# Plot of the obtained clustering solution Constructs a click-plot for the clustering solution. ```r 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. ## References 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. ## Author(s) Melnykov, V. ## See Also click.EM ## Examples ```r 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) ```

ClickPlot function