glm1path object.

Usage

## S3 method for class 'manyglm'
residuals(object, ...)

Arguments

object	a fitted object of class inheriting from "manyglm".
...	further arguments passed to or from other methods.

Details

residuals.manyglm computes Randomised Quantile or “Dunn-Smyth" residuals (Dunn & Smyth 1996) for a manyglm object. If the fitted model is correct then Dunn-Smyth residuals are standard normal in distribution.

Similar functions have been written to compute Dunn-Smyth residuals from manyany and glm1path objects.

Note that for discrete data, Dunn-Smyth residuals involve random number generation, and will not return identical results on replicate runs. Hence it is worth calling this function multiple times to get a sense for whether your interpretation of results holds up under replication.

Value

A matrix of Dunn-Smyth residuals.

Author(s)

David Warton <[e-mail]>.

References

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

See Also

manyglm, manyany, glm1path, plot.manyglm.

Examples

data(spider)
spiddat <- mvabund(spider$abund)
X <- as.matrix(spider$x)

## obtain residuals for Poisson regression of the spider data, and doing a qqplot:
glmP.spid  <- manyglm(spiddat~X, family="poisson")
resP       <- residuals(glmP.spid)
qqnorm(resP)
qqline(resP,col="red")
#clear departure from normality.

## try again using negative binomial regression:
glmNB.spid <- manyglm(spiddat~X, family="negative.binomial")
resNB      <- residuals(glmNB.spid)
qqnorm(resNB)
qqline(resNB,col="red")
#that looks a lot more promising.

#note that you could construct a similar plot directly from the manyglm object using
plot(glmNB.spid, which=2)

mvabund documentation built on March 18, 2022, 7:25 p.m.