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Last data update: 2014.03.03

R: Plot of the obtained clustering solution

click.plot

R Documentation

Plot of the obtained clustering solution

Description

Constructs a click-plot for the clustering solution.

Usage

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)

Arguments

`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

Details

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.

Author(s)

Melnykov, V.

References

Melnykov, V. (2014) Model-based biclustering of clickstream data, accepted by Computational Statistics & Data Analysis.

Examples


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)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(ClickClust)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/ClickClust/ClickPlot.Rd_%03d_medium.png", width=480, height=480)
> ### Name: click.plot
> ### Title: Plot of the obtained clustering solution
> ### Aliases: click.plot
> ### Keywords: click-plot EM algorithm Markov model
> 
> ### ** Examples
> 
> 
> 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)
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>