compCDF {mixtools} | R Documentation |
Plot the components' CDF via the posterior probabilities.
compCDF(x, weights)
x |
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. |
compCDF
returns an object which is a list with components:
result |
The component means and standard deviations for a k-component mixture. |
plot |
The plotted component CDF. |
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.
makemultdata
, multmixmodel.sel
, multmixEM
.
## The sulfur content of the coal seams in Texas 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$y, lambda = dis.coal$lambda, ## theta = dis.coal$theta) ## Now plot the components' CDF via the posterior probabilities ## compCDF(dis.coal$x, temp$posterior)