在R中使用package mclust的coordProj绘图，如何更改"；“错误”；情节
我正在使用package mclust的coordProj函数，当您使用“errors”选项打印时，我想更改符号和颜色 我的代码在R中使用package mclust的coordProj绘图，如何更改"；“错误”；情节,r,plot,mixture-model,R,Plot,Mixture Model,我正在使用package mclust的coordProj函数，当您使用“errors”选项打印时，我想更改符号和颜色 我的代码 library("mclust") data(iris) init=sample(1:3,length(iris[,5]),replace=TRUE) est <- meVVV(iris[,-5], unmap(init)) coordProj(iris[,-5],parameters = est$parameters,z=est$z, truth = iris
library("mclust")
data(iris)
init=sample(1:3,length(iris[,5]),replace=TRUE)
est <- meVVV(iris[,-5], unmap(init))
coordProj(iris[,-5],parameters = est$parameters,z=est$z,
truth = iris[,5],what="errors",symbols=c("T","F"),colors=c("blue","red"))
library（“mclust”）
数据（iris）
初始=样本（1:3，长度（iris[，5]），替换=真）
est如果您使用页面（coordProj）
查看函数，您将看到，如果不更改它，这是不可能的。
一个快速修复方法是更改它以允许额外的参数，这里是symbol2=
。以下是第171行的编辑功能：
coordProj<-function (data, dimens = c(1, 2), parameters = NULL, z = NULL,
classification = NULL, truth = NULL, uncertainty = NULL,
what = c("classification", "errors", "uncertainty"), quantiles = c(0.75,
0.95), symbols = NULL, colors = NULL, scale = FALSE,
xlim = NULL, ylim = NULL, CEX = 1, PCH = ".", identify = FALSE,symbols2,
...)
{
if (is.null(dimens))
dimens <- c(1, 2)
if (is.null(classification) && !is.null(z))
classification <- map(z)
if (is.null(uncertainty) && !is.null(z))
uncertainty <- 1 - apply(z, 1, max)
if (!is.null(parameters)) {
mu <- parameters$mean
L <- ncol(mu)
sigma <- parameters$variance$sigma
haveParams <- !is.null(mu) && !is.null(sigma) && !any(is.na(mu)) &&
!any(is.na(sigma))
}
else haveParams <- FALSE
data <- data[, dimens, drop = FALSE]
if (dim(data)[2] != 2)
stop("need two dimensions")
if (is.null(xlim))
xlim <- range(data[, 1])
if (is.null(ylim))
ylim <- range(data[, 2])
if (scale) {
par(pty = "s")
d <- diff(xlim) - diff(ylim)
if (d > 0) {
ylim <- c(ylim[1] - d/2, ylim[2] + d/2)
}
else {
xlim <- c(xlim[1] + d/2, xlim[2] - d/2)
}
}
if (is.null(dnames <- dimnames(data)[[2]]))
xlab <- ylab <- ""
else {
xlab <- dnames[1]
ylab <- dnames[2]
}
if (haveParams) {
G <- ncol(mu)
dimpar <- dim(sigma)
if (length(dimpar) != 3) {
haveParams <- FALSE
warning("covariance must be a 3D matrix")
}
if (G != dimpar[3]) {
haveParams <- FALSE
warning("means and variance parameters are incompatible")
}
mu <- array(mu[dimens, ], c(2, G))
sigma <- array(sigma[dimens, dimens, ], c(2, 2, G))
}
if (!is.null(truth)) {
if (is.null(classification)) {
classification <- truth
truth <- NULL
}
}
if (!is.null(classification)) {
classification <- as.character(classification)
U <- sort(unique(classification))
L <- length(U)
noise <- classification[1] == "0"
if (is.null(symbols)) {
if (L <= length(.mclust$classPlotSymbols)) {
symbols <- .mclust$classPlotSymbols
if (noise) {
first <- symbols[1]
symbols[symbols == 16] <- first
symbols[1] <- 16
}
}
else if (L <= 9) {
symbols <- as.character(1:9)
}
else if (L <= 26) {
symbols <- LETTERS
}
}
else if (length(symbols) == 1)
symbols <- rep(symbols, L)
if (is.null(colors)) {
if (L <= length(.mclust$classPlotColors)) {
colors <- .mclust$classPlotColors[1:L]
if (noise) {
first <- colors[1]
