TQresidualshiny: function for creating the T2 vs Q residual
In andreaelia/pcashinyapp: PCA functions for the PCA shiny application
Description
Usage
Arguments
Details
Note
Author(s)
References
Examples
Description
This function works within the pca shiny application. It works with the pcashiny function and use the data set to create the T2 vs Q residual plot, together with the contribution plot
Usage
1
TQresidualshiny(data, centering, scaling, pc_number, labels, point_dim, legend_name, LegendPos, legend_dim, Title, text.row, text.labels, point_type, CP_Point, CP_txt_Point, PLOT, CP_dim_var, CP_las)
Arguments
data
data matrix
centering
TRUE or FALSE
scaling
TRUE or FALSE
pc_number
number of principal component
labels
vector of scores labels
point_dim
dimension of scores
legend_name
name of the legend
LegendPos
position of the legend
legend_dim
dimension of the legend
Title
title of the plot
text.row
TRUE or FALSE
text.labels
vector for texting the scores
point_type
type of point
CP_Point
cp point
CP_txt_Point
cp text point
PLOT
TRUE or FALSE
CP_dim_var
variable dimension cp
CP_las
cp las
Details
There are no more details
Note
There are no further notes
Author(s)
Elia Andrea
References
package.skeleton code
Examples
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##---- Should be DIRECTLY executable !! ----
##-- ==> Define data, use random,
##-- or do help(data=index) for the standard data sets.
## The function is currently defined as
function (data, centering, scaling, pc_number, labels, point_dim,
legend_name, LegendPos, legend_dim, Title, text.row, text.labels,
point_type, CP_Point, CP_txt_Point, PLOT, CP_dim_var, CP_las)
{
if (missing(CP_dim_var)) {
CP_dim_var = 0.8
}
if (missing(CP_las)) {
CP_las = 2
}
if (CP_las == TRUE) {
CP_las = 2
}
if (CP_las == FALSE) {
CP_las = 1
}
if (missing(text.labels)) {
text.labels = row.names(data)
}
X <- scale(data, center = centering, scale = scaling)
row.names(X) <- c(1:nrow(X))
nc <- ncol(X)
X.cov <- cov(X)
T <- eigen(X.cov)
L <- T[[1]]
V <- sqrt(L[1:pc_number])
sgt <- sum(apply(X, 2, "var"))
T <- T[[2]]
S <- as.matrix(X) %*% T
PCA <- list(dataset = X, sdev = V, sgt = sgt, loadings = T,
scores = S, center = centering, scale = scaling, n.obs = nc)
m <- ncol(X)
if (missing(pc_number) == FALSE) {
ncp <- pc_number
}
if (missing(pc_number)) {
ncp = ncol(PCA$loadings) - 1
}
if (missing(legend_dim)) {
legend_dim <- 0.5
}
if (missing(legend_name)) {
legend_name = "Group"
}
n <- nrow(PCA$dataset)
X <- matrix(PCA$dataset, nrow = n, ncol = m)
P <- PCA$loadings[, 1:ncp]
L <- (PCA$sdev[1:ncp])^2
sgl <- sum(L)
sgr <- PCA$sgt - sgl
MQ <- diag(rep(1, m)) - (P %*% t(P))
MT <- P %*% (diag(1/L)) %*% t(P)
Q <- diag(X %*% MQ %*% t(X))
T <- diag(X %*% MT %*% t(X))
Tlim <- (n - 1) * ncp/(n - ncp) * qf(0.95, ncp, n - ncp)
if (is.na(Tlim))
Tlim <- 0
mT <- max(T, Tlim)
t1 <- sgr
t2 <- sgr^2
t3 <- sgr^3
h0 = 1 - 2 * t1 * t2/3/t3^2
Qlim <- t1 * (1 + h0 * qnorm(0.95) * sqrt(2 * t2)/t1 + t2 *
h0 * (h0 - 1)/t1^2)^(1/h0)
if (is.na(Qlim))
Qlim <- 0
mQ <- max(Q, Qlim)
liv <- factor(labels, ordered = TRUE)
if (missing(text.row)) {
text.row = FALSE
}
