The DISCRIM Procedure

Output Data Sets

Subsections:

When an output data set includes variables containing the posterior probabilities of group membership (OUT=, OUTCROSS=, or TESTOUT= data sets) or group-specific density estimates (OUTD= or TESTOUTD= data sets), the names of these variables are constructed from the formatted values of the class levels converted to valid SAS variable names.

OUT= Data Set

The OUT= data set contains all the variables in the DATA= data set, plus new variables containing the posterior probabilities and the resubstitution classification results. The names of the new variables containing the posterior probabilities are constructed from the formatted values of the class levels converted to SAS names. A new variable, _INTO_, with the same attributes as the CLASS variable, specifies the class to which each observation is assigned. If an observation is labeled as ’Other’, the variable _INTO_ has a missing value. When you specify the CANONICAL option, the data set also contains new variables with canonical variable scores. The NCAN= option determines the number of canonical variables. The names of the canonical variables are constructed as described in the CANPREFIX= option. The canonical 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.

OUTD= Data Set

The OUTD= data set contains all the variables in the DATA= data set, plus new variables containing the group-specific density estimates. The names of the new variables containing the density estimates are constructed from the formatted values of the class levels.

An OUTD= data set cannot be created if the DATA= data set is not an ordinary SAS data set.

OUTCROSS= Data Set

The OUTCROSS= data set contains all the variables in the DATA= data set, plus new variables containing the posterior probabilities and the classification results of cross validation. The names of the new variables containing the posterior probabilities are constructed from the formatted values of the class levels. A new variable, _INTO_, with the same attributes as the CLASS variable, specifies the class to which each observation is assigned. When an observation is labeled as ’Other’, the variable _INTO_ has a missing value. When you specify the CANONICAL option, the data set also contains new variables with canonical variable scores. The NCAN= option determines the number of new variables. The names of the new variables are constructed as described in the CANPREFIX= option. The new variables have mean zero and pooled within-class variance equal to one.

An OUTCROSS= data set cannot be created if the DATA= data set is not an ordinary SAS data set.

TESTOUT= Data Set

The TESTOUT= data set contains all the variables in the TESTDATA= data set, plus new variables containing the posterior probabilities and the classification results. The names of the new variables containing the posterior probabilities are formed from the formatted values of the class levels. A new variable, _INTO_, with the same attributes as the CLASS variable, gives the class to which each observation is assigned. If an observation is labeled as ’Other’, the variable _INTO_ has a missing value. When you specify the CANONICAL option, the data set also contains new variables with canonical variable scores. The NCAN= option determines the number of new variables. The names of the new variables are formed as described in the CANPREFIX= option.

TESTOUTD= Data Set

The TESTOUTD= data set contains all the variables in the TESTDATA= data set, plus new variables containing the group-specific density estimates. The names of the new variables containing the density estimates are formed from the formatted values of the class levels.

OUTSTAT= Data Set

The OUTSTAT= data set is similar to the TYPE=CORR data set produced by the CORR procedure. The data set contains various statistics such as means, standard deviations, and correlations. For an example of an OUTSTAT= data set, see Example 35.3. When you specify the CANONICAL option, canonical correlations, canonical structures, canonical coefficients, and means of canonical variables for each class are included in the data set.

If you specify METHOD=NORMAL, the output data set also includes coefficients of the discriminant functions, and the data set is TYPE=LINEAR (POOL=YES), TYPE=QUAD (POOL=NO), or TYPE=MIXED (POOL=TEST). If you specify METHOD=NPAR, this output data set is TYPE=CORR.

The OUTSTAT= data set 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, the name of the canonical variable, or the type of the discriminant function coefficients

  • the quantitative variables—that is, 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 values:

_TYPE_

Contents

N

number of observations both for the total sample (CLASS variable missing) and within each class (CLASS variable present)

SUMWGT

sum of weights both for the total sample (CLASS variable missing) and within each class (CLASS variable present), if a WEIGHT statement is specified

MEAN

means both for the total sample (CLASS variable missing) and within each class (CLASS variable present)

PRIOR

prior probability for each class

STDMEAN

total-standardized class means

PSTDMEAN

pooled within-class standardized class means

STD

standard deviations both for 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 R squares

LNDETERM

the natural log of the determinant or the natural log of the quasi determinant of the within-class covariance matrix either pooled (CLASS variable missing) or not pooled (CLASS variable present)

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 both 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 both 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 both 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 you request canonical discriminant analysis, the _NAME_ variable identifies a canonical variable, and _TYPE_ variable can have one of the following values:

_TYPE_

Contents

CANCORR

canonical correlations

STRUCTUR

canonical structure

BSTRUCT

between canonical structure

PSTRUCT

pooled within-class canonical structure

SCORE

standardized canonical coefficients

RAWSCORE

raw canonical coefficients

CANMEAN

means of the canonical variables for each class

When you specify METHOD=NORMAL, the _NAME_ variable identifies different types of coefficients in the discriminant function, and the _TYPE_ variable can have one of the following values:

_TYPE_

Contents

LINEAR

coefficients of the linear discriminant functions

QUAD

coefficients of the quadratic discriminant functions

The values of the _NAME_ variable are as follows:

_NAME_

Contents

variable names

quadratic coefficients of the quadratic discriminant functions (a symmetric matrix for each class)

_LINEAR_

linear coefficients of the discriminant functions

_CONST_

constant coefficients of the discriminant functions