# Produce Cutpoint Multinomial Data 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. ```r 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 `NA`s 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. ## Returns `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. ## 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]. ## See Also `compCDF`, `multmixmodel.sel`, `multmixEM` ## 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. ## Examples ```r ## 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,] ```

makemultdata function