# stab.blockSeg: 'stab.blockSeg' algorithm In blockseg: Two Dimensional Change-Points Detection

## Description

Model selection for the `blockSeg` algorithm.

## Usage

 ```1 2 3``` ```stab.blockSeg(Y, nsimu, max.break, max.var = floor(ncol(Y)^2/8), random.break = TRUE, sym.break = FALSE, mc.cores = 2, verbose = TRUE) ```

## Arguments

 `Y` matrix of observations. `nsimu` a positive integer. `max.break` a positive integer less than number of columns divided by 2 and number of rows divided by 2. `max.var` a positive integer less than number of columns times number of rows. By default, `ncol(Y)**2/8`. `random.break` logical. To change the position of the first row (resp. column); the rows before this position are moved to the end. By default TRUE. `sym.break` logical. In the case of symmetric matrices, it is possible to accumulate breaks in row and columns to improve the quality of the estimation. By default FALSE. Warning: a check is made on the dimensions of the matrix but not on the fact that it is symmetrical or not; this choice was made for the case where the user would like to have symmetrical breaks even if the matrix is not (not recommended by the authors of the package). `mc.cores` a positive integer giving the number of cores used. If you use windows, the parallelization is impossible. By default, 2 `verbose` logical. To display each step. By default TRUE.

## Examples

 ```1 2 3 4 5 6``` ``` ## model parameters n <- 100 K <- 5 mu <- suppressWarnings(matrix(rep(c(1,0),ceiling(K**2/2)), K,K)) Y <- rblockdata(n,mu,sigma=.5)\$Y res <- stab.blockSeg(Y, 100, 20) ```

blockseg documentation built on May 2, 2019, 6:10 a.m.