mixtools (version 1.0.4)

compCDF: 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.

Value

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

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 \& 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")