mvabund (version 4.1.12)

plot.manyany: Plot Diagnostics for a manyany or glm1path Object

Description

A residual vs fits plot from a manyany or glm1path object.

Usage

# S3 method for manyany
plot( x, ...)

Arguments

x

manyany object, resulting from a call to manyany.

…

other parameters to be passed through to plotting functions.

Details

plot.manyany is used to check assumptions that are made when fitting a model via manyany or glm1path. As in Wang et al (2012), you should check the residual vs fits plot for no pattern (hence no suggestion of failure of any linearity and mean-variance assumptions). It is also desirable that residuals follow a straight line of slope one on a normal Q-Q plot.

These plots use Dunn-Smyth residuals (Dunn & Smyth 1996), described at residuals.manyglm. Note that for discrete data, these residuals involve random number generation, and will not return identical results on replicate runs - so it is recommended that you plot your data a few times to check if any pattern shows up consistently across replicate plots.

Note also that for glm1path objects, slope coefficients have been shrunk towards zero so it is not unusual to see an increasing slope on the residual plot, with larger residuals coinciding with larger fitted values. This arises a a consequent of shrinkage, check if it goes away upon removing the penaly term (e.g. on refitting using manyglm) before ringing any alarm bells.

References

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

Wang Y., Naumann U., Wright S.T. & Warton D.I. (2012). mvabund - an R package for model-based analysis of multivariate abundance data. Methods in Ecology and Evolution 3, 471-474.

See Also

glm1path, manyany, manyglm

Examples

# NOT RUN {
require(graphics)

data(spider)
abund <- mvabund(spider$abund)
X <- spider$x

## Plot the diagnostics for a log-linear model assuming counts are poisson:
spidPois <- manyany("glm", abund, data=X, abund ~ X, family=poisson())
plot(spidPois,pch=19,cex=0.2)
## Fan-shape means trouble for our Poisson assumption.

## Try a negative binomial instead...
require(MASS) # this package is needed for its negative binomial family function
spidNB <- manyany("manyglm", abund, data=X, abund ~ X, family="negative.binomial")
plot(spidNB,pch=19,cex=0.2,xlim=c(-15,6))
## That's looking a lot better...

# }