PROC DISCRIM Statement

PROC DISCRIM < options > ;

This statement invokes the DISCRIM procedure. You can specify the following options in the PROC DISCRIM statement.

Tasks			Options
Specify Input Data Set			DATA=
			TESTDATA=
Specify Output Data Set			OUTSTAT=
			OUT=
			OUTCROSS=
			OUTD=
			TESTOUT=
			TESTOUTD=
Discriminant Analysis			METHOD=
			POOL=
			SLPOOL=
Nonparametric Methods			K=
			R=
			KERNEL=
			METRIC=

Tasks		Options
Classification Rule		THRESHOLD=
Determine Singularity		SINGULAR=
Canonical Discriminant Analysis		CANONICAL
		CANPREFIX=
		NCAN=
Resubstitution Classification		LIST
		LISTERR
		NOCLASSIFY
Cross Validation Classification		CROSSLIST
		CROSSLISTERR
		CROSSVALIDATE
Test Data Classification		TESTLIST
		TESTLISTERR
Estimate Error Rate		POSTERR
Control Displayed Output
	Correlations	BCORR
		PCORR
		TCORR
		WCORR
	Covariances	BCOV
		PCOV
		TCOV
		WCOV
	SSCP Matrix	BSSCP
		PSSCP
		TSSCP
		WSSCP
	Miscellaneous	ALL
		ANOVA
		DISTANCE
		MANOVA
		SIMPLE
		STDMEAN
	Suppress output	NOPRINT
		SHORT

ALL

activates all options that control displayed output. When the derived classification criterion is used to classify observations, the ALL option also activates the POSTERR option.

ANOVA

displays univariate statistics for testing the hypothesis that the class means are equal in the population for each variable.

BCORR

displays between-class correlations.

BCOV

displays between-class covariances. The between-class covariance matrix equals the between-class SSCP matrix divided by n(c-1)/c, where n is the number of observations and c is the number of classes. You should interpret the between-class covariances in comparison with the total-sample and within-class covariances, not as formal estimates of population parameters.

BSSCP

displays the between-class SSCP matrix.

CANONICAL

CAN

performs canonical discriminant analysis.

CANPREFIX=name

specifies a prefix for naming the canonical variables. By default, the names are Can1, Can2, ... , Cann. If you specify CANPREFIX=ABC, the components are named ABC1, ABC2, ABC3, and so on. The number of characters in the prefix, plus the number of digits required to designate the canonical variables, should not exceed 32. The prefix is truncated if the combined length exceeds 32.

The CANONICAL option is activated when you specify either the NCAN= or the CANPREFIX= option. A discriminant criterion is always derived in PROC DISCRIM. If you want canonical discriminant analysis without the use of discriminant criteria, you should use PROC CANDISC.

CROSSLIST

displays the cross validation classification results for each observation.

CROSSLISTERR

displays the cross validation classification results for misclassified observations only.

CROSSVALIDATE

specifies the cross validation classification of the input DATA= data set. When a parametric method is used, PROC DISCRIM classifies each observation in the DATA= data set using a discriminant function computed from the other observations in the DATA= data set, excluding the observation being classified. When a nonparametric method is used, the covariance matrices used to compute the distances are based on all observations in the data set and do not exclude the observation being classified. However, the observation being classified is excluded from the nonparametric density estimation (if you specify the R= option) or the k nearest neighbors (if you specify the K= option) of that observation. The CROSSVALIDATE option is set when you specify the CROSSLIST, CROSSLISTERR, or OUTCROSS= option.

DATA=SAS-data-set

specifies the data set to be analyzed. The data set can be an ordinary SAS data set or one of several specially structured data sets created by SAS/STAT procedures. These specially structured data sets include TYPE=CORR, TYPE=COV, TYPE=CSSCP, TYPE=SSCP, TYPE=LINEAR, TYPE=QUAD, TYPE=MIXED, and TYPE=DISCAL. The input data set must be an ordinary SAS data set if you specify METHOD=NPAR. If you omit the DATA= option, the procedure uses the most recently created SAS data set.

DISTANCE

displays the squared Mahalanobis distances between the group means, F statistics, and the corresponding probabilities of greater Mahalanobis squared distances between the group means. The squared distances are based on the specification of the POOL= and METRIC= options.

K=k

specifies a k value for the k-nearest-neighbor rule. An observation x is classified into a group based on the information from the k nearest neighbors of x. Do not specify both the K= and R= options.