if (PLOT == "Diagnostic Plot T2 vs Q Residuals") {
if (missing(labels)) {
if (!legend_name == FALSE) {
if (LegendPos == FALSE) {
layout(matrix(c(1, 2), nrow = 1), widths = c(0.7,
0.2))
par(mar = c(5, 4, 4, 2) + 0.1)
}
}
if (text.row == FALSE) {
plot(T, Q, ylim = c(0, mQ * 1.1), xlim = c(0,
mT * 1.1), ylab = "Q Residuals", xlab = "T^2 Hotelling Index",
cex = point_dim, cex.lab = 1.2, col = labels,
pch = point_type)
title(main = paste("T^2 vs Q residuals, Comp. Number:",
ncp), sub = "Confidence: 95%", cex.sub = 0.6)
grid()
if (Tlim != 0)
abline(v = Tlim, lty = 2, col = "red")
if (Qlim != 0)
abline(h = Qlim, lty = 2, col = "red")
if ((Tlim != 0) & (Qlim != 0)) {
QT <- data.frame(Q = Q, T = T, tx = row.names(data))
QTs <- subset(QT, ((T > Tlim) & (Q > Qlim)))
if (nrow(QTs) != 0)
text(QTs$T, QTs$Q, label = QTs$tx, cex = 0.5,
pos = 3)
}
}
if (text.row == TRUE) {
plot(T, Q, type = "n", ylim = c(0, mQ * 1.1),
xlim = c(0, mT * 1.1), ylab = "Q Residuals",
xlab = "T^2 Hotelling Index")
title(main = paste("T^2 vs Q residuals, Comp. Number:",
ncp), sub = "Confidence: 95%", cex.sub = 0.6)
if (Tlim != 0)
abline(v = Tlim, lty = 2, col = "red")
if (Qlim != 0)
abline(h = Qlim, lty = 2, col = "red")
col_text <- c(1:length(levels(liv)))
text(T, Q, labels = text.labels, cex = point_dim,
col = col_text[liv])
grid()
}
if (!legend_name == FALSE) {
if (LegendPos == FALSE) {
par(mar = c(5, 0, 4, 2) + 0.1)
plot(1:3, rnorm(3), pch = 1, lty = 1, ylim = c(-2,
2), type = "n", axes = FALSE, ann = FALSE)
legend(1, 1, col = unique(liv), unique(liv),
pch = unique(point_type), bty = "n", cex = legend_dim,
title = legend_name)
}
if (!LegendPos == FALSE) {
legend(LegendPos, col = unique(liv), legend = unique(liv),
pch = unique(point_type), bty = "n", cex = legend_dim,
title = legend_name)
}
}
}
if (missing(labels) == FALSE) {
if (!legend_name == FALSE) {
if (LegendPos == FALSE) {
layout(matrix(c(1, 2), nrow = 1), widths = c(0.7,
0.2))
par(mar = c(5, 4, 4, 2) + 0.1)
}
}
if (text.row == FALSE) {
plot(T, Q, ylim = c(0, mQ * 1.1), xlim = c(0,
mT * 1.1), ylab = "Q Residuals", xlab = "T^2 Hotelling Index",
cex = point_dim, cex.lab = 1.5, pch = point_type,
col = liv)
title(main = paste("T^2 vs Q residuals, Comp. Number:",
ncp), sub = "Confidence: 95%", cex.main = 1.5,
cex.sub = 1)
grid()
if (Tlim != 0)
abline(v = Tlim, lty = 2, col = "red")
if (Qlim != 0)
abline(h = Qlim, lty = 2, col = "red")
if ((Tlim != 0) & (Qlim != 0)) {
QT <- data.frame(Q = Q, T = T, tx = row.names(data))
QTs <- subset(QT, ((T > Tlim) & (Q > Qlim)))
if (nrow(QTs) != 0)
text(QTs$T, QTs$Q, label = QTs$tx, cex = 0.5,
pos = 3)
}
}
if (text.row == TRUE) {
plot(T, Q, type = "n", ylim = c(0, mQ * 1.1),
xlim = c(0, mT * 1.1), ylab = "Q Residuals",
xlab = "T^2 Hotelling Index", cex = point_dim,
cex.lab = 1.5, pch = point_type, col = liv)
title(main = paste("T^2 vs Q residuals, Comp. Number:",
ncp), sub = "Confidence: 95%", cex.main = 1.5,
cex.sub = 1)
if (Tlim != 0)
abline(v = Tlim, lty = 2, col = "red")
if (Qlim != 0)
abline(h = Qlim, lty = 2, col = "red")
col_text <- c(1:length(levels(liv)))
text(T, Q, labels = text.labels, cex = point_dim,
col = col_text[liv])
grid()
