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plot.mixEM.R
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plot.mixEM.R
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plot.mixEM <-function (x, whichplots = 1,
loglik = 1 %in% whichplots,
density = 2 %in% whichplots,
xlab1="Iteration", ylab1="Log-Likelihood",
main1="Observed Data Log-Likelihood", col1=1, lwd1=2,
xlab2=NULL, ylab2=NULL, main2=NULL, col2=NULL,
lwd2=2,
alpha = 0.05, marginal = FALSE, ...) {
def.par <- par(ask=(loglik + density > 1), "mar") # only ask and mar are changed
mix.object <- x
if (!inherits(mix.object, "mixEM"))
stop("Use only with \"mixEM\" objects!")
if (loglik) {
plot(mix.object$all.loglik, xlab = xlab1, ylab = ylab1, main = main1,
type="l", lwd=lwd1, col=col1, ...)
}
if (density) {
if (mix.object$ft == "logisregmixEM") {
if (ncol(mix.object$x) != 2) {
stop("The predictors must have 2 columns!")
}
if (sum((mix.object$y == 1) + (mix.object$y == 0)) != length(mix.object$y)) {
stop("The response must be binary!")
}
k = ncol(mix.object$beta)
x = mix.object$x[, 2]
if(is.null(main2)) { main2 <- "Most Probable Component Membership" }
if(is.null(xlab2)) { xlab2 <- "Predictor" }
if(is.null(ylab2)) { ylab2 <- "Response" }
if(is.null(col2)) { col2 <- 2:(k+1) }
plot(x, mix.object$y, main=main2, xlab=xlab2, ylab=ylab2,
col = col2[apply(mix.object$posterior, 1, which.max)], ...)
a = cbind(x, mix.object$y)
a = a[order(a[, 1]), ]
for (i in 1:k) {
lines(a[, 1], plogis(mix.object$beta[1, i] + mix.object$beta[2,
i] * a[, 1]), col = col2[i])
}
}
if (mix.object$ft == "normalmixEM") {
k <- ncol(mix.object$posterior)
x <- sort(mix.object$x)
a <- hist(x, plot = FALSE)
maxy <- max(max(a$density), .3989*mix.object$lambda/mix.object$sigma)
if(is.null(main2)) { main2 <- "Density Curves" }
if(is.null(xlab2)) { xlab2 <- "Data" }
if(is.null(col2)) { col2 <- 2:(k+1) }
hist(x, prob = TRUE, main = main2, xlab = xlab2,
ylim = c(0,maxy), ...)
if (length(mix.object$mu) == 1) {
arbvar <- TRUE
mix.object$sigma <- mix.object$scale * mix.object$sigma
arbmean <- FALSE
}
if (length(mix.object$mu) == k && length(mix.object$sigma) == 1) {
arbmean <- TRUE
arbvar <- FALSE
}
if (length(mix.object$sigma) == k && length(mix.object$mu) == k) {
arbmean <- TRUE
arbvar <- TRUE
}
for (i in 1:k) {
lines(x, mix.object$lambda[i] *
dnorm(x, mean = mix.object$mu[i * arbmean + (1 - arbmean)],
sd = mix.object$sigma[i * arbvar + (1 - arbvar)]),
col = col2[i], lwd = lwd2)
}
}
if (mix.object$ft == "repnormmixEM") {
x <- as.vector(as.matrix(x))
k <- ncol(mix.object$posterior)
x <- sort(mix.object$x)
a <- hist(x, plot = FALSE)
maxy <- max(max(a$density), .3989*mix.object$lambda/mix.object$sigma)
if (is.null(main2)) { main2 <- "Density Curves" }
if(is.null(xlab2)) { xlab2 <- "Data" }
if(is.null(col2)) { col2 <- 2:(k+1) }
hist(x, prob = TRUE, main = main2, xlab = xlab2,
ylim = c(0, maxy), ...)
if (length(mix.object$mu) == 1) {
arbvar <- TRUE
mix.object$sigma = mix.object$scale * mix.object$sigma
arbmean <- FALSE
}
if (length(mix.object$mu) == k && length(mix.object$sigma) == 1) {
arbmean <- TRUE
arbvar <- FALSE
}
if (length(mix.object$sigma) == k && length(mix.object$mu) == k) {
arbmean <- TRUE
arbvar <- TRUE
}
for (i in 1:k) {
lines(x, mix.object$lambda[i] *
dnorm(x, mean = mix.object$mu[i * arbmean + (1 - arbmean)],
sd = mix.object$sigma[i * arbvar + (1 - arbvar)]),
col = col2[i], lwd = lwd2)
}
}
if (mix.object$ft == "regmixEM.mixed") {
x.1 = mix.object$x
n = sum(sapply(x.1, nrow))
x.1.sum = sum(sapply(1:length(x.1), function(i) length(x.1[[i]][,
1])))
if (x.1.sum == n) {
x = lapply(1:length(x.1), function(i) matrix(x.1[[i]][,
-1], ncol = 1))
}
else {
x = x.1
}
post.beta(x = x, y = mix.object$y, p.beta = mix.object$posterior.beta,
p.z = mix.object$posterior.z)
}
if (mix.object$ft == "mvnormalmixEM") {
x = mix.object$x
if (ncol(x) != 2) {
stop("The data must have 2 columns!")