colors[colors == "black"] <- first
colors[1] <- "black"
}
}
}
else if (length(colors) == 1)
colors <- rep(colors, L)
if (length(symbols) < L) {
warning("more symbols needed to show classification ")
symbols <- rep(16, L)
}
if (length(colors) < L) {
warning("more colors needed to show classification ")
colors <- rep("black", L)
}
}
if (length(what) > 1)
what <- what[1]
choices <- c("classification", "errors", "uncertainty")
m <- charmatch(what, choices, nomatch = 0)
if (m) {
what <- choices[m]
bad <- what == "classification" && is.null(classification)
bad <- bad || (what == "uncertainty" && is.null(uncertainty))
bad <- bad || (what == "errors" && (is.null(classification) ||
is.null(truth)))
if (bad)
warning("insufficient input for specified plot")
badClass <- (what == "errors" && (length(unique(classification)) !=
length(unique(truth))))
if (badClass && !bad)
warning("classification and truth differ in number of groups")
bad <- bad && badClass
}
else {
bad <- !m
warning("what improperly specified")
}
if (bad)
what <- "bad"
switch(EXPR = what, classification = {
plot(data[, 1], data[, 2], type = "n", xlab = xlab, ylab = ylab,
xlim = xlim, ylim = ylim, main = "", ...)
if (identify) {
TITLE <- paste(paste(dimens, collapse = ","), "Coordinate Projection showing Classification")
title(main = TITLE)
}
for (k in 1:L) {
I <- classification == U[k]
points(data[I, 1], data[I, 2], pch = symbols[k],
col = colors[k], cex = if (U[k] == "0") CEX/4 else CEX)
}
}, errors = {
ERRORS <- classError(classification, truth)$misclassified
plot(data[, 1], data[, 2], type = "n", xlab = xlab, ylab = ylab,
xlim = xlim, ylim = ylim, main = "", ...)
if (identify) {
TITLE <- paste(paste(dimens, collapse = ","), "Coordinate Projection showing Errors")
title(main = TITLE)
}
CLASSES <- unique(as.character(truth))
symOpen <- c(2, 0, 1, 5)
symFill <- c(17, 15, 16, 18)
good <- rep(TRUE, length(classification))
good[ERRORS] <- FALSE
if (L > 4) {
points(data[good, 1], data[good, 2], pch = 1, col = colors,
cex = CEX)
points(data[!good, 1], data[!good, 2], pch = 16,
cex = CEX)
} else {
for (k in 1:L) {
K <- truth == CLASSES[k]
if (any(I <- (K & good))) {
points(data[I, 1], data[I, 2], pch = symOpen[k],
col = colors[k], cex = CEX)
}
if (any(I <- (K & !good))) {
points(data[I, 1], data[I, 2], pch = symbols2[k], #Before: pch = symFill[k]
cex = CEX)
}
}
}
}, uncertainty = {
plot(data[, 1], data[, 2], type = "n", xlab = xlab, ylab = ylab,
xlim = xlim, ylim = ylim, main = "", ...)
if (identify) {
TITLE <- paste(paste(dimens, collapse = ","), "Coordinate Projection showing Uncertainty")
title(main = TITLE)
}
breaks <- quantile(uncertainty, probs = sort(quantiles))
I <- uncertainty <= breaks[1]
points(data[I, 1], data[I, 2], pch = 16, col = "gray75",
cex = 0.5 * CEX)
I <- uncertainty <= breaks[2] & !I
points(data[I, 1], data[I, 2], pch = 16, col = "gray50",
cex = 1 * CEX)
I <- uncertainty > breaks[2] & !I
points(data[I, 1], data[I, 2], pch = 16, col = "black",
cex = 1.5 * CEX)
}, {
plot(data[, 1], data[, 2], type = "n", xlab = xlab, ylab = ylab,
xlim = xlim, ylim = ylim, main = "", ...)