}
if (!legend_name == FALSE) {
if (LegendPos == FALSE) {
par(mar = c(5, 0, 4, 2) + 0.1)
plot(1:3, rnorm(3), pch = 1, lty = 1, ylim = c(-2,
2), type = "n", axes = FALSE, ann = FALSE)
legend(1, 1, col = unique(liv), unique(liv),
pch = unique(point_type), bty = "n", cex = legend_dim,
title = legend_name)
}
if (!LegendPos == FALSE) {
legend(LegendPos, col = unique(liv), legend = unique(liv),
pch = unique(point_type), bty = "n", cex = legend_dim,
title = legend_name)
}
}
}
}
Res <- list(dataset = X, sdev = V, sgt = sgt, loadings = T,
scores = S, center = centering, scale = scaling, n.obs = nc)
QT <- data.frame(Q = Q, T = T, tx = row.names(data))
QTs <- subset(QT, ((T > Tlim) & (Q > Qlim)))
if (PLOT == "Contribution Plot") {
if (missing(CP_Point) == FALSE) {
if (is.vector(CP_Point) == TRUE) {
S <- Res$scores[, 1:ncp]
MQ <- S %*% t(P)
MT <- P %*% (diag(1/L)) %*% t(P)
T <- X %*% MT
Q <- sign(X - MQ) * (X - MQ)^2
colnames(T) <- colnames(data)
colnames(Q) <- colnames(data)
Ti <- T[CP_Point, ]
Qi <- Q[CP_Point, ]
lim_Qi <- max(abs(min(Qi)), max(Qi))
lim_Ti <- max(abs(min(Ti)), max(Ti))
op <- par(mfrow = c(1, 2))
barplot(Qi, main = paste("ContrPlot Q point:",
CP_txt_Point), ylim = c(-lim_Qi, lim_Qi), col = 4,
cex.lab = 1.2, cex.names = CP_dim_var, las = CP_las,
plot.grid = TRUE, cex.axis = 0.6)
barplot(Ti, main = paste("ContrPlot Ti^2 point:",
CP_txt_Point), ylim = c(-lim_Ti, lim_Ti), col = 4,
cex.lab = 1.2, cex.names = CP_dim_var, las = CP_las,
plot.grid = TRUE, cex.axis = 0.6)
par(op)
Res <- list(dataset = X, sdev = V, sgt = sgt,
loadings = T, scores = S, center = centering,
scale = scaling, n.obs = nc, T = T, Q = Q)
}
}
}
Res
}
andreaelia/pcashinyapp documentation built on May 12, 2019, 3:32 a.m.
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Description Usage Arguments Details Note Author(s) References Examples
Description
This function works within the pca shiny application. It works with the pcashiny function and use the data set to create the T2 vs Q residual plot, together with the contribution plot
Usage
1 | TQresidualshiny(data, centering, scaling, pc_number, labels, point_dim, legend_name, LegendPos, legend_dim, Title, text.row, text.labels, point_type, CP_Point, CP_txt_Point, PLOT, CP_dim_var, CP_las)
|
Arguments
data |
data matrix |
centering |
TRUE or FALSE |
scaling |
TRUE or FALSE |
pc_number |
number of principal component |
labels |
vector of scores labels |
point_dim |
dimension of scores |
legend_name |
name of the legend |
LegendPos |
position of the legend |
legend_dim |
dimension of the legend |
Title |
title of the plot |
text.row |
TRUE or FALSE |
text.labels |
vector for texting the scores |
point_type |
type of point |
CP_Point |
cp point |
CP_txt_Point |
cp text point |
PLOT |
TRUE or FALSE |
CP_dim_var |
variable dimension cp |
CP_las |
cp las |
Details
There are no more details
Note
There are no further notes
Author(s)
Elia Andrea
References
package.skeleton code
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 | ##---- Should be DIRECTLY executable !! ----
##-- ==> Define data, use random,
##-- or do help(data=index) for the standard data sets.
