plotObserved <- function(data, results, pch=NULL, col=NULL, xlab=NULL, ylab=NULL, main=NULL, SE=T, reset=F){
.pardefault <- par(no.readonly = T)
genome.perc <- read.csv(file = system.file("genome.scale.csv", package = "SAGA"))[,2:3]
x <- cbind(data, genome.perc[data[,1],2])
colnames(x)[4] <- "gen.perc"
if(is.null(pch)) pch <- 16
if(is.null(col)) col <- "black"
if(is.null(xlab)) xlab <- "% P1 Genome"
if(is.null(ylab)) ylab <- "Phenotype Measure"
if(length(unique(x[,4])) != length(x[,4])){
xvals <- jitter(x[,4])
}else{
xvals <- x[,4]
}
if(SE==T){
yvals <- vector()
for(i in 1:nrow(x)){
high <- sum(x[i,2:3])
if(i == 1) yvals[i] <- sum(x[i,2:3])
low <- x[i,2] - x[i,3]
if(high > max(yvals)) yvals <- c(yvals, high)
if(low < min(yvals)) yvals <- c(yvals, low)
}
high <- max(yvals)
low <- min(yvals)
plot(x=xvals, y=x[,2], ylab=ylab, xlab=xlab, xaxt="n", pch=pch, main=main, ylim=c(low,high))
for(i in 1:nrow(x)){
lines(x=rep(xvals[i], 2), y= c(sum(x[i,2:3]), x[i,2] - x[i,3]))
}
}else{
plot(x=xvals, y=x[,2], ylab=ylab, xlab=xlab, xaxt="n", pch=pch, main=main)
}
axis(side=1,labels=c(0,50,100), at=c(0,50,100))
abline(glm(x[,2]~x[,4], weights = data[, 2] ^ - 2), lty="dashed", col="blue")
if(reset==T) par(.pardefault)
}
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