#Permute rows of a matrix how to

The ROW function returns the row number for each element in a matrix.Consequently, the RANPERM function can generate a permutation of the column subscripts for each row. The RANPERM function can generate n independent random permutations of a set of k elements.To efficiently permute elements within each row, I will use three facts: That approach is fine for small data sets, but it is not a vectorized operation. One approach to permute the elements of each row would be to loop over the rows and apply the RANPERM function to each row. Let's start by defining some data and reading the data into a SAS/IML matrix:Д¤5 50 55 42 42 45 36 41 43 39 35 40 51 55 59 44 49 56 The purpose of this article is to provide an efficient way to permute elements within each row of a matrix. (That is, the data are stored in 'wide form,' as opposed to the 'long form' that would be used by the ANOVA or GLM procedures.) One way to implement a permutation test for ANOVA is to apply a permutation to the k elements in each row. In a matrix language such as SAS/IML, data is often packed into a matrix with n rows and k columns. Randomly shuffled (permuted) and a new value of could be Identifying them as belonging to a particular group, could be That is, the labels that are associated with particular values, Observations are exchangeable between the different groups. Hypothesis is true and the groups are not really different (in

#Permute rows of a matrix how to

Recently,ДЄ SAS user asked how to create a permutation test that compares the means of k groups.ДЄn excellent overview of permutation tests in the ANOVA context is provided by M. The matched-pair permutation test enables you to determine whether the means of the two groups are significantly different. I have described a SAS/IML implementation of a bootstrap permutation test for matched pairs of data (an alternative to a matched-pair t test) in my paper 'Modern Data Analysis for the Practicing Statistician' (Wicklin, 2010, pp 11вЂ“14).

Bootstrap methods and permutation tests are popular and powerful nonparametric methods for testing hypotheses and approximating the sampling distribution of a statistic.