The CORR Procedure

Example 2: Computing Rectangular Correlation Statistics with Missing Data

Procedure features:

PROC CORR statement options:

	COV
	NOSIMPLE
	SSCP

VAR statement

WITH statement

This example

suppresses descriptive statistics
prints uncorrected sum-of-squares and crossproducts
calculates a rectangular covariance matrix
calculates a rectangular correlation matrix
excludes observations with missing values using pairwise deletion (default method).

Program

options nodate pageno=1 linesize=80 pagesize=60;

data setosa;
   input SepalLength SepalWidth PetalLength PetalWidth @@;
   label sepallength='Sepal Length in mm.'
         sepalwidth='Sepal Width in mm.'
         petallength='Petal Length in mm.'
         petalwidth='Petal Width in mm.';
   datalines;
50 33 14 02   46 34 14 03   46 36 .  02
51 33 17 05   55 35 13 02   48 31 16 02
52 34 14 02   49 36 14 01   44 32 13 02
50 35 16 06   44 30 13 02   47 32 16 02
48 30 14 03   51 38 16 02   48 34 19 02
50 30 16 02   50 32 12 02   43 30 11 .
58 40 12 02   51 38 19 04   49 30 14 02
51 35 14 02   50 34 16 04   46 32 14 02
57 44 15 04   50 36 14 02   54 34 15 04
52 41 15 .    55 42 14 02   49 31 15 02
54 39 17 04   50 34 15 02   44 29 14 02
47 32 13 02   46 31 15 02   51 34 15 02
50 35 13 03   49 31 15 01   54 37 15 02
54 39 13 04   51 35 14 03   48 34 16 02
48 30 14 01   45 23 13 03   57 38 17 03
51 38 15 03   54 34 17 02   51 37 15 04
52 35 15 02   53 37 15 02
;

proc corr data=setosa sscp cov nosimple;

   var sepallength sepalwidth;
   with petallength petalwidth;

   title 'Fisher (1936) Iris Setosa Data';
run;

Output

The correlation report includes rectangular sum-of-squares and crossproducts, covariances, and the correlation matrix using the two WITH variables and two VAR variables. The descriptive statistics do not appear. PROC CORR uses variable labels to label matrix rows (WITH variables).
PROC CORR calculates sum-of-squares and crossproducts and covariances statistics for each pair of variables by using observations with nonmissing row and column variable values.
Because missing data are excluded pairwise, the number of observations PROC CORR uses to calculate the correlation coefficients changes.

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