cfluctile(x, tau0 = NULL, method = "Kendall", nsplit = NULL,
maxsplit = NULL, trafo = I, gap.prop = 0.2, floor = 0,
rev.y = FALSE, add = FALSE, shape = "r", just = "c",
dir = "b", plot = TRUE, rect.opt = list(), border =
NULL, label = TRUE, lab.opt = list(), tile.col =
hsv(0.1, 0.1, 0.1, alpha = 0.6), bg.col = "lightgrey",...)
x
."Kendall"
for Kendall's Tau, "Cohen"
for Cohen's Kappa or "WBCI"
for the Weighted Bertin Classification Criterion.tau0
is ignored.trafo = function(z) log(1+z)
.floor
will be considered for the clustering.fluctile
.col
The rectangle color.
lwd
The line width. Default is "red"
.
lty
The line type. Deborder = 0.05
.invisible(TRUE)
The algorithm cuts the data matrix once horizontally and once vertically and computes a criterion for the 2x2 table consisting of the sums of the four parts that resulted from the cuts. This is done for all possible horizontal and vertical cuts and the best combination is chosen.
Then the same procedure is applied to the bottom right submatrix and the top left submatrix. The algorithms stops if no cut yields a better criterion value than tau0
.
M <- arsim(10000,c(30,40),8, noise = 0.4)
cfluctile( M2 <- optile(M,iter=20) )
cfluctile( M3 <- sortandcut(M) )
cfluctile( M3, nsplit = 4 )
cfluctile( M3, maxsplit = 12 )
cfluctile( M3, tau0 = 0.8 )
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