The CANDISC Procedure 
PROC CANDISC Statement 
The PROC CANDISC statement invokes the CANDISC procedure. The options listed in Table 27.1 are available in the PROC CANDISC statement.
Option 
Description 

Input Data Set 

specifies input SAS data set 

Output Data Sets 

specifies output data set with canonical scores 

specifies output statistics data set 

Method Details 

specifies the number of canonical variables 

specifies a prefix for naming the canonical variables 

specifies the singularity criterion 

Control Displayed Output 

displays all output 

displays univariate statistics 

displays between correlations 

displays between covariances 

displays between SSCPs 

displays squared Mahalanobis distances 

suppresses all displayed output 

displays pooled correlations 

displays pooled covariances 

displays pooled SSCPs 

suppresses some displayed output 

displays simple descriptive statistics 

displays standardized class means 

displays total correlations 

displays total covariances 

displays total SSCPs 

displays within correlations 

displays within covariances 

displays within SSCPs 
displays univariate statistics for testing the hypothesis that the class means are equal in the population for each variable.
displays betweenclass covariances. The betweenclass covariance matrix equals the betweenclass SSCP matrix divided by , where is the number of observations and is the number of classes. The betweenclass covariances should be interpreted in comparison with the totalsample and withinclass covariances, not as formal estimates of population parameters.
specifies the data set to be analyzed. The data set can be an ordinary SAS data set or one of several specially structured data sets created by SAS statistical procedures. These specially structured data sets include TYPE=CORR, COV, CSSCP, and SSCP. If you omit the DATA= option, the procedure uses the most recently created SAS data set.
displays squared Mahalanobis distances between the group means, statistics, and the corresponding probabilities of greater squared Mahalanobis distances between the group means.
specifies the number of canonical variables to be computed. The value of must be less than or equal to the number of variables. If you specify NCAN=0, the procedure displays the canonical correlations, but not the canonical coefficients, structures, or means. A negative value suppresses the canonical analysis entirely. Let be the number of variables in the VAR statement, and let be the number of classes. If you omit the NCAN= option, only canonical variables are generated; if you also specify an OUT= output data set, canonical variables are generated, and the last canonical variables have missing values.
suppresses the normal display of results. Note that this option temporarily disables the Output Delivery System (ODS); see Chapter 20, Using the Output Delivery System, for more information.
creates an output SAS data set containing the original data and the canonical variable scores. To create a permanent SAS data set, specify a twolevel name (see SAS Language Reference: Concepts), for more information about permanent SAS data sets).
creates a TYPE=CORR output SAS data set that contains various statistics, including class means, standard deviations, correlations, canonical correlations, canonical structures, canonical coefficients, and means of canonical variables for each class. To create a permanent SAS data set, specify a twolevel name (see SAS Language Reference: Concepts, for more information about permanent SAS data sets).
displays pooled withinclass correlations (partial correlations based on the pooled withinclass covariances).
specifies a prefix for naming the canonical variables. By default the names are Can1, Can2, Can3, and so forth. If you specify PREFIX=Abc, the components are named Abc1, Abc2, and so on. The number of characters in the prefix plus the number of digits required to designate the canonical variables should not exceed 32. The prefix is truncated if the combined length exceeds 32.
suppresses the display of canonical structures, canonical coefficients, and class means on canonical variables; only tables of canonical correlations and multivariate test statistics are displayed.
displays simple descriptive statistics for the total sample and within each class.
specifies the criterion for determining the singularity of the totalsample correlation matrix and the pooled withinclass covariance matrix, where . The default is SINGULAR=1E–8.
Let be the totalsample correlation matrix. If the R square for predicting a quantitative variable in the VAR statement from the variables preceding it exceeds , then is considered singular. If is singular, the probability levels for the multivariate test statistics and canonical correlations are adjusted for the number of variables with R square exceeding .
If is considered singular and the inverse of (squared Mahalanobis distances) is required, a quasi inverse is used instead. For details see the section Quasiinverse in Chapter 31, The DISCRIM Procedure.
displays totalsample and pooled withinclass standardized class means.
displays the withinclass corrected SSCP matrix for each class level.
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