if (identify) {
TITLE <- paste(paste(dimens, collapse = ","), "Coordinate Projection")
title(main = TITLE)
}
points(data[, 1], data[, 2], pch = PCH, cex = CEX)
})
if (haveParams) {
for (k in 1:G) mvn2plot(mu = mu[, k], sigma = sigma[,
, k], k = 15)
}
invisible()
}
导致：
我使用的最后一个函数是这个函数
coordProj2<-function (data, dimens = c(1, 2), parameters = NULL, z = NULL,
classification = NULL, truth = NULL, uncertainty = NULL,
what = c("classification", "errors", "uncertainty"), quantiles = c(0.75,
0.95), symbols = NULL, colors = NULL, scale = FALSE,
xlim = NULL, ylim = NULL, CEX = 1, PCH = ".", identify = FALSE,symbolsTrue,symbolsFalse,colorsTrue,colorsFalse,
...)
{
if (is.null(dimens))
dimens <- c(1, 2)
if (is.null(classification) && !is.null(z))
classification <- map(z)
if (is.null(uncertainty) && !is.null(z))
uncertainty <- 1 - apply(z, 1, max)
if (!is.null(parameters)) {
mu <- parameters$mean
L <- ncol(mu)
sigma <- parameters$variance$sigma
haveParams <- !is.null(mu) && !is.null(sigma) && !any(is.na(mu)) &&
!any(is.na(sigma))
}
else haveParams <- FALSE
data <- data[, dimens, drop = FALSE]
if (dim(data)[2] != 2)
stop("need two dimensions")
if (is.null(xlim))
xlim <- range(data[, 1])
if (is.null(ylim))
ylim <- range(data[, 2])
if (scale) {
par(pty = "s")
d <- diff(xlim) - diff(ylim)
if (d > 0) {
ylim <- c(ylim[1] - d/2, ylim[2] + d/2)
}
else {
xlim <- c(xlim[1] + d/2, xlim[2] - d/2)
}
}
if (is.null(dnames <- dimnames(data)[[2]]))
xlab <- ylab <- ""
else {
xlab <- dnames[1]
ylab <- dnames[2]
}
if (haveParams) {
G <- ncol(mu)
dimpar <- dim(sigma)
if (length(dimpar) != 3) {
haveParams <- FALSE
warning("covariance must be a 3D matrix")
}
if (G != dimpar[3]) {
haveParams <- FALSE
warning("means and variance parameters are incompatible")
}
mu <- array(mu[dimens, ], c(2, G))
sigma <- array(sigma[dimens, dimens, ], c(2, 2, G))
}
if (!is.null(truth)) {
if (is.null(classification)) {
classification <- truth
truth <- NULL
}
}
if (!is.null(classification)) {
classification <- as.character(classification)
U <- sort(unique(classification))
L <- length(U)
noise <- classification[1] == "0"
if (is.null(symbols)) {
if (L <= length(.mclust$classPlotSymbols)) {
symbols <- .mclust$classPlotSymbols
if (noise) {
first <- symbols[1]
symbols[symbols == 16] <- first
symbols[1] <- 16
}
}
else if (L <= 9) {
symbols <- as.character(1:9)
}
else if (L <= 26) {
symbols <- LETTERS
}
}
else if (length(symbols) == 1)
symbols <- rep(symbols, L)
if (is.null(colors)) {
if (L <= length(.mclust$classPlotColors)) {
colors <- .mclust$classPlotColors[1:L]
if (noise) {
first <- colors[1]
colors[colors == "black"] <- first
colors[1] <- "black"
}
}
}
else if (length(colors) == 1)
colors <- rep(colors, L)
if (length(symbols) < L) {
warning("more symbols needed to show classification ")
symbols <- rep(16, L)
}
if (length(colors) < L) {
warning("more colors needed to show classification ")
colors <- rep("black", L)
}
}
if (length(what) > 1)
what <- what[1]
choices <- c("classification", "errors", "uncertainty")
m <- charmatch(what, choices, nomatch = 0)
if (m) {
what <- choices[m]
bad <- what == "classification" && is.null(classification)
bad <- bad || (what == "uncertainty" && is.null(uncertainty))
bad <- bad || (what == "errors" && (is.null(classification) ||
is.null(truth)))
if (bad)
warning("insufficient input for specified plot")
badClass <- (what == "errors" && (length(unique(classification)) !=
length(unique(truth))))
if (badClass && !bad)
warning("classification and truth differ in number of groups")
bad <- bad && badClass
}
else {
bad <- !m
warning("what improperly specified")
}
if (bad)
what <- "bad"
switch(EXPR = what, classification = {
plot(data[, 1], data[, 2], type = "n", xlab = xlab, ylab = ylab,
xlim = xlim, ylim = ylim, main = "", ...)
