Name of file containing matrix to be analyzed. Data type: Matrix.

Name of file containing actor attributes, given as a vector of shared attributes so that (1,2,3,1,2,2) means that actors 1 and 4 share the same attribute actors 2,5,and 6 share the same attribute and actor 3 has a different attribute from all the others.

Choices are:

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

If Yes, the values along the main diagonals of each matrix are included in the computations. Otherwise, they are treated as missing.

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.

The between group and in-group means are reported if either of the homophily models were chosen. For constant homophily the in-group mean is the overall mean of all within group interactions. For variable homophily each separate within group mean is reported. For the structural blockmodels option the total sum, the average value and the number of cells within each block are reported. In all cases this is followed by the value of the autocorrelation together with the r-squared value, the root mean square and the sum of squares. Below this is the autocorrelation averaged over all the permutations together with the standard error. Finally the proportion of random values which are as large as the actual autocorrelation is reported. This gives the significance of the calculated value, so for example if this were below 0.05 we would conclude at the 5% level that the dyadic variable is related to the categorical attribute.