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gof <- function( glmfit, pcutoff=0.1, adjust="holm", plot=FALSE, main="qq-plot of residual deviances", ...)
# Compare residual deviances to chisquare distribution to identify dispersion outlier genes
# Davis McCarthy, Gordon Smyth
# 23 Mar 2011. Last modified 2 Jun 2020.
{
stopifnot( is(glmfit, "DGEGLM") )
gof.stats <- glmfit$deviance
gof.pvals <- pchisq(gof.stats, df=glmfit$df.residual, lower.tail=FALSE, log.p=FALSE)
outlier <- p.adjust(gof.pvals, method=adjust) < pcutoff
if(plot) {
n <- length(gof.stats)
z <- zscoreGamma(gof.stats,shape=glmfit$df.residual/2,scale=2)
col <- rep_len("black",n)
col[outlier] <- "blue"
pch <- rep_len(1,n)
pch[outlier] <- 16
qqnorm(z,col=col,pch=pch,main=main,...)
abline(0,1)
}
invisible(list(gof.statistics=gof.stats, gof.pvalues=gof.pvals, outlier=outlier, df=glmfit$df.residual[1]))
}
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