R/Examples_vignettes.R

In DawnTrisha/hclustHDCP: High Dimensional Change-point detection based on Hierarchical clustering

#
# X1 = matrix(rnorm(8*20, mean = 0, sd = 1), nrow = 8, ncol = 20)
# X2 = matrix(rnorm(8*20, mean = 3, sd = 1), nrow = 8, ncol = 20)
# X3 = matrix(rnorm(8*20, mean = 6, sd = 1), nrow = 8, ncol = 20)
#
#
# X = rbind(X1, X2, X3)
#
# X.pca  = prcomp(X, center = T, scale. = T)
# M = X.pca$x[ , 1]
#
# par(mar=c(3.75,3.4,2,1),mgp=c(2.3,0.5,0),oma=c(0.5,0.5,0.5,0.5),mfrow=c(2,2))
#
# plot(M, pch = 1, xlim = c(1,24), ylim = c(-8,8),
#      xlab = "t", ylab = expression(paste("PC"[1])),
#      #xaxt = "n",
#      #yaxt = "n",
#      axes = F,
#      #frame.plot = F, main = expression(paste("(a) ", mu," = 0", " (Before Clustering)"))
#      frame.plot = F, main = expression(paste("(a) ", " Before Clustering")))
# axis(side = 1, at = c(1,8,16,24), tck = -0.03)
# axis(side = 2, at = c(-8,0,8), las = 2, tck = -0.03)
# #points(X2, pch = 16)
# #abline(h = -3, v = 0)
# box()
#
# cl1=cutree(hclust(dist(X),method="average"),k=3)
# cl1
# which(cl1==1)
# which(cl1==2)
# which(cl1==3)
#
# z1 = as.vector(which(cl1==1))
# z2 = as.vector(which(cl1==2))
# z3 = as.vector(which(cl1==3))
# y1 = NA
# y2 = NA
# y3 = NA
# y1[z1] = -8
# y2[z2] = -8
# y3[z3] = -8
#
# v1 = NA
# v2 = NA
# v3 = NA
# v1[z1] = M[z1]
# v2[z2] = M[z2]
# v3[z3] = M[z3]
#
# plot(y1, pch = 16, xlim = c(1,24), ylim = c(-8,8),
#      xlab = "t", ylab = expression(paste("PC"[1])),
#      #xaxt = "n",
#      #yaxt = "n",
#      axes = F,
#      frame.plot = F, col = "red", main = expression(paste("(b) ", "Average Linkage")))
# axis(side = 1, at = c(1,8,16,24),tck = -0.03)
# axis(side = 2, at = c(-8,0,8), las = 2, tck = -0.03)
# points(y2, pch = 16, col = "blue")
# points(y3, pch = 16, col = "green")
# points(v1, pch = 16, col = "red")
# points(v2, pch = 16, col = "blue")
# points(v3, pch = 16, col = "green")
# #abline(h = -3, v = 0)
# box()
#
# ###########################
#
#
# X1 = matrix(rnorm(8*20, mean = 0, sd = 1), nrow = 8, ncol = 20)
# X2 = matrix(rnorm(8*20, mean = 3, sd = 1), nrow = 8, ncol = 20)
# X3 = matrix(rnorm(8*20, mean = 0, sd = 1), nrow = 8, ncol = 20)
#
#
# X = rbind(X1, X2, X3)
#
# X.pca  = prcomp(X, center = T, scale. = T)
# M = X.pca$x[ , 1]
#
# par(mar=c(3.75,3.4,2,1),mgp=c(2.3,0.5,0),oma=c(0.5,0.5,0.5,0.5),mfrow=c(2,2))
#
# plot(M, pch = 1, xlim = c(1,24), ylim = c(-8,8),
#      xlab = "t", ylab = expression(paste("PC"[1])),
#      #xaxt = "n",
#      #yaxt = "n",
#      axes = F,
#      #frame.plot = F, main = expression(paste("(a) ", mu," = 0", " (Before Clustering)"))
#      frame.plot = F, main = expression(paste("(a) ", " Before Clustering")))
# axis(side = 1, at = c(1,8,16,24), tck = -0.03)
# axis(side = 2, at = c(-8,0,8), las = 2, tck = -0.03)
# #points(X2, pch = 16)
# #abline(h = -3, v = 0)
# box()
#
# cl1=cutree(hclust(dist(X),method="average"),k=3)
# cl1
# which(cl1==1)
# which(cl1==2)
# which(cl1==3)
#
# z1 = as.vector(which(cl1==1))
# z2 = as.vector(which(cl1==2))
# z3 = as.vector(which(cl1==3))
# y1 = NA
# y2 = NA
# y3 = NA
# y1[z1] = -8
# y2[z2] = -8
# y3[z3] = -8
#
# v1 = NA
# v2 = NA
# v3 = NA
# v1[z1] = M[z1]
# v2[z2] = M[z2]
# v3[z3] = M[z3]
#
# plot(y1, pch = 16, xlim = c(1,24), ylim = c(-8,8),
#      xlab = "t", ylab = expression(paste("PC"[1])),
#      #xaxt = "n",
#      #yaxt = "n",
#      axes = F,
#      frame.plot = F, col = "red", main = expression(paste("(b) ", "Average Linkage")))
# axis(side = 1, at = c(1,8,16,24),tck = -0.03)
# axis(side = 2, at = c(-8,0,8), las = 2, tck = -0.03)
# points(y2, pch = 16, col = "blue")
# points(y3, pch = 16, col = "green")
# points(v1, pch = 16, col = "red")
# points(v2, pch = 16, col = "blue")
# points(v3, pch = 16, col = "green")
# #abline(h = -3, v = 0)
# box()
#
# detect_multiple_cp(X = X, numcp = 2)