## The function is currently defined as
function (data, centering, scaling, pc_number, labels, point_dim,
legend_name, LegendPos, legend_dim, Title, text.row, text.labels,
point_type, CP_Point, CP_txt_Point, PLOT, CP_dim_var, CP_las)
{
if (missing(CP_dim_var)) {
CP_dim_var = 0.8
}
if (missing(CP_las)) {
CP_las = 2
}
if (CP_las == TRUE) {
CP_las = 2
}
if (CP_las == FALSE) {
CP_las = 1
}
if (missing(text.labels)) {
text.labels = row.names(data)
}
X <- scale(data, center = centering, scale = scaling)
row.names(X) <- c(1:nrow(X))
nc <- ncol(X)
X.cov <- cov(X)
T <- eigen(X.cov)
L <- T[[1]]
V <- sqrt(L[1:pc_number])
sgt <- sum(apply(X, 2, "var"))
T <- T[[2]]
S <- as.matrix(X) %*% T
PCA <- list(dataset = X, sdev = V, sgt = sgt, loadings = T,
scores = S, center = centering, scale = scaling, n.obs = nc)
m <- ncol(X)
if (missing(pc_number) == FALSE) {
ncp <- pc_number
}
if (missing(pc_number)) {
ncp = ncol(PCA$loadings) - 1
}
if (missing(legend_dim)) {
legend_dim <- 0.5
}
if (missing(legend_name)) {
legend_name = "Group"
}
n <- nrow(PCA$dataset)
X <- matrix(PCA$dataset, nrow = n, ncol = m)
P <- PCA$loadings[, 1:ncp]
L <- (PCA$sdev[1:ncp])^2
sgl <- sum(L)
sgr <- PCA$sgt - sgl
MQ <- diag(rep(1, m)) - (P %*% t(P))
MT <- P %*% (diag(1/L)) %*% t(P)
Q <- diag(X %*% MQ %*% t(X))
T <- diag(X %*% MT %*% t(X))
Tlim <- (n - 1) * ncp/(n - ncp) * qf(0.95, ncp, n - ncp)
if (is.na(Tlim))
Tlim <- 0
mT <- max(T, Tlim)
t1 <- sgr
t2 <- sgr^2
t3 <- sgr^3
h0 = 1 - 2 * t1 * t2/3/t3^2
Qlim <- t1 * (1 + h0 * qnorm(0.95) * sqrt(2 * t2)/t1 + t2 *
h0 * (h0 - 1)/t1^2)^(1/h0)
if (is.na(Qlim))
Qlim <- 0
mQ <- max(Q, Qlim)
liv <- factor(labels, ordered = TRUE)
if (missing(text.row)) {
text.row = FALSE
}
if (PLOT == "Diagnostic Plot T2 vs Q Residuals") {
if (missing(labels)) {
if (!legend_name == FALSE) {
if (LegendPos == FALSE) {
layout(matrix(c(1, 2), nrow = 1), widths = c(0.7,
0.2))
par(mar = c(5, 4, 4, 2) + 0.1)
}
}
if (text.row == FALSE) {
plot(T, Q, ylim = c(0, mQ * 1.1), xlim = c(0,
mT * 1.1), ylab = "Q Residuals", xlab = "T^2 Hotelling Index",
cex = point_dim, cex.lab = 1.2, col = labels,
pch = point_type)
title(main = paste("T^2 vs Q residuals, Comp. Number:",
ncp), sub = "Confidence: 95%", cex.sub = 0.6)
grid()
if (Tlim != 0)
abline(v = Tlim, lty = 2, col = "red")
if (Qlim != 0)
abline(h = Qlim, lty = 2, col = "red")
if ((Tlim != 0) & (Qlim != 0)) {
QT <- data.frame(Q = Q, T = T, tx = row.names(data))
QTs <- subset(QT, ((T > Tlim) & (Q > Qlim)))
if (nrow(QTs) != 0)
text(QTs$T, QTs$Q, label = QTs$tx, cex = 0.5,
pos = 3)
}
}
if (text.row == TRUE) {
plot(T, Q, type = "n", ylim = c(0, mQ * 1.1),
xlim = c(0, mT * 1.1), ylab = "Q Residuals",
xlab = "T^2 Hotelling Index")
title(main = paste("T^2 vs Q residuals, Comp. Number:",
ncp), sub = "Confidence: 95%", cex.sub = 0.6)
if (Tlim != 0)
abline(v = Tlim, lty = 2, col = "red")
if (Qlim != 0)
abline(h = Qlim, lty = 2, col = "red")
col_text <- c(1:length(levels(liv)))
text(T, Q, labels = text.labels, cex = point_dim,
col = col_text[liv])
grid()
}
if (!legend_name == FALSE) {
if (LegendPos == FALSE) {
par(mar = c(5, 0, 4, 2) + 0.1)
plot(1:3, rnorm(3), pch = 1, lty = 1, ylim = c(-2,
2), type = "n", axes = FALSE, ann = FALSE)
legend(1, 1, col = unique(liv), unique(liv),
pch = unique(point_type), bty = "n", cex = legend_dim,
