The HPCORR Procedure

prints the n highest correlation coefficients for each variable. The value of n must be greater than or equal to 1. Correlations are ordered from highest to lowest in absolute value. If you do not specify this option, PROC HPCORR prints correlations in a rectangular table, using the variable names as row and column labels.

displays the variance/covariance matrix. When you specify this option, the Pearson correlations are also displayed. If you specify the OUTP= option, the output data set also contains the covariance matrix with the corresponding _TYPE_ variable value COV.

displays a table of the corrected sums of squares and crossproducts. When you specify this option, the Pearson correlations are also displayed. If you specify the OUTP= option, the output data set also contains a CSSCP matrix with the corresponding _TYPE_ variable value CSSCP.

names the SAS data set to be analyzed by PROC HPCORR. If you do not specify this option, PROC HPCORR uses the most recently created SAS data set.

excludes from the analysis observations that have nonpositive weight values. If you do not specify this option, PROC HPCORR treats observations with negative weights like those with zero weights and counts them in the total number of observations.

suppresses the display of the Pearson correlations. If you specify the OUTP= option, the data set _TYPE_ remains CORR. To change the data set type to COV, CSSCP, or SSCP, use the (TYPE=) data set option.

excludes from the analysis observations that have missing values. If you do not specify this option, PROC HPCORR computes correlation statistics by using all of the nonmissing pairs of variables. Using this option is computationally more efficient.

suppresses all displayed output. Use this option if you only want to create an output data set.

suppresses the display of the probabilities that are associated with each correlation coefficient.

suppresses the printing of simple descriptive statistics for each variable. However, if you request an output data set, the output data set still contains simple descriptive statistics for the variables.

creates an output data set that contains Pearson correlation statistics. This data set also includes means, standard deviations, and the number of observations. The value of the _TYPE_ variable is CORR.

displays the ordered correlation coefficients for each variable. Correlations are ordered from highest to lowest in absolute value.

displays a table of the sums of squares and crossproducts. When you specify this option, the Pearson correlations are also displayed. If you specify the OUTP= option, the output data set contains a SSCP matrix and the corresponding _TYPE_ variable value is SSCP.

specifies the variance divisor in the calculation of variances and covariances. The default is VARDEF=DF.

Table 4.2 shows the available values and associated divisors for the VARDEF= option, where n is the number of nonmissing observations and is the weight associated with the jth nonmissing observation.