compCDF {mixtools}

Plot the Component CDF

Description

Plot the components' CDF via the posterior probabilities.

Usage

compCDF(data, weights, 
        x=seq(min(data, na.rm=TRUE), max(data, na.rm=TRUE), len=250), 
        comp=1:NCOL(weights), makeplot=TRUE, ...) 

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?

...

Additional arguments (other than lty and type, which are already used) to be passed directly to plot and lines functions.

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.

Value

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

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.

See Also

makemultdata, multmixmodel.sel, multmixEM.

Examples

## 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

compCDF(dis.coal$x, temp$posterior, xlab="Sulfur", ylab="", main="empirical CDFs")