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pamr.plotcv <- function(fit) {
par(mar = c(5, 5, 5, 1))
par(mfrow = c(2, 1))
n <- nrow(fit$yhat)
y <- fit$y
if(!is.null(fit$newy)) {
y <- fit$newy[fit$sample.subset]
}
nc <- length(table(y))
nfolds <- length(fit$folds)
err <- matrix(NA, ncol = ncol(fit$yhat), nrow = nfolds)
temp <- matrix(y, ncol = ncol(fit$yhat), nrow = n)
ni <- rep(NA, nfolds)
for(i in 1:nfolds) {
ii <- fit$folds[[i]]
ni[i] <- length(fit$folds[[i]])
err[i, ] <- apply(temp[ii, ] != fit$yhat[ii, ], 2, sum)/ni[i]
}
se <- sqrt(apply(err, 2, var)/nfolds)
plot(fit$threshold, fit$error, ylim = c(-0.1, 0.8), xlab =
"Value of threshold ", ylab = "Misclassification Error", type
= "n", yaxt = "n")
axis(3, at = fit$threshold, labels = paste(fit$size), srt = 90, adj = 0)
mtext("Number of genes", 3, 4, cex = 1.2)
axis(2, at = c(0, 0.2, 0.4, 0.6, 0.8))
lines(fit$threshold, fit$error, col = 2)
o <- fit$err == min(fit$err)
points(fit$threshold[o], fit$error[o], pch = "x")
error.bars(fit$threshold, fit$err - se, fit$err + se)
err2 <- matrix(NA, nrow = length(unique(y)), ncol = length(fit$threshold
))
for(i in 1:(length(fit$threshold) - 1)) {
s <- pamr.confusion(fit, fit$threshold[i], extra = FALSE)
diag(s) <- 0
err2[, i] <- apply(s, 1, sum)/table(y)
}
plot(fit$threshold, err2[1, ], ylim = c(-0.1, 1.1), xlab =
"Value of threshold ", ylab = "Misclassification Error", type
= "n", yaxt = "n")
axis(3, at = fit$threshold, labels = paste(fit$size), srt = 90, adj = 0)
# mtext("Number of genes", 3, 4,cex=1.2)
axis(2, at = c(0, 0.2, 0.4, 0.6, 0.8))
for(i in 1:nrow(err2)) {
lines(fit$threshold, err2[i, ], col = i + 1)
}
legend(0, 0.9, dimnames(table(y))[[1]], col = (2:(nc + 1)), lty = 1)
par(mfrow = c(1, 1))
}
error.bars <-function(x, upper, lower, width = 0.02, ...) {
xlim <- range(x)
barw <- diff(xlim) * width
segments(x, upper, x, lower, ...)
segments(x - barw, upper, x + barw, upper, ...)
segments(x - barw, lower, x + barw, lower, ...)
range(upper, lower)
}
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