Description Usage Arguments Details Author(s) References See Also Examples
Constructs a click-plot for the clustering solution.
1 2 3 4 5 6 | click.plot(X, y = NULL, file = NULL, id, states = NULL, marg = 1,
font.cex = 2, font.col = "black", cell.cex = 1, cell.lwd = 1.3,
cell.col = "black", sep.lwd = 1.3, sep.col = "black",
obs.lwd = NULL, colors = c("lightcyan", "pink", "darkred"),
col.levels = 8, legend = TRUE, leg.cex = 1.3, top.srt = 0,
frame = TRUE)
|
X |
dataset array (p x p x n) |
y |
vector of initial states (length n) |
file |
name of the output pdf-file |
id |
classification vector (length n) |
states |
vector of state labels (length p) |
marg |
plot margin value (for the left and top) |
font.cex |
magnification of labels |
font.col |
color of labels |
cell.cex |
magnification of cells |
cell.lwd |
width of cell frames |
cell.col |
color of cell frames |
sep.lwd |
width of separator lines |
sep.col |
color of separator lines |
obs.lwd |
width of observation lines |
colors |
edge colors for interpolation |
col.levels |
number of colors obtained by interpolation |
legend |
legend of color hues |
leg.cex |
magnification of legend labels |
top.srt |
rotation of state names in the top |
frame |
frame around the plot |
Constructs a click-plot for the provided clustering solution. Click-plot is a graphical display representing relative transition frequencies for the partitioning specified via the parameter 'id'. If the parameter 'file' is specified, the constructed plot will be saved in the pdf-file with the name 'file'. If the width of observation lines 'obs.lwd' is not specified, median colors will be used for all cell segments.
Melnykov, V.
Melnykov, V. (2016) Model-Based Biclustering of Clickstream Data, Computational Statistics and Data Analysis, 93, 31-45.
Melnykov, V. (2016) ClickClust: An R Package for Model-Based Clustering of Categorical Sequences, Journal of Statistical Software, 74, 1-34.
click.EM
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 | set.seed(123)
n.seq <- 200
p <- 5
K <- 2
mix.prop <- c(0.3, 0.7)
TP1 <- matrix(c(0.20, 0.10, 0.15, 0.15, 0.40,
0.20, 0.20, 0.20, 0.20, 0.20,
0.15, 0.10, 0.20, 0.20, 0.35,
0.15, 0.10, 0.20, 0.20, 0.35,
0.30, 0.30, 0.10, 0.10, 0.20), byrow = TRUE, ncol = p)
TP2 <- matrix(c(0.15, 0.15, 0.20, 0.20, 0.30,
0.20, 0.10, 0.30, 0.30, 0.10,
0.25, 0.20, 0.15, 0.15, 0.25,
0.25, 0.20, 0.15, 0.15, 0.25,
0.10, 0.30, 0.20, 0.20, 0.20), byrow = TRUE, ncol = p)
TP <- array(rep(NA, p * p * K), c(p, p, K))
TP[,,1] <- TP1
TP[,,2] <- TP2
# DATA SIMULATION
A <- click.sim(n = n.seq, int = c(10, 50), alpha = mix.prop, gamma = TP)
C <- click.read(A$S)
# EM ALGORITHM
M2 <- click.EM(X = C$X, y = C$y, K = 2)
# CONSTRUCT CLICK-PLOT
click.plot(X = C$X, y = C$y, file = NULL, id = M2$id)
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