}
post = apply(mix.object$posterior, 1, which.max)
k <- ncol(mix.object$posterior)
if (is.list(mix.object$sigma)) {
sigma = mix.object$sigma
}
else {
sigma = lapply(1:k, function(i) mix.object$sigma)
}
if (is.list(mix.object$mu)) {
mu = mix.object$mu
}
else {
mu = lapply(1:k, function(i) mix.object$mu)
}
if(is.null(xlab2)) { xlab2 <- "X.1" }
if(is.null(ylab2)) { ylab2 <- "X.2" }
if(is.null(col2)) { col2 <- 2:(k+1) }
if (marginal == FALSE) {
if (is.null(main2)) { main2 <- "Density Curves" }
plot(x, col = col2[post], xlab = xlab2, ylab = ylab2, main = main2, ...)
lapply(1:k, function(i) points(mu[[i]][1], mu[[i]][2], pch = 19))
for (i in 1:k) {
for (j in 1:length(alpha)) {
ellipse(mu = mu[[i]], sigma = sigma[[i]], alpha = alpha[j],
col = col2[i])
}
}
} else {
if (is.null(main2)) { main2 <- "" } # FIXME: What's the right main2 here?
x <- mix.object$x[, 1]
y <- mix.object$x[, 2]
xhist <- hist(x, plot = FALSE)
yhist <- hist(y, plot = FALSE)
top <- max(c(xhist$counts, yhist$counts))
xrange <- range(x)
yrange <- range(y)
nf <- layout(matrix(c(2, 0, 1, 3), 2, 2, byrow = TRUE),
c(4, 1), c(1, 4), TRUE)
layout.show(nf)
par(mar = c(3, 3, 1, 1))
plot(mix.object$x[, 1], mix.object$x[,2], col = col2[post],
xlab = xlab2, ylab = ylab2, main = main2, ...)
lapply(1:k, function(i) points(mu[[i]][1], mu[[i]][2], pch = 19))
for (i in 1:k) {
for (j in 1:length(alpha)) {
ellipse(mu = mu[[i]], sigma = sigma[[i]], alpha = alpha[j],
col = (i + 1))
}
}
par(mar = c(0, 3, 1, 1))
barplot(xhist$counts, axes = FALSE, ylim = c(0, top),
space = 0, ...)
par(mar = c(3, 0, 1, 1))
barplot(yhist$counts, axes = FALSE, xlim = c(0, top),
space = 0, horiz = TRUE, ...)
}
}
if (mix.object$ft == "regmixEM") {
if (ncol(mix.object$x) != 2) {
stop("The predictors must have 2 columns!")
}
post <- apply(mix.object$posterior, 1, which.max)
k <- ncol(mix.object$posterior)
x <- mix.object$x[, 2]
y <- mix.object$y
n <- length(y)
if(is.null(main2)) { main2 <- "Most Probable Component Membership" }
if(is.null(xlab2)) { xlab2 <- "Predictor" }
if(is.null(ylab2)) { ylab2 <- "Response" }
if(is.null(col2)) { col2 <- 2:(k+1) }
plot(x, y, main = main2, xlab=xlab2, ylab=ylab2, type="n", ...)
a = cbind(mix.object$x[, 2], mix.object$y, post)
for (i in 1:k) {
xy = subset(cbind(a, mix.object$posterior[, i]), a[, 3] == i)[, -3]
xy = matrix(xy, ncol=3)
points(xy[, 1], xy[, 2], col = col2[i])
if (is.matrix(mix.object$beta) == FALSE) {
abline(coef = mix.object$beta)
beta = matrix(mix.object$beta, ncol = k, nrow = 2)
}
else {
abline(coef = mix.object$beta[, i], col = col2[i])
beta = mix.object$beta
}
out = lm(y ~ x, weights = mix.object$posterior[,i])
fit = beta[1, i] + beta[2, i] * x
out.aov = anova(out)
MSE = out.aov$Mean[2]
xy.f = cbind(x, y, fit)
xy.sort = xy.f[order(xy.f[, 1]), ]
x.new = seq(from=min(x),to=max(x),length.out=100)
y.new = beta[1, i] + beta[2, i] * x.new
s.h <- sqrt(MSE * (1/n + (x.new - mean(xy.sort[,1]))^2 /
var(xy.sort[, 1])/(n - 1)))
for (j in 1:length(alpha)) {
W = sqrt(qf(1 - alpha[j], 2, n - 2))
upper = y.new + W * s.h
lower = y.new - W * s.h
lines(x.new, upper, col = (i + 1))
lines(x.new, lower, col = (i + 1))
}
}
}
if (mix.object$ft == "expRMM_EM") {plotexpRMM(mix.object, ...)} # all default
if (mix.object$ft == "weibullRMM_SEM") {plotweibullRMM(mix.object, ...)} # all default
}
par(def.par) # reset ask and mar to original values
}