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Special SAS Data Sets |

Many SAS/STAT procedures create SAS data sets containing various statistics. Some of these data sets are organized according to certain conventions that allow them to be read by a SAS/STAT procedure for further analysis. Such specially organized data sets are recognized by the TYPE= attribute of the data set.

For example, the CORR procedure (refer to the *SAS Procedures
Guide*) can create a data set
with the attribute TYPE=CORR containing a correlation matrix.
This TYPE=CORR data set can be read by the REG or FACTOR procedure,
among others. If the original data set is large, using a special
SAS data set in this way can save a great deal of computer time
by avoiding the recomputation of the correlation matrix in each
of several analyses.

As another example, the REG procedure can create a TYPE=EST data set containing estimated regression coefficients. If you need to make predictions for new observations, you can have the SCORE procedure read both the TYPE=EST data set and a data set containing the new observations. PROC SCORE can then compute predicted values or residuals without repeating the entire regression analysis. See Chapter 57, "The SCORE Procedure," for an example.

Many procedures provide options to produce output data sets of TYPE=null; you can also create TYPE=null data sets from any table generated by a procedure by using the Output Delivery System (ODS); see Chapter 15, "Using the Output Delivery System," for more information.

A special SAS data set may contain different kinds of statistics. A special variable called _TYPE_ is used to distinguish the various statistics. For example, in a TYPE=CORR data set, an observation in which _TYPE_='MEAN' contains the means of the variables in the analysis, and an observation in which _TYPE_='STD' contains the standard deviations. Correlations appear in observations with _TYPE_='CORR'. Another special variable, _NAME_, is needed to identify the row of the correlation matrix. Thus, the correlation between variables X and Y would be given by the value of the variable X in the observation for which _TYPE_='CORR' and _NAME_='Y', or by the value of the variable Y in the observation for which