KERNEL=BIWEIGHT | BIW

KERNEL=EPANECHNIKOV | EPA

KERNEL=NORMAL | NOR

KERNEL=TRIWEIGHT | TRI

KERNEL=UNIFORM | UNI

specifies a kernel density to estimate the group-specific densities. You can specify the KERNEL= option only when the R= option is specified. The default is KERNEL=UNIFORM.

LIST

displays the resubstitution classification results for each observation. You can specify this option only when the input data set is an ordinary SAS data set.

LISTERR

displays the resubstitution classification results for misclassified observations only. You can specify this option only when the input data set is an ordinary SAS data set.

MANOVA

displays multivariate statistics for testing the hypothesis that the class means are equal in the population.

METHOD=NORMAL | NPAR

determines the method to use in deriving the classification criterion. When you specify METHOD=NORMAL, a parametric method based on a multivariate normal distribution within each class is used to derive a linear or quadratic discriminant function. The default is METHOD=NORMAL. When you specify METHOD=NPAR, a nonparametric method is used and you must also specify either the K= or R= option.

METRIC=DIAGONAL | FULL | IDENTITY

specifies the metric in which the computations of squared distances are performed. If you specify METRIC=FULL, PROC DISCRIM uses either the pooled covariance matrix (POOL=YES) or individual within-group covariance matrices (POOL=NO) to compute the squared distances. If you specify METRIC=DIAGONAL, PROC DISCRIM uses either the diagonal matrix of the pooled covariance matrix (POOL=YES) or diagonal matrices of individual within-group covariance matrices (POOL=NO) to compute the squared distances. If you specify METRIC=IDENTITY, PROC DISCRIM uses Euclidean distance. The default is METRIC=FULL. When you specify METHOD=NORMAL, the option METRIC=FULL is used.

NCAN=number

specifies the number of canonical variables to compute. The value of number must be less than or equal to the number of variables. If you specify the option NCAN=0, the procedure displays the canonical correlations but not the canonical coefficients, structures, or means. Let v be the number of variables in the VAR statement and c be the number of classes. If you omit the NCAN= option, only min(v, c-1) canonical variables are generated. If you request an output data set (OUT=, OUTCROSS=, TESTOUT=), v canonical variables are generated. In this case, the last v-(c-1) canonical variables have missing values.

NOCLASSIFY

suppresses the resubstitution classification of the input DATA= data set. You can specify this option only when the input data set is an ordinary SAS data set.

NOPRINT

suppresses the normal display of results. Note that this option temporarily disables the Output Delivery System (ODS); see Chapter 14, "Using the Output Delivery System," for more information.

OUT=SAS-data-set

creates an output SAS data set containing all the data from the DATA= data set, plus the posterior probabilities and the class into which each observation is classified by resubstitution. When you specify the CANONICAL option, the data set also contains new variables with canonical variable scores. See the "OUT= Data Set" section.

OUTCROSS=SAS-data-set

creates an output SAS data set containing all the data from the DATA= data set, plus the posterior probabilities and the class into which each observation is classified by cross validation. When you specify the CANONICAL option, the data set also contains new variables with canonical variable scores. See the "OUT= Data Set" section.

OUTD=SAS-data-set

creates an output SAS data set containing all the data from the DATA= data set, plus the group-specific density estimates for each observation. See the "OUT= Data Set" section.

OUTSTAT=SAS-data-set

creates an output SAS data set containing various statistics such as means, standard deviations, and correlations. When the input data set is an ordinary SAS data set or when TYPE=CORR, TYPE=COV, TYPE=CSSCP, or TYPE=SSCP, this option can be used to generate discriminant statistics. 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 output 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. This data set also holds calibration information that can be used to classify new observations. See the "Saving and Using Calibration Information" section and the "OUT= Data Set" section.

PCORR

displays pooled within-class correlations.

PCOV

displays pooled within-class covariances.

POOL=NO | TEST | YES

determines whether the pooled or within-group covariance matrix is the basis of the measure of the squared distance. If you specify POOL=YES, PROC DISCRIM uses the pooled covariance matrix in calculating the (generalized) squared distances. Linear discriminant functions are computed. If you specify POOL=NO, the procedure uses the individual within-group covariance matrices in calculating the distances. Quadratic discriminant functions are computed. The default is POOL=YES.

