# Simulated Example

    library(MAP)

    ## Loading required package: flexmix

    ## Loading required package: lattice

    ## Loading required package: Matrix

simulate data to test the algorithm

    set.seed(123)
    n = 400
    ICD = c(rpois(n / 4, 10), rpois(n / 4, 1), rep(0, n / 2))
    NLP = c(rpois(n / 4, 10), rpois(n / 4, 1), rep(0, n / 2))
    mat = Matrix(data = cbind(ICD, NLP), sparse = TRUE)
    note = Matrix(rpois(n, 10) + 5, ncol = 1, sparse = TRUE)
    res = MAP(mat = mat, note = note)

    ## #######################
    ## MAP only considers patients who have note count data and
    ##         at least one non-missing variable
    ## ####
    ## Here is a summary of the input data:
    ## Total number of patients: 400
    ##   ICD NLP note Freq
    ## 1 YES YES  YES  400
    ## ####

    head(res$scores)

    ## 6 x 1 sparse Matrix of class "dgCMatrix"
    ##
    ## [1,] 0.9989973
    ## [2,] 0.9999445
    ## [3,] 1.0000000
    ## [4,] 0.9999906
    ## [5,] 0.9993466
    ## [6,] 0.9999966

    res$cut.MAP

    ## [1] 0.1252229