Syntax | SAS/STAT 9.1 Users Guide, Volumes 1-7

The following statements are available in PROC CANCORR.

PROC CANCORR < options > ;
- WITH variables ;
- BY variables ;
- FREQ variable ;
- PARTIAL variables ;
- VAR variables ;
- WEIGHT variable ;

The PROC CANCORR statement and the WITH statement are required. The rest of this section provides detailed syntax information for each of the preceding statements, beginning with the PROC CANCORR statement. The remaining statements are covered in alphabetical order.

PROC CANCORR Statement

PROC CANCORR < options > ;

The PROC CANCORR statement starts the CANCORR procedure and optionally identifies input and output data sets, specifies the analyses performed, and controls displayed output. Table 20.1 summarizes the options.

Table 20.1: PROC CANCORR Statement Options
Task	Options	Description
Specify computational details	EDF=	specify error degrees of freedom if input observations are regression residuals
	NOINT	omit intercept from canonical correlation and regression models
	RDF=	specify regression degrees of freedom if input observations are regression residuals
	SINGULAR=	specify the singularity criterion
Specify input and output data sets	DATA=	specify input data set name
	OUT=	specify output data set name
	OUTSTAT=	specify output data set name containing various statistics
Specify labeling options	VNAME=	specify a name to refer to VAR statement variables
	VPREFIX=	specify a prefix for naming VAR statement canonical variables
	WNAME=	specify a name to refer to WITH statement variables
	WPREFIX=	specify a prefix for naming WITH statement canonical variables
Control amount of output	ALL	produce simple statistics, input variable correlations , and canonical redundancy analysis
	CORR	produce input variable correlations
	NCAN=	specify number of canonical variables for which full output is desired
	NOPRINT	suppress all displayed output
	REDUNDANCY	produce canonical redundancy analysis
	SHORT	suppress default output from canonical analysis
	SIMPLE	produce means and standard deviations
Request regression analyses	VDEP	request multiple regression analyses with the VAR variables as dependents and the WITH variables as regressors
	VREG	request multiple regression analyses with the VAR variables as regressors and the WITH variables as dependents
	WDEP	same as VREG
	WREG	same as VDEP
Specify regression statistics	ALL	produce all regression statistics and includes these statistics in the
	OUTSTAT=	data set
	B	produce raw regression coefficients
	CLB	produce 95% confidence interval limits for the regression coefficients
	CORRB	produce correlations among regression coefficients
	INT	request statistics for the intercept when you specify the B, CLB, SEB, T, or PROBT option
	PCORR	display partial correlations between regressors and dependents
	PROBT	display probability levels for t statistics
	SEB	display standard errors of regression coefficients
	SMC	display squared multiple correlations and F tests
	SPCORR	display semipartial correlations between regressors and dependents
	SQPCORR	display squared partial correlations between regressors and dependents
	SQSPCORR	display squared semipartial correlations between regressors and dependents
	STB	display standardized regression coefficients
	T	display t statistics for regression coefficients

Following are explanations of the options that can be used in the PROC CANCORR statement (in alphabetic order):

ALL

displays simple statistics, correlations among the input variables, the confidence limits for the regression coefficients, and the canonical redundancy analysis. If you specify the VDEP or WDEP option, the ALL option displays all related regression statistics (unless the NOPRINT option is specified) and includes these statistics in the OUTSTAT= data set.

produces raw regression coefficients from the regression analyses.

CLB

produces the 95% confidence limits for the regression coefficients from the regression analyses.

CORR

produces correlations among the original variables. If you include a PARTIAL statement, the CORR option produces a correlation matrix for all variables in the analysis, the regression statistics ( R ² , RMSE), the standardized regression coefficients for both the VAR and WITH variables as predicted from the PARTIAL statement variables, and partial correlation matrices.

CORRB

produces correlations among the regression coefficient estimates.

DATA= SAS-data-set

names the SAS data set to be analyzed by PROC CANCORR. It can be an ordinary SAS data set or a TYPE=CORR, COV, FACTOR, SSCP, UCORR, or UCOV data set. By default, the procedure uses the most recently created SAS data set.