When you specify METHOD=NORMAL, the option POOL=TEST requests Bartlett's modification of the likelihood ratio test (Morrison 1976; Anderson 1984) of the homogeneity of the within-group covariance matrices. The test is unbiased (Perlman 1980). However, it is not robust to nonnormality. If the test statistic is significant at the level specified by the SLPOOL= option, the within-group covariance matrices are used. Otherwise, the pooled covariance matrix is used. The discriminant function coefficients are displayed only when the pooled covariance matrix is used.

POSTERR

displays the posterior probability error-rate estimates of the classification criterion based on the classification results.

PSSCP

displays the pooled within-class corrected SSCP matrix.

R=r

specifies a radius r value for kernel density estimation. With uniform, Epanechnikov, biweight, or triweight kernels, an observation x is classified into a group based on the information from observations y in the training set within the radius r of x, that is, the group t observations y with squared distance $d_t^2(x,y) \leq r^2$ .When a normal kernel is used, the classification of an observation x is based on the information of the estimated group-specific densities from all observations in the training set. The matrix r² V_t is used as the group t covariance matrix in the normal-kernel density, where V_t is the matrix used in calculating the squared distances. Do not specify both the K= and R= options. For more information on selecting r, see the "Nonparametric Methods" section.

SHORT

suppresses the display of certain items in the default output. If you specify METHOD= NORMAL, PROC DISCRIM suppresses the display of determinants, generalized squared distances between-class means, and discriminant function coefficients. When you specify the CANONICAL option, PROC DISCRIM suppresses the display of canonical structures, canonical coefficients, and class means on canonical variables; only tables of canonical correlations are displayed.

SIMPLE

displays simple descriptive statistics for the total sample and within each class.

SINGULAR=p

specifies the criterion for determining the singularity of a matrix, where 0<p<1. The default is SINGULAR=1E-8.

Let S be the total-sample correlation matrix. If the R² for predicting a quantitative variable in the VAR statement from the variables preceding it exceeds 1-p, then S is considered singular. If S is singular, the probability levels for the multivariate test statistics and canonical correlations are adjusted for the number of variables with R² exceeding 1-p.

Let S_t be the group t covariance matrix and S_p be the pooled covariance matrix. In group t, if the R² for predicting a quantitative variable in the VAR statement from the variables preceding it exceeds 1-p, then S_t is considered singular. Similarly, if the partial R² for predicting a quantitative variable in the VAR statement from the variables preceding it, after controlling for the effect of the CLASS variable, exceeds 1-p, then S_p is considered singular.

If PROC DISCRIM needs to compute either the inverse or the determinant of a matrix that is considered singular, then it uses a quasi-inverse or a quasi-determinant. For details, see the "Quasi-Inverse" section.

SLPOOL=p

specifies the significance level for the test of homogeneity. You can specify the SLPOOL= option only when POOL=TEST is also specified. If you specify POOL= TEST but omit the SLPOOL= option, PROC DISCRIM uses 0.10 as the significance level for the test.

STDMEAN

displays total-sample and pooled within-class standardized class means.

TCORR

displays total-sample correlations.

TCOV

displays total-sample covariances.

TESTDATA=SAS-data-set

names an ordinary SAS data set with observations that are to be classified. The quantitative variable names in this data set must match those in the DATA= data set. When you specify the TESTDATA= option, you can also specify the TESTCLASS, TESTFREQ, and TESTID statements. When you specify the TESTDATA= option, you can use the TESTOUT= and TESTOUTD= options to generate classification results and group-specific density estimates for observations in the test data set.

TESTLIST

lists classification results for all observations in the TESTDATA= data set.

TESTLISTERR

lists only misclassified observations in the TESTDATA= data set but only if a TESTCLASS statement is also used.

TESTOUT=SAS-data-set

creates an output SAS data set containing all the data from the TESTDATA= data set, plus the posterior probabilities and the class into which each observation is classified. When you specify the CANONICAL option, the data set also contains new variables with canonical variable scores. See the "OUT= Data Set" section.

TESTOUTD=SAS-data-set

creates an output SAS data set containing all the data from the TESTDATA= data set, plus the group-specific density estimates for each observation. See the "OUT= Data Set" section.

THRESHOLD=p

specifies the minimum acceptable posterior probability for classification, where $0 \leq p \leq 1$ .If the largest posterior probability of group membership is less than the THRESHOLD value, the observation is classified into group OTHER. The default is THRESHOLD=0.

TSSCP

displays the total-sample corrected SSCP matrix.

WCORR

displays within-class correlations for each class level.

WCOV

displays within-class covariances for each class level.

WSSCP

displays the within-class corrected SSCP matrix for each class level.