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.
You determine the number of new variables using the NCAN= option.
The names of the new variables are formed
as described in the PREFIX= option.
The new variables have means equal to zero and
pooled within-class variances equal to one.
An OUT= data set cannot be created if the DATA=
data set is not an ordinary SAS data set.
OUTSTAT= Data Set
The OUTSTAT= data set is similar to the TYPE=CORR data set
produced by the CORR procedure but contains many results
in addition to those produced by the CORR procedure.
The OUTSTAT= data set is TYPE=CORR, and it
contains the following variables:
- the BY variables, if any
- the CLASS variable
- _TYPE_, a character variable of length 8 that identifies
the type of statistic
- _NAME_, a character variable of length 32 that identifies
the row of the matrix or the name of the canonical variable
- the quantitative variables (those in the VAR
statement, or if there is no VAR statement, all numeric
variables not listed in any other statement)
The observations, as identified by the variable
_TYPE_, have the following _TYPE_ values:
- _TYPE_
- Contents
- N
- number of observations for both the total sample (CLASS
variable missing) and within each class (CLASS variable present)
- SUMWGT
- sum of weights for both the total sample (CLASS variable missing)
and within each class (CLASS variable present)
if a WEIGHT statement is specified
- MEAN
- means for both the total sample (CLASS variable missing)
and within each class (CLASS variable present)
- STDMEAN
- total-standardized class means
- PSTDMEAN
- pooled within-class standardized class means
- STD
- standard deviations for both the total sample (CLASS variable
missing) and within each class (CLASS variable present)
- PSTD
- pooled within-class standard deviations
- BSTD
- between-class standard deviations
- RSQUARED
- univariate R2s
The following kinds of observations are identified by
the combination of the variables _TYPE_ and _NAME_.
When the _TYPE_ variable has one of the following values,
the _NAME_ variable identifies the row of the matrix.
- _TYPE_
- Contents
- CSSCP
- corrected SSCP matrix for the total sample (CLASS variable
missing) and within each class (CLASS variable present)
- PSSCP
- pooled within-class corrected SSCP matrix
- BSSCP
- between-class SSCP matrix
- COV
- covariance matrix for the total sample (CLASS variable missing)
and within each class (CLASS variable present)
- PCOV
- pooled within-class covariance matrix
- BCOV
- between-class covariance matrix
- CORR
- correlation matrix
for the total sample (CLASS variable missing)
and within each class (CLASS variable present)
- PCORR
- pooled within-class correlation matrix
- BCORR
- between-class correlation matrix
When the _TYPE_ variable has one of the following values,
the _NAME_ variable identifies the canonical variable:
- _TYPE_
- Contents
- CANCORR
- canonical correlations
- STRUCTUR
- canonical structure
- BSTRUCT
- between canonical structure
- PSTRUCT
- pooled within-class canonical structure
- SCORE
- total sample standardized canonical coefficients
- PSCORE
- pooled within-class standardized canonical coefficients
- RAWSCORE
- raw canonical coefficients
- CANMEAN
- means of the canonical variables for each class
Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