EDF= error-df

specifies the error degrees of freedom if the input observations are residuals from a regression analysis. The effective number of observations is the EDF= value plus one. If you have 100 observations, then specifying EDF=99 has the same effect as omitting the EDF= option.

INT

requests that statistics for the intercept be included when B, CLB, SEB, T, or PROBT is specified for the regression analyses.

NCAN= number

specifies the number of canonical variables for which full output is desired. The number must be less than or equal to the number of canonical variables in the analysis.

The value of the NCAN= option specifies the number of canonical variables for which canonical coefficients and canonical redundancy statistics are displayed, and the number of variables shown in the canonical structure matrices. The NCAN= option does not affect the number of displayed canonical correlations.

If an OUTSTAT= data set is requested, the NCAN= option controls the number of canonical variables for which statistics are output. If an OUT= data set is requested , the NCAN= option controls the number of canonical variables for which scores are output.

NOINT

omits the intercept from the canonical correlation and regression models. Standard deviations, variances, covariances, and correlations are not corrected for the mean. If you use a TYPE=SSCP data set as input to the CANCORR procedure and list the variable Intercept in the VAR or WITH statement, the procedure runs as if you also specified the NOINT option. If you use NOINT and also create an OUTSTAT= data set, the data set is TYPE=UCORR.

NOPRINT

suppresses the display of all output. Note that this option temporarily disables the Output Delivery System (ODS). For more information, see Chapter 14, 'Using the Output Delivery System.'

OUT= SAS-data-set

creates an output SAS data set to contain all the original data plus scores on the canonical variables. If you want to create a permanent SAS data set, you must specify a two-level name. The OUT= option cannot be used when the DATA= data set is TYPE=CORR, COV, FACTOR, SSCP, UCORR, or UCOV. For details on OUT= data sets, see the section 'Output Data Sets' on page 766. Refer to SAS Language Reference: Concepts for more information on permanent SAS data sets.

OUTSTAT= SAS-data-set

creates an output SAS data set containing various statistics, including the canonical correlations and coefficients and the multiple regression statistics you request. If you want to create a permanent SAS data set, you must specify a two-level name. For details on OUTSTAT= data sets, see the section 'Output Data Sets' on page 766. Refer to SAS Language Reference: Concepts for more information on permanent SAS data sets.

PCORR

produces partial correlations between regressors and dependent variables, removing from each dependent variable and regressor the effects of all other regressors.

PROBT

produces probability levels for the t statistics in the regression analyses.

RDF= regression-df

specifies the regression degrees of freedom if the input observations are residuals from a regression analysis. The effective number of observations is the actual number minus the RDF= value. The degrees of freedom for the intercept should not be included in the RDF= option.

REDUNDANCY

RED

produces canonical redundancy statistics.

SEB

produces standard errors of the regression coefficients.

SHORT

suppresses all default output from the canonical analysis except the tables of canonical correlations and multivariate statistics.

SIMPLE

produces means and standard deviations.

SINGULAR= p

SING= p

specifies the singularity criterion, where 0 < p < 1. If a variable in the PARTIAL statement has an R ² as large as 1- p (where p is the value of the SINGULAR= option) when predicted from the variables listed before it in the statement, the variable is assigned a standardized regression coefficient of 0, and the LOG generates a linear dependency warning message. By default, SINGULAR=1E-8.

SMC

produces squared multiple correlations and F tests for the regression analyses.

SPCORR

produces semipartial correlations between regressors and dependent variables, removing from each regressor the effects of all other regressors.

SQPCORR

produces squared partial correlations between regressors and dependent variables, removing from each dependent variable and regressor the effects of all other regressors.

SQSPCORR

produces squared semipartial correlations between regressors and dependent variables, removing from each regressor the effects of all other regressors.

STB

produces standardized regression coefficients.

produces t statistics for the regression coefficients.

VDEP

WREG

requests multiple regression analyses with the VAR variables as dependent variables and the WITH variables as regressors.

VNAME= 'label'

VN= 'label'

specifies a character constant to refer to variables from the VAR statement on the output. Enclose the constant in single quotes. If you omit the VNAME= option, these variables are referred to as the VAR Variables. The number of characters in the label should not exceed the label length defined by the VALIDVARNAME= system option. For more information on the VALIDVARNAME= system option, refer to SAS Language Reference: Dictionary .

VPREFIX= name

VP= name

specifies a prefix for naming canonical variables from the VAR statement. By default, these canonical variables are given the names V1 , V2 , and so on. If you specify VPREFIX=ABC, the names are ABC1 , ABC2 , and so forth. The number of characters in the prefix plus the number of digits required to designate the variables should not exceed the name length defined by the VALIDVARNAME= system option. For more information on the VALIDVARNAME= system option, refer to SAS Language Reference: Dictionary .

WDEP

VREG

requests multiple regression analyses with the WITH variables as dependent variables and the VAR variables as regressors.

WNAME= 'label'

WN= 'label'

specifies a character constant to refer to variables in the WITH statement on the output. Enclose the constant in quotes. If you omit the WNAME= option, these variables are referred to as the WITH Variables. The number of characters in the label should not exceed the label length defined by the VALIDVARNAME= system option. For more information, on the VALIDVARNAME= system option, refer to SAS Language Reference: Dictionary .

WPREFIX= name

WP= name

specifies a prefix for naming canonical variables from the WITH statement. By default, these canonical variables are given the names W1 , W2 , and so on. If you specify WPREFIX= XYZ , then the names are XYZ1 , XYZ2 , and so forth. The number of characters in the prefix plus the number of digits required to designate the variables should not exceed the label length defined by the VALIDVARNAME= system option. For more information, on the VALIDVARNAME= system option, refer to SAS Language Reference: Dictionary .

BY Statement

BY variables ;

You can specify a BY statement with PROC CANCORR to obtain separate analyses on observations in groups defined by the BY variables. When a BY statement appears, the procedure expects the input data set to be sorted in order of the BY variables.

If your input data set is not sorted in ascending order, use one of the following alternatives:

Sort the data using the SORT procedure with a similar BY statement.
Specify the BY statement option NOTSORTED or DESCENDING in the BY statement for the CANCORR procedure. The NOTSORTED option does not mean that the data are unsorted but rather that the data are arranged in groups (according to values of the BY variables) and that these groups are not necessarily in alphabetical or increasing numeric order.
Create an index on the BY variables using the DATASETS procedure.

For more information on the BY statement, refer to the discussion in SAS Language Reference: Concepts . For more information on the DATASETS procedure, refer to the discussion in the SAS Procedures Guide .

FREQ Statement

FREQ variable ;

If one variable in your input data set represents the frequency of occurrence for other values in the observation, specify the variable's name in a FREQ statement. PROC CANCORR then treats the data set as if each observation appeared n times, where n is the value of the FREQ variable for the observation. If the value of the FREQ variable is less than one, the observation is not used in the analysis. Only the integer portion of the value is used. The total number of observations is considered to be equal to the sum of the FREQ variable when PROC CANCORR calculates significance probabilities.

PARTIAL Statement

PARTIAL variables ;

You can use the PARTIAL statement to base the canonical analysis on partial correlations. The variables in the PARTIAL statement are partialled out of the VAR and WITH variables.

VAR Statement

VAR variables ;

The VAR statement lists the variables in the first of the two sets of variables to be analyzed. The variables must be numeric. If you omit the VAR statement, all numeric variables not mentioned in other statements make up the first set of variables. If, however, the DATA= data set is TYPE=SSCP, the default set of variables used as VAR variables does not include the variable Intercept .

WEIGHT Statement

WEIGHT variable ;

If you want to compute weighted product-moment correlation coefficients, specify the name of the weighting variable in a WEIGHT statement. The WEIGHT and FREQ statements have a similar effect, except the WEIGHT statement does not alter the degrees of freedom or number of observations. An observation is used in the analysis only if the WEIGHT variable is greater than zero.

WITH Statement

WITH variables ;

The WITH statement lists the variables in the second set of variables to be analyzed. The variables must be numeric. The WITH statement is required.