Nothing
plotBMAnormal <-
function (WEIGHTS, MEAN, SD, obs = NULL, exchangeable = NULL)
{
#
# copyright 2006-present, University of Washington. All rights reserved.
# for terms of use, see the LICENSE file
#
k <- length(WEIGHTS)
Q <- matrix( NA, 2, k + 1)
W <- WEIGHTS
n <- 1000
bot <- 1/n
top <- (n-1)/n
Q[1,k+1] <- quantBMAnormal( bot, WEIGHTS, MEAN, SD)
Q[2,k+1] <- quantBMAnormal( top, WEIGHTS, MEAN, SD)
for (j in 1:k){
W[] <- 0
W[j] <- 1
Q[1,j] <- quantBMAnormal( bot, W, MEAN, SD)
Q[2,j] <- quantBMAnormal( top, W, MEAN, SD)
}
r <- range(Q)
if (!is.null(obs) && !is.na(obs)) r <- range( c(r, obs))
if (is.null(exchangeable)) exchangeable <- 1:k
tex <- table(exchangeable)
lex <- length(tex)
FORC <- matrix( NA, n, lex + 1)
x <- seq(from = r[1], to = r[2], length = n)
for (l in 1:lex) {
j <- which(exchangeable == l)[1]
FORC[,l] <- tex[l]*(WEIGHTS[j]*dnorm( x, MEAN[j], SD[j]))
}
for (i in 1:n) FORC[i,lex+1] <- sum(FORC[i,1:lex])
# matplot(FORC,PROB)
med <- quantBMAnormal( .5, WEIGHTS, MEAN, SD)
up <- quantBMAnormal( .9, WEIGHTS, MEAN, SD)
lo <- quantBMAnormal( .1, WEIGHTS, MEAN, SD)
ylim <- range(FORC)
#if (!is.null(obs) && (obs <= lo || obs >= up)) return(invisible())
plot( x, FORC[,lex+1], type = "l", col = "black", ylim = ylim,
xlab = "Temperature", ylab = "Probability Density", lwd = 3)
abline( v = med, col = "black")
abline( v = lo, col = "black", lty = 2)
abline( v = up, col = "black", lty = 2)
if (!is.null(obs) && !is.na(obs)) abline( v = obs, col = "orange", lwd = 3)
colors <- rainbow(lex)
for (l in 1:lex) {
lines( x, FORC[,l], col = colors[l], lty = 1)
}
lines( x, FORC[,lex+1], col = "black", lwd = 3)
#aux <- list(...)
#print(c(lat = aux$lat, lon = aux$lon))
invisible(FORC)
}
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