Obtains Dunn-Smyth residuals from a fitted
a fitted object of class inheriting from
further arguments passed to or from other methods.
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
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.
A matrix of Dunn-Smyth residuals.
David Warton <[email protected]>.
Dunn, P.K., & Smyth, G.K. (1996). Randomized quantile residuals. Journal of Computational and Graphical Statistics 5, 236-244.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
data(spider) spiddat <- mvabund(spider$abund) X <- spider$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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.