makemultdata {mixtools}

Produce Cutpoint Multinomial Data

Description

Change data into a matrix of multinomial counts using the cutpoint method and generate EM algorithm starting values for a k-component mixture of multinomials.

Usage

makemultdata(..., cuts)

Arguments

...

Either vectors (possibly of different lengths) of raw data or an nxm matrix (or data frame) of data. If ... are vectors of varying length, then makemultdata will create a matrix of size nxm where n is the sample size and m is the length of the vector with maximum length. Those vectors with length less than m will have NAs to make the corresponding row in the matrix of length m. If ... is a matrix (or data frame), then the rows must correspond to the sample and the columns the repeated measures.

cuts

A vector of cutpoints. This vector is sorted by the algorithm.

Details

The (i, j)th entry of the matrix y (for j < p) is equal to the number of entries in the ith column of x that are less than or equal to cuts[j]. The (i, p)th entry is equal to the number of entries greater than cuts[j].

Value

makemultdata returns an object which is a list with components:

x

An nxm matrix of the raw data.

y

An nxp matrix of the discretized data where p is one more than the number of cutpoints. Each row is a multinomial vector of counts. In particular, each row should sum to the number of repeated measures for that sample.

References

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

compCDF, multmixmodel.sel, multmixEM

Examples

## Randomly generated data.

set.seed(100)
y <- matrix(rpois(70, 6), 10, 7)
cuts <- c(2, 5, 7)
out1 <- makemultdata(y, cuts = cuts)
out1

## 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)

out2 <- makemultdata(A, B, C, D, E, 
                     cuts = median(c(A, B, C, D, E)))
out2

## The reaction time data.

data(RTdata)
out3 <- makemultdata(RTdata, cuts = 
                     100*c(5, 10, 12, 14, 16, 20, 25, 30, 40, 50))
dim(out3$y)
out3$y[1:10,]