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.
If you use a PARTIAL statement, the OUT= data set also contains the residuals from predicting the VAR variables from the PARTIAL variables. The names of the residual variables are formed by concatenating the values given by the PARPREFIX= option (the default is R_) with the numbers 1, 2, 3, and so on.
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
means
standard deviations
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’).
number of observations on which the analysis is based. This value is the same for each variable.
sum of the weights if a WEIGHT statement is used. This value is the same for each variable.
correlations. The _NAME_
variable contains the name of the variable corresponding to each row of the correlation matrix.
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.
correlations among the regression coefficient estimates
standardized regression coefficients. The _NAME_
variable contains the name of the dependent variable.
raw regression coefficients
standard errors of the regression coefficients
95% lower confidence limits for the regression coefficients
95% upper confidence limits for the regression coefficients
t statistics for the regression coefficients
probability levels for the t statistics
semipartial correlations between regressors and dependent variables
squared semipartial correlations between regressors and dependent variables
partial correlations between regressors and dependent variables
squared partial correlations between regressors and dependent variables
R squares for the multiple regression analyses
adjusted R squares
approximate 95% lower confidence limits for the R squares
approximate 95% upper confidence limits for the R squares
F statistics for the multiple regression analyses
probability levels for the F statistics
canonical correlations
standardized canonical coefficients. The _NAME_
variable contains the name of the canonical variable.
To obtain the canonical variable scores, these coefficients should be multiplied by the standardized data, using means obtained
from the observation with _TYPE_
=’MEAN’ and standard deviations obtained from the observation with _TYPE_
=’STD’.
raw canonical coefficients.
To obtain the canonical variable scores, these coefficients should be multiplied by the raw data centered by means obtained
from the observation with _TYPE_
=’MEAN’.
scoring coefficients to be applied without subtracting the mean from the raw variables. These are standardized canonical coefficients computed under a NOINT model.
To obtain the canonical variable scores, these coefficients should be multiplied by the data that are standardized by the
uncorrected standard deviations obtained from the observation with _TYPE_
=’USTD’.
canonical structure.