# Example 1 - single change-point
# X1 = matrix(rnorm(10 * 150, mean = 0, sd = 1), nrow = 10, ncol = 150)
# X2 = matrix(rnorm(20 * 150, mean = 1, sd = 1), nrow = 20, ncol = 150)
# X = rbind(X1, X2)
#
# detect_single_cp(X = X, dist.method = "complete")
#
# # Example 2 - Multiple change-point
# X1 = matrix(rnorm(10 * 150, mean = 0, sd = 1), nrow = 10, ncol = 150)
# X2 = matrix(rnorm(10 * 150, mean = 5, sd = 1), nrow = 10, ncol = 150)
# X3 = matrix(rnorm(10 * 150, mean = 0.2, sd = 1), nrow = 10, ncol = 150)
# X = rbind(X1, X2, X3)
#
# detect_multiple_cp(X = X, numcp = 2)

# # Example 2 - Multiple change-point
# X1 = matrix(rnorm(10 * 150, mean = 0, sd = 1), nrow = 10, ncol = 150)
# X2 = matrix(rnorm(10 * 150, mean = 5, sd = 1), nrow = 10, ncol = 150)
# X3 = matrix(rnorm(10 * 150, mean = 0, sd = 1), nrow = 10, ncol = 150)
# X = rbind(X1, X2, X3)
#
# detect_estimated_cp(X = X)


# set.seed(0)
# X1 = matrix(rnorm(15 * 200, mean = 0, sd = 1), nrow = 15, ncol = 200)
# X2 = matrix(rnorm(15 * 200, mean = 0, sd = 1.5), nrow = 15, ncol = 200)
# X3 = matrix(rnorm(15 * 200, mean = 0, sd = 2), nrow = 15, ncol = 200)
# X = rbind(X1, X2, X3)
#
# detect_estimated_cp(X = X)
#
# D_mat = distmat_HDLSS(X = X, option = "MADD")
#
# detect_estimated_cp(D = D_mat, p = 200)
#
# set.seed(1)
# X1 = matrix(rnorm((15 * 250), mean = 0, sd = 2), nrow = 15, ncol = 250)
# X2 = matrix(rt((15 * 250), ncp = 0, df = 4), nrow = 15, ncol = 250)
# X = rbind(X1, X2)
#
# D1_mat = distmat_HDLSS(X = X, option = "MADD")
#
# D2_mat = distmat_HDLSS(X = X, option = "genMADD")
#
# detect_estimated_cp(D = D1_mat, p = 250)
# detect_estimated_cp(D = D2_mat, p = 250)