Description Usage Arguments Value Methods Author(s) References Examples
Plots true clusters if x
equals "RNGMIX"
. Plots the REBMIX output
depending on what
argument if x
equals "REBMIX"
.
Plots predictive clusters if x
equals "RCLRMIX"
.
Wrongly clustered observations are plotted only if x@Zt
is available.
Plots predictive classes and wrongly classified observations if x
equals "RCLSMIX"
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  ## S4 method for signature 'RNGMIX,missing'
plot(x, y, pos = 1, nrow = 1, ncol = 1, cex = 0.8,
fg = "black", lty = "solid", lwd = 1, pty = "m", tcl = 0.5,
plot.cex = 0.8, plot.pch = 19, ...)
## S4 method for signature 'REBMIX,missing'
plot(x, y, pos = 1, what = c("density"),
nrow = 1, ncol = 1, npts = 200, n = 200, cex = 0.8, fg = "black",
lty = "solid", lwd = 1, pty = "m", tcl = 0.5,
plot.cex = 0.8, plot.pch = 19, contour.drawlabels = FALSE,
contour.labcex = 0.8, contour.method = "flattest",
contour.nlevels = 12, ...)
## S4 method for signature 'RCLRMIX,missing'
plot(x, y, s = expression(c), nrow = 1, ncol = 1, cex = 0.8,
fg = "black", lty = "solid", lwd = 1, pty = "m", tcl = 0.5,
plot.cex = 0.8, plot.pch = 19, ...)
## S4 method for signature 'RCLSMIX,missing'
plot(x, y, nrow = 1, ncol = 1, cex = 0.8,
fg = "black", lty = "solid", lwd = 1, pty = "m", tcl = 0.5,
plot.cex = 0.8, plot.pch = 19, ...)
## ... and for other signatures

x 
see Methods section below. 
y 
currently not used. 
pos 
a desired row number in 
s 
a desired number of clusters to be plotted. The default value is 
what 
a character vector giving the plot types. One of 
nrow 
a desired number of rows in which the empirical and predictive densities are to be plotted. The default value is 
ncol 
a desired number of columns in which the empirical and predictive densities are to be plotted. The default value is 
npts 
a number of points at which the predictive densities are to be plotted. The default value is 
n 
a number of observations to be plotted. The default value is 
cex 
a numerical value giving the amount by which the plotting text and symbols should be magnified
relative to the default, see also 
fg 
a colour used for things like axes and boxes around plots, see also 
lty 
a line type, see also 
lwd 
a line width, see also 
pty 
a character specifying the type of the plot region to be used. One of 
tcl 
a length of tick marks as a fraction of the height of a line of the text, see also 
plot.cex 
a numerical vector giving the amount by which plotting characters and symbols should be
scaled relative to the default. It works as a multiple of 
plot.pch 
a vector of plotting characters or symbols, see also 
contour.drawlabels 
logical. The contours are labelled if 
contour.labcex 

contour.method 
a character specifying where the labels will be located. The possible values
are 
contour.nlevels 
a number of desired contour levels. The default value is 
... 
further arguments to 
Returns (invisibly) a list containing graphical parameters par
. Such a list can be passed as an argument to par
to restore the parameter values.
signature(x = "RNGMIX", y = "missing")
an object of class RNGMIX
.
signature(x = "RNGMVNORM", y = "missing")
an object of class RNGMVNORM
.
signature(x = "REBMIX", y = "missing")
an object of class REBMIX
.
signature(x = "REBMVNORM", y = "missing")
an object of class REBMVNORM
.
signature(x = "RCLRMIX", y = "missing")
an object of class RCLRMIX
.
signature(x = "RCLRMVNORM", y = "missing")
an object of class RCLRMVNORM
.
signature(x = "RCLSMIX", y = "missing")
an object of class RCLSMIX
.
signature(x = "RCLSMVNORM", y = "missing")
an object of class RCLSMVNORM
.
Marko Nagode
C. M. Bishop. Neural Networks for Pattern Recognition. Clarendon Press, Oxford, 1995.
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  ## Not run:
devAskNewPage(ask = TRUE)
data("wine")
colnames(wine)
# Remove Cultivar column from wine dataset.
winecolnames < !(colnames(wine) %in% "Cultivar")
wine < wine[, winecolnames]
# Determine number of dimensions d and wine dataset size n.
d < ncol(wine)
n < nrow(wine)
# Estimate number of components, component weights and component parameters.
Sturges < as.integer(1 + log2(n)) # Minimum v follows Sturges rule.
RootN < as.integer(2 * n^0.5) # Maximum v follows RootN rule.
K < c(floor(Sturges^(1/13)), ceiling(RootN^(1/13)))
wineest < REBMIX(model = "REBMVNORM",
Dataset = list(wine = wine),
Preprocessing = "Parzen window",
Criterion = "ICLBIC",
pdf = rep("normal", d),
K = K[1]:K[2])
# Plot finite mixture.
plot(wineest, what = c("density", "IC", "logL", "D"),
nrow = 2, ncol = 2, pty = "s")
## End(Not run)

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