if (identify) {
TITLE <- paste(paste(dimens, collapse = ","), "Coordinate Projection showing Classification")
title(main = TITLE)
}
for (k in 1:L) {
I <- classification == U[k]
points(data[I, 1], data[I, 2], pch = symbols[k],
col = colors[k], cex = if (U[k] == "0") CEX/4 else CEX)
}
}, errors = {
ERRORS <- classError(classification, truth)$misclassified
plot(data[, 1], data[, 2], type = "n", xlab = xlab, ylab = ylab,
xlim = xlim, ylim = ylim, main = "", ...)
if (identify) {
TITLE <- paste(paste(dimens, collapse = ","), "Coordinate Projection showing Errors")
title(main = TITLE)
}
CLASSES <- unique(as.character(truth))
symOpen <- c(2, 0, 1, 5)
symFill <- c(17, 15, 16, 18)
good <- rep(TRUE, length(classification))
good[ERRORS] <- FALSE
if (L > 4) {
points(data[good, 1], data[good, 2], pch = 1, col = colors,
cex = CEX)
points(data[!good, 1], data[!good, 2], pch = 16,
cex = CEX)
} else {
for (k in 1:L) {
K <- truth == CLASSES[k]
if (any(I <- (K & good))) {
points(data[I, 1], data[I, 2], pch = symbolsTrue[k],
col = colorsTrue[k], cex = CEX)
}
if (any(I <- (K & !good))) {
points(data[I, 1], data[I, 2], pch = symbolsFalse[k], #Before: pch = symFill[k]
col=colorsFalse[k], cex = CEX)
}
}
}
}, uncertainty = {
plot(data[, 1], data[, 2], type = "n", xlab = xlab, ylab = ylab,
xlim = xlim, ylim = ylim, main = "", ...)
if (identify) {
TITLE <- paste(paste(dimens, collapse = ","), "Coordinate Projection showing Uncertainty")
title(main = TITLE)
}
breaks <- quantile(uncertainty, probs = sort(quantiles))
I <- uncertainty <= breaks[1]
points(data[I, 1], data[I, 2], pch = 16, col = "gray75",
cex = 0.5 * CEX)
I <- uncertainty <= breaks[2] & !I
points(data[I, 1], data[I, 2], pch = 16, col = "gray50",
cex = 1 * CEX)
I <- uncertainty > breaks[2] & !I
points(data[I, 1], data[I, 2], pch = 16, col = "black",
cex = 1.5 * CEX)
}, {
plot(data[, 1], data[, 2], type = "n", xlab = xlab, ylab = ylab,
xlim = xlim, ylim = ylim, main = "", ...)
