Nothing
plot.NaiveBayes <- function(x, vars, n = 1000, legendplot = TRUE, lty, col,
ylab = "Density", main = "Naive Bayes Plot", ...)
{
if(missing(vars)) vars <- names(x$tables)
if(missing(lty)) lty <- seq(along = x$apriori)
if(missing(col)) col <- rainbow(length(x$apriori))
vars <- vars[is.element(vars,names(x$tables))]
if(interactive() && length(vars > 1)){
opar <- par(ask=TRUE)
on.exit(par(opar))
}
if(length(vars))
for(j in seq(along = vars))
{
dummy <- (x$tables[names(x$tables) == as.name(vars[j])])
if(inherits(dummy[[1]], "matrix"))
{
dummy <- data.frame(dummy)
plotvector <- seq(min(x$x[,vars[j]]), max(x$x[,vars[j]]), len = n)
pv <- matrix(0, nrow = nrow(dummy), ncol = n)
for (i in 1:nrow(dummy))
pv[i,] <- dnorm(plotvector, mean = dummy[i,1], sd = dummy[i,2]) * x$apriori[i]
plot(plotvector, pv[1,], type = "l", lty = lty[1],
ylim = c(0, max(pv)), xlab = vars[j], ylab = ylab,
col = col[1], main = main,...)
for(i in 2:nrow(dummy))
lines(plotvector, pv[i,], lty = lty[i], col = col[i], ...)
if (legendplot)
legend(min(plotvector), max(pv), legend = rownames(dummy),
lty = lty, col = col)
}
if(inherits(dummy[[1]], "table"))
mosaicplot(dummy[[1]], main = main, ...)
if(inherits(dummy[[1]], "list"))
{
plotvector <- seq(min(x$x[,vars[j]]), max(x$x[,vars[j]]), len = n)
pv <- matrix(0, nrow = length(dummy[[1]]), ncol = n)
for (i in seq(along = dummy[[1]]))
pv[i,] <- dkernel(plotvector, kernel = dummy[[1]][[i]]) * x$apriori[i]
plot(plotvector, pv[1,], type = "l", lty = lty[1], ylim = c(0, max(pv)),
xlab = vars[j], ylab = ylab, col = col[1], main = main, ...)
for(i in 2:nrow(pv))
lines(plotvector, pv[i,], lty = lty[i], col = col[i], ...)
if (legendplot)
legend(min(plotvector), max(pv), legend = names(dummy[[1]]),
lty = lty, col = col)
}
}
}
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