Plot the Component CDF
Plot the components' CDF via the posterior probabilities.
compCDF(data, weights,
x=seq(min(data, na.rm=TRUE), max(data, na.rm=TRUE), len=250),
comp=1:NCOL(weights), makeplot=TRUE, ...)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 |
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.
A matrix with length(comp) rows and length(x) columns
in which each row gives the CDF evaluated at each point of x.
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.
## 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")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.