if (identify) {
TITLE <- paste(paste(dimens, collapse = ","), "Coordinate Projection")
title(main = TITLE)
}
points(data[, 1], data[, 2], pch = PCH, cex = CEX)
})
if (haveParams) {
for (k in 1:G) mvn2plot(mu = mu[, k], sigma = sigma[,
, k], k = 15)
}
invisible()
}
谢谢你的回答。我试图实现一些不同的东西，但根据你的回答，我只需要改变一点。
coordProj2<-function (data, dimens = c(1, 2), parameters = NULL, z = NULL,
classification = NULL, truth = NULL, uncertainty = NULL,
what = c("classification", "errors", "uncertainty"), quantiles = c(0.75,
0.95), symbols = NULL, colors = NULL, scale = FALSE,
xlim = NULL, ylim = NULL, CEX = 1, PCH = ".", identify = FALSE,symbolsTrue,symbolsFalse,colorsTrue,colorsFalse,
...)
{
if (is.null(dimens))
dimens <- c(1, 2)
if (is.null(classification) && !is.null(z))
classification <- map(z)
if (is.null(uncertainty) && !is.null(z))
uncertainty <- 1 - apply(z, 1, max)
if (!is.null(parameters)) {
mu <- parameters$mean
L <- ncol(mu)
sigma <- parameters$variance$sigma
haveParams <- !is.null(mu) && !is.null(sigma) && !any(is.na(mu)) &&
!any(is.na(sigma))
}
else haveParams <- FALSE
data <- data[, dimens, drop = FALSE]
if (dim(data)[2] != 2)
stop("need two dimensions")
if (is.null(xlim))
xlim <- range(data[, 1])
if (is.null(ylim))
ylim <- range(data[, 2])
if (scale) {
par(pty = "s")
d <- diff(xlim) - diff(ylim)
if (d > 0) {
ylim <- c(ylim[1] - d/2, ylim[2] + d/2)
}
else {
xlim <- c(xlim[1] + d/2, xlim[2] - d/2)
}
}
if (is.null(dnames <- dimnames(data)[[2]]))
xlab <- ylab <- ""
else {
xlab <- dnames[1]
ylab <- dnames[2]
}
if (haveParams) {
G <- ncol(mu)
dimpar <- dim(sigma)
if (length(dimpar) != 3) {
haveParams <- FALSE
warning("covariance must be a 3D matrix")
}
if (G != dimpar[3]) {
haveParams <- FALSE
warning("means and variance parameters are incompatible")
}
mu <- array(mu[dimens, ], c(2, G))
sigma <- array(sigma[dimens, dimens, ], c(2, 2, G))
}
if (!is.null(truth)) {
if (is.null(classification)) {
classification <- truth
truth <- NULL
}
}
if (!is.null(classification)) {
classification <- as.character(classification)
U <- sort(unique(classification))
L <- length(U)
noise <- classification[1] == "0"
if (is.null(symbols)) {
if (L <= length(.mclust$classPlotSymbols)) {
symbols <- .mclust$classPlotSymbols
if (noise) {
first <- symbols[1]
symbols[symbols == 16] <- first
symbols[1] <- 16
}
}
else if (L <= 9) {
symbols <- as.character(1:9)
}
else if (L <= 26) {
symbols <- LETTERS
}
}
else if (length(symbols) == 1)
symbols <- rep(symbols, L)
if (is.null(colors)) {
if (L <= length(.mclust$classPlotColors)) {
colors <- .mclust$classPlotColors[1:L]
if (noise) {
first <- colors[1]
colors[colors == "black"] <- first
colors[1] <- "black"
}
}
}
else if (length(colors) == 1)
colors <- rep(colors, L)
if (length(symbols) < L) {
warning("more symbols needed to show classification ")
symbols <- rep(16, L)
}
if (length(colors) < L) {
warning("more colors needed to show classification ")
colors <- rep("black", L)
}
}
if (length(what) > 1)
what <- what[1]
choices <- c("classification", "errors", "uncertainty")
m <- charmatch(what, choices, nomatch = 0)
if (m) {
what <- choices[m]
bad <- what == "classification" && is.null(classification)
bad <- bad || (what == "uncertainty" && is.null(uncertainty))
bad <- bad || (what == "errors" && (is.null(classification) ||
is.null(truth)))
if (bad)
warning("insufficient input for specified plot")
badClass <- (what == "errors" && (length(unique(classification)) !=
length(unique(truth))))
if (badClass && !bad)
warning("classification and truth differ in number of groups")
bad <- bad && badClass
}
else {
bad <- !m
warning("what improperly specified")
}
if (bad)
what <- "bad"
switch(EXPR = what, classification = {
plot(data[, 1], data[, 2], type = "n", xlab = xlab, ylab = ylab,
xlim = xlim, ylim = ylim, main = "", ...)
