The algorithm proceeds in two steps. In the first step, it performs a standard multiple regression across corresponding cells of the dependent and independent vectors.

In the second step, it randomly permutes rows the elements of the dependent vector and recomputes the regression, storing resultant values of r-square and all coefficients. This step is repeated hundreds of times in order to estimate standard errors for the statistics of interest. For each coefficient, the program counts the proportion of random permutations that yielded a coefficient as extreme as the one computed in step 1.

Specifies which column of the data matrix contains the dependent vector.

Names of dataset containing the independent vectors. All independent vectors must be contained in a single matrix. Data type: Matrix.

Specifies which columns of the independent dataset contain the independent vectors. Columns to be selected are specified by a list. Each column number is listed separated by a comma or space. The keywords TO, FIRST and LAST are permissible. Hence FIRST 3, 5 TO 7, 10, 12 would give column numbers 1, 2, 3, 5, 6, 7, 10 and 12. ALL gives all possible columns. Lists kept in a UCINET dataset can be used. Enter the filename followed by ROW (or COLUMN) and a number to specify which row or column of the file to use.The list must be specified using a binary vector where a 1 in position k indicates that vertex k is a member of the list, a zero indicates that k is not a member.

Number of regressions to compute between the original data and the randomly permuted data. The larger the number of permutations, the better the estimates of standard error and "significance", but the longer the computation time.

The random number seed sets off the random permutations. UCINET generates a different random number as default each time it is run. This number should be changed if the user wishes to repeat an analysis. The range is 1 to 32000.

Name of file containing the regression coefficients.

Name of file containing the correlation matrix.

Name of file containing the inverse of the correlation matrix.

Name of file containing the predicted values and residuals.