title = legend_name)
}
if (!LegendPos == FALSE) {
legend(LegendPos, col = unique(liv), legend = unique(liv),
pch = unique(point_type), bty = "n", cex = legend_dim,
title = legend_name)
}
}
}
if (missing(labels) == FALSE) {
if (!legend_name == FALSE) {
if (LegendPos == FALSE) {
layout(matrix(c(1, 2), nrow = 1), widths = c(0.7,
0.2))
par(mar = c(5, 4, 4, 2) + 0.1)
}
}
if (text.row == FALSE) {
plot(T, Q, ylim = c(0, mQ * 1.1), xlim = c(0,
mT * 1.1), ylab = "Q Residuals", xlab = "T^2 Hotelling Index",
cex = point_dim, cex.lab = 1.5, pch = point_type,
col = liv)
title(main = paste("T^2 vs Q residuals, Comp. Number:",
ncp), sub = "Confidence: 95%", cex.main = 1.5,
cex.sub = 1)
grid()
if (Tlim != 0)
abline(v = Tlim, lty = 2, col = "red")
if (Qlim != 0)
abline(h = Qlim, lty = 2, col = "red")
if ((Tlim != 0) & (Qlim != 0)) {
QT <- data.frame(Q = Q, T = T, tx = row.names(data))
QTs <- subset(QT, ((T > Tlim) & (Q > Qlim)))
if (nrow(QTs) != 0)
text(QTs$T, QTs$Q, label = QTs$tx, cex = 0.5,
pos = 3)
}
}
if (text.row == TRUE) {
plot(T, Q, type = "n", ylim = c(0, mQ * 1.1),
xlim = c(0, mT * 1.1), ylab = "Q Residuals",
xlab = "T^2 Hotelling Index", cex = point_dim,
cex.lab = 1.5, pch = point_type, col = liv)
title(main = paste("T^2 vs Q residuals, Comp. Number:",
ncp), sub = "Confidence: 95%", cex.main = 1.5,
cex.sub = 1)
if (Tlim != 0)
abline(v = Tlim, lty = 2, col = "red")
if (Qlim != 0)
abline(h = Qlim, lty = 2, col = "red")
col_text <- c(1:length(levels(liv)))
text(T, Q, labels = text.labels, cex = point_dim,
col = col_text[liv])
grid()
}
if (!legend_name == FALSE) {
if (LegendPos == FALSE) {
par(mar = c(5, 0, 4, 2) + 0.1)
plot(1:3, rnorm(3), pch = 1, lty = 1, ylim = c(-2,
2), type = "n", axes = FALSE, ann = FALSE)
legend(1, 1, col = unique(liv), unique(liv),
pch = unique(point_type), bty = "n", cex = legend_dim,
title = legend_name)
}
if (!LegendPos == FALSE) {
legend(LegendPos, col = unique(liv), legend = unique(liv),
pch = unique(point_type), bty = "n", cex = legend_dim,
title = legend_name)
}
}
}
}
Res <- list(dataset = X, sdev = V, sgt = sgt, loadings = T,
scores = S, center = centering, scale = scaling, n.obs = nc)
QT <- data.frame(Q = Q, T = T, tx = row.names(data))
QTs <- subset(QT, ((T > Tlim) & (Q > Qlim)))
if (PLOT == "Contribution Plot") {
if (missing(CP_Point) == FALSE) {
if (is.vector(CP_Point) == TRUE) {
S <- Res$scores[, 1:ncp]
MQ <- S %*% t(P)
MT <- P %*% (diag(1/L)) %*% t(P)
T <- X %*% MT
Q <- sign(X - MQ) * (X - MQ)^2
colnames(T) <- colnames(data)
colnames(Q) <- colnames(data)
Ti <- T[CP_Point, ]
Qi <- Q[CP_Point, ]
lim_Qi <- max(abs(min(Qi)), max(Qi))
lim_Ti <- max(abs(min(Ti)), max(Ti))
op <- par(mfrow = c(1, 2))
barplot(Qi, main = paste("ContrPlot Q point:",
CP_txt_Point), ylim = c(-lim_Qi, lim_Qi), col = 4,
cex.lab = 1.2, cex.names = CP_dim_var, las = CP_las,
plot.grid = TRUE, cex.axis = 0.6)
barplot(Ti, main = paste("ContrPlot Ti^2 point:",
CP_txt_Point), ylim = c(-lim_Ti, lim_Ti), col = 4,
cex.lab = 1.2, cex.names = CP_dim_var, las = CP_las,
plot.grid = TRUE, cex.axis = 0.6)
par(op)
Res <- list(dataset = X, sdev = V, sgt = sgt,
loadings = T, scores = S, center = centering,
scale = scaling, n.obs = nc, T = T, Q = Q)
}
}
}
Res
}
|
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