if (identify) {
TITLE <- paste(paste(dimens, collapse = ","), "Coordinate Projection showing Classification")
title(main = TITLE)
}
for (k in 1:L) {
I <- classification == U[k]
points(data[I, 1], data[I, 2], pch = symbols[k],
col = colors[k], cex = if (U[k] == "0") CEX/4 else CEX)
}
}, errors = {
ERRORS <- classError(classification, truth)$misclassified
plot(data[, 1], data[, 2], type = "n", xlab = xlab, ylab = ylab,
xlim = xlim, ylim = ylim, main = "", ...)
if (identify) {
TITLE <- paste(paste(dimens, collapse = ","), "Coordinate Projection showing Errors")
title(main = TITLE)
}
CLASSES <- unique(as.character(truth))
symOpen <- c(2, 0, 1, 5)
symFill <- c(17, 15, 16, 18)
good <- rep(TRUE, length(classification))
good[ERRORS] <- FALSE
if (L > 4) {
points(data[good, 1], data[good, 2], pch = 1, col = colors,
cex = CEX)
points(data[!good, 1], data[!good, 2], pch = 16,
cex = CEX)
} else {
for (k in 1:L) {
K <- truth == CLASSES[k]
if (any(I <- (K & good))) {
points(data[I, 1], data[I, 2], pch = symbolsTrue[k],
col = colorsTrue[k], cex = CEX)
}
if (any(I <- (K & !good))) {
points(data[I, 1], data[I, 2], pch = symbolsFalse[k], #Before: pch = symFill[k]
col=colorsFalse[k], cex = CEX)
}
}
}
}, uncertainty = {
plot(data[, 1], data[, 2], type = "n", xlab = xlab, ylab = ylab,
xlim = xlim, ylim = ylim, main = "", ...)
if (identify) {
TITLE <- paste(paste(dimens, collapse = ","), "Coordinate Projection showing Uncertainty")
title(main = TITLE)
}
breaks <- quantile(uncertainty, probs = sort(quantiles))
I <- uncertainty <= breaks[1]
points(data[I, 1], data[I, 2], pch = 16, col = "gray75",
cex = 0.5 * CEX)
I <- uncertainty <= breaks[2] & !I
points(data[I, 1], data[I, 2], pch = 16, col = "gray50",
cex = 1 * CEX)
I <- uncertainty > breaks[2] & !I
points(data[I, 1], data[I, 2], pch = 16, col = "black",
cex = 1.5 * CEX)
}, {
plot(data[, 1], data[, 2], type = "n", xlab = xlab, ylab = ylab,
xlim = xlim, ylim = ylim, main = "", ...)
if (identify) {
TITLE <- paste(paste(dimens, collapse = ","), "Coordinate Projection")
title(main = TITLE)
}
points(data[, 1], data[, 2], pch = PCH, cex = CEX)
})
if (haveParams) {
for (k in 1:G) mvn2plot(mu = mu[, k], sigma = sigma[,
, k], k = 15)
}
invisible()
}
colorsTrue=c("red","blue","yellow")
colorsFalse=c("pink","gray","black")
symbolsTrue=c("T1","T2","T3")
symbolsFalse=c("F1","F2","F3")
coordProj2(iris[,-5],parameters = est$parameters,z=est$z,
truth = iris[,5],what="errors",colors=c(4,2,3),symbols=".",
symbolsTrue=symbolsTrue,symbolsFalse=symbolsFalse,
colorsTrue=colorsTrue,colorsFalse=colorsFalse)