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Make the AnalyzeCovariate plots look a little nicer when there are a small number of data points 

git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@2298 348d0f76-0448-11de-a6fe-93d51630548a
This commit is contained in:
rpoplin 2009-12-09 21:22:40 +00:00
parent 408f6f3dee
commit 12ec154f01
1 changed files with 18 additions and 6 deletions
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24
R/plot_residualError_OtherCovariate.R
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@ -34,9 +34,13 @@ d.1000$residualError[which(d.1000$residualError < -10)] = -10
c$residualError = c$Qempirical-c$Qreported
c$residualError[which(c$residualError > 10)] = 10
c$residualError[which(c$residualError < -10)] = -10
pointType = "p"
if( length(c$Covariate) <= 20 ) {
pointType = "o"
}
if( is.numeric(c$Covariate) ) {
plot(d.good$Covariate, d.good$residualError, type="p", main=theTitle, ylab="Empirical - Reported Quality", xlab=covariateName, col="blue", pch=20, ylim=c(-10, 10), xlim=c(min(c$Covariate),max(c$Covariate)))
points(d.1000$Covariate, d.1000$residualError, type="p", col="cornflowerblue", pch=20)
plot(d.good$Covariate, d.good$residualError, type=pointType, main=theTitle, ylab="Empirical - Reported Quality", xlab=covariateName, col="blue", pch=20, ylim=c(-10, 10), xlim=c(min(c$Covariate),max(c$Covariate)))
points(d.1000$Covariate, d.1000$residualError, type=pointType, col="cornflowerblue", pch=20)
} else { # Dinuc (and other non-numeric covariates) are different to make their plots look nice
plot(c$Covariate, c$residualError, type="l", main=theTitle, ylab="Empirical - Reported Quality", xlab=covariateName, col="blue", ylim=c(-10, 10))
points(d.1000$Covariate, d.1000$residualError, type="l", col="cornflowerblue")
@ -53,17 +57,25 @@ outfile = paste(input, ".", covariateName,"_hist.pdf", sep="")
pdf(outfile, height=7, width=7)
hst=subset(data.frame(e$Covariate, e$nBases), e.nBases != 0)
hst2=subset(data.frame(f$Covariate, f$nBases), f.nBases != 0)
lwdSize=2
if( length(c$Covariate) <= 20 ) {
lwdSize=7
} else if( length(c$Covariate) <= 70 ) {
lwdSize=4
}
if( is.numeric(c$Covariate) ) {
if( length(hst$e.Covariate) == 0 ) {
plot(hst2$f.Covariate, hst2$f.nBases, type="h", lwd=2, col="cornflowerblue", main=paste(covariateName,"histogram"), ylim=c(0, max(hst2$f.nBases)), xlab=covariateName, ylab="Count",yaxt="n",xlim=c(min(c$Covariate),max(c$Covariate)))
plot(hst2$f.Covariate, hst2$f.nBases, type="h", lwd=lwdSize, col="cornflowerblue", main=paste(covariateName,"histogram"), ylim=c(0, max(hst2$f.nBases)), xlab=covariateName, ylab="Count",yaxt="n",xlim=c(min(c$Covariate),max(c$Covariate)))
} else {
plot(hst$e.Covariate, hst$e.nBases, type="h", lwd=2, main=paste(covariateName,"histogram"), xlab=covariateName, ylim=c(0, max(hst$e.nBases)),ylab="Number of Bases",yaxt="n",xlim=c(min(c$Covariate),max(c$Covariate)))
points(hst2$f.Covariate, hst2$f.nBases, type="h", lwd=2, col="cornflowerblue")
plot(hst$e.Covariate, hst$e.nBases, type="h", lwd=lwdSize, main=paste(covariateName,"histogram"), xlab=covariateName, ylim=c(0, max(hst$e.nBases)),ylab="Number of Bases",yaxt="n",xlim=c(min(c$Covariate),max(c$Covariate)))
points(hst2$f.Covariate, hst2$f.nBases, type="h", lwd=lwdSize, col="cornflowerblue")
}
axis(2,axTicks(2), format(axTicks(2), scientific=F))
} else { # Dinuc (and other non-numeric covariates) are different to make their plots look nice
hst=subset(data.frame(c$Covariate, c$nBases), c.nBases != 0)
plot(1:length(hst$c.Covariate), hst$c.nBases, type="h", lwd=7, main=paste(covariateName,"histogram"), ylim=c(0, max(hst$c.nBases)),xlab=covariateName, ylab="Number of Bases",yaxt="n",xaxt="n")
plot(1:length(hst$c.Covariate), hst$c.nBases, type="h", lwd=lwdSize, main=paste(covariateName,"histogram"), ylim=c(0, max(hst$c.nBases)),xlab=covariateName, ylab="Number of Bases",yaxt="n",xaxt="n")
axis(1, at=seq(1,length(hst$c.Covariate),2), labels = hst$c.Covariate[seq(1,length(hst$c.Covariate),2)])
axis(2,axTicks(2), format(axTicks(2), scientific=F))
}