Contents - Index


PURPOSE Regress a dependent vectors on one or more independent vectors, and assess significance of the r-square and regression coefficients.

DESCRIPTION The procedure is principally used to model a vector using values of other vectors.  

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. 

Dependent dataset:
Name of dataset containing the observed or dependent data:  the vector whose values are to be predicted. This is given as a column in a matrix. Data type:  Matrix.

Dependent column #:  (Default=1)
Specifies which column of the data matrix contains the dependent vector. 

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

Independent column #s:  (Default=1)
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 random permutations: (Default = 1000)
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.

Random number seed:
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.

(Output) Regression Coefficients: (Default='Coefs')
Name of file containing the regression coefficients.

(Output) Correlation Matrix:(Default= 'RegCorr')
Name of file containing the correlation matrix.

(Output) Inverse of correlation Matrix (Default='RegInv')
Name of file containing the inverse of the correlation matrix.

(Output) Predicted values and residuals. (Default='PredVals') 
Name of file containing the predicted values and residuals.

LOG FILE The correlation matrix followed by information on the model fit. This is followed by a table of regression coefficients. This table gives the Unstandardized and standardized regression coefficients for each independent variable, including the intercept, along with the proportion of random trials yielding a coefficient i) as large or larger, ii) as small or smaller and iii) as extreme as  the observed value. These values give the significance of the coefficients.


COMMENTS The program ignores missing values.