plotly_compCDF: Plot the Component CDF using `plotly`

Description

Plot the components' CDF via the posterior probabilities using plotly.

Usage

plotly_compCDF(data, weights, x=seq(min(data, na.rm=TRUE), max(data, na.rm=TRUE), 
               len=250), comp=1:NCOL(weights), makeplot=TRUE,
               cex = 3, width = 3,
               legend.text = "Composition", legend.text.size = 15, legend.size = 15,
               title = "Empirical CDF", title.x = 0.5, title.y = 0.95, title.size = 15,
               xlab = "Data", xlab.size = 15, xtick.size = 15,
               ylab = "Probability", ylab.size = 15, ytick.size = 15,
               col.comp = NULL)

Value

A matrix with length(comp) rows and length(x) columns in which each row gives the CDF evaluated at each point of x.

Arguments

data: A matrix containing the raw data. Rows are subjects and columns are repeated measurements.
weights: The weights to compute the empirical CDF; however, most of time they are the posterior probabilities.
x: The points at which the CDFs are to be evaluated.
comp: The mixture components for which CDFs are desired.
makeplot: Logical: Should a plot be produced as a side effect?
cex: Size of markers.
width: Line width.
title: Text of the main title.
title.size: Size of the main title.
title.x: Horsizontal position of the main title.
title.y: Vertical posotion of the main title.
xlab: Label of X-axis.
xlab.size: Size of the lable of X-axis.
xtick.size: Size of tick lables of X-axis.
ylab: Label of Y-axis.
ylab.size: Size of the lable of Y-axis.
ytick.size: Size of tick lables of Y-axis.
legend.text: Title of legend.
legend.text.size: Size of the legend title.
legend.size: Size of legend.
col.comp: Color of compositions. Number of color specified needs to be consistent with number of compositions.

Details

When makeplot is TRUE, a line plot is produced of the CDFs evaluated at x. The plot is not a step function plot; the points \((x, CDF(x))\) are simply joined by line segments.

References

McLachlan, G. J. and Peel, D. (2000) Finite Mixture Models, John Wiley and Sons, Inc.

Elmore, R. T., Hettmansperger, T. P. and Xuan, F. (2004) The Sign Statistic, One-Way Layouts and Mixture Models, Statistical Science 19(4), 579--587.

Examples

Run this code

## The sulfur content of the coal seams in Texas
set.seed(100)
A <- c(1.51, 1.92, 1.08, 2.04, 2.14, 1.76, 1.17)
B <- c(1.69, 0.64, .9, 1.41, 1.01, .84, 1.28, 1.59)
C <- c(1.56, 1.22, 1.32, 1.39, 1.33, 1.54, 1.04, 2.25, 1.49)
D <- c(1.3, .75, 1.26, .69, .62, .9, 1.2, .32)
E <- c(.73, .8, .9, 1.24, .82, .72, .57, 1.18, .54, 1.3)
dis.coal <- makemultdata(A, B, C, D, E,
                         cuts = median(c(A, B, C, D, E)))
temp <- multmixEM(dis.coal)
## Now plot the components' CDF via the posterior probabilities
plotly_compCDF(dis.coal$x, temp$posterior, xlab="Sulfur")

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