##' LDA plot for a transband.
##'
##' @inheritParams plottrans.PCA
##' @export
plottrans.LDA <- function(Y, geno, nonrecomb, max.p=100, ...){
## use the first 100.
Y <- Y[, 1:min(max.p, ncol(Y))]
fit <- MASS::lda(Y[nonrecomb, ], grouping=geno[nonrecomb])
pred <- predict(fit, Y[-nonrecomb, ])
sca <- fit$scaling
y.lda <- Y %*% sca
orange <- rgb(230, 159, 0, maxColorValue = 256)
green <- rgb(27, 159, 120, maxColorValue = 256)
blue <- rgb(123, 104, 238, maxColorValue = 256)
yellow <- rgb(255, 255, 0, maxColorValue = 256)
genecolor <- c(blue,orange,green, yellow)
Class <- geno
Class[-nonrecomb] <- 4
xlim <- range(y.lda[, 1])
ylim <- range(y.lda[, 2])
px <- pretty(xlim)
py <- pretty(ylim)
broman::grayplot(x=y.lda[,1],y=y.lda[,2],
pch=21,xat=px,yat=py,col="black",bg=genecolor[Class],
hlines=py, vlines=px,
xaxt="n", yaxt="n",
xaxs="r", yaxs="r",
xlim=xlim, ylim=ylim,
xlab="Linear Discriminant 1", ylab="Linear Discriminant 2",
mgp=c(1.6,0.2,0), cex=0.8, las=1, ...)
u <- par("usr")
x <- u[1] + diff(u[1:2])*((2:5)*0.1+0.05)
points(x, rep(u[4]+diff(u[3:4])*0.035, 4), pch=21, col="black",
bg=genecolor, xpd=TRUE, cex=0.8)
x <- u[1] + diff(u[1:2])*((2:5)*0.1+0.05)
text(x, rep(u[4]+diff(u[3:4])*0.035, 3), c("BB","BR","RR","Recombinants"),
col=c(genecolor[1:3],"black"), xpd=TRUE, cex=0.8, pos=4)
}
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