compCDF | R Documentation |
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 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.
makemultdata
, multmixmodel.sel
, multmixEM
.
## 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")
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