Output Data Sets

The CANCORR Procedure

Output Data Sets

OUT= Data Set

The OUT= data set contains all the variables in the original data set plus new variables containing the canonical variable scores. The number of new variables is twice that specified by the NCAN= option. The names of the new variables are formed by concatenating the values given by the VPREFIX= and WPREFIX= options (the defaults are V and W) with the numbers 1, 2, 3, and so on. The new variables have mean 0 and variance equal to 1. An OUT= data set cannot be created if the DATA= data set is TYPE=CORR, COV, FACTOR, SSCP, UCORR, or UCOV or if a PARTIAL statement is used.

OUTSTAT= Data Set

The OUTSTAT= data set is similar to the TYPE=CORR or TYPE=UCORR data set produced by the CORR procedure, but it contains several results in addition to those produced by PROC CORR.

The new data set contains the following variables:

the BY variables, if any
two new character variables, _TYPE_ and _NAME_
Intercept, if the INT option is used
the variables analyzed (those in the VAR statement and the WITH statement)

Each observation in the new data set contains some type of statistic as indicated by the _TYPE_ variable. The values of the _TYPE_ variable are as follows:

_TYPE_: Contents
USTD: uncorrected standard deviations. When you specify the NOINT option in the PROC CANCORR statement, the OUTSTAT= data set contains standard deviations not corrected for the mean (_TYPE_='USTD').
N: number of observations on which the analysis is based. This value is the same for each variable.
SUMWGT: sum of the weights if a WEIGHT statement is used. This value is the same for each variable.
CORR: correlations. The _NAME_ variable contains the name of the variable corresponding to each row of the correlation matrix.
UCORR: uncorrected correlation matrix. When you specify the NOINT option in the PROC CANCORR statement, the OUTSTAT= data set contains a matrix of correlations not corrected for the means.
CANCORR: canonical correlations
SCORE: standardized canonical coefficients. The _NAME_ variable contains the name of the canonical variable.
RAWSCORE: raw canonical coefficients
USCORE: scoring coefficients to be applied without subtracting the mean from the raw variables. These are standardized canonical coefficients computed under a NOINT model.
STRUCTUR: canonical structure
RSQUARED: R²s for the multiple regression analyses
ADJRSQ: adjusted R²s
LCLRSQ: approximate 95% lower confidence limits for the R²s
UCLRSQ: approximate 95% upper confidence limits for the R²s
F: F statistics for the multiple regression analyses
PROBF: probability levels for the F statistics
CORRB: correlations among the regression coefficient estimates
STB: standardized regression coefficients. The _NAME_ variable contains the name of the dependent variable.
B: raw regression coefficients
SEB: standard errors of the regression coefficients
LCLB: 95% lower confidence limits for the regression coefficients
MEAN: means
STD: standard deviations
UCLB: 95% upper confidence limits for the regression coefficients
T: t statistics for the regression coefficients
PROBT: probability levels for the t statistics
SPCORR: semipartial correlations between regressors and dependent variables
SQSPCORR: squared semipartial correlations between regressors and dependent variables
PCORR: partial correlations between regressors and dependent variables
SQPCORR: squared partial correlations between regressors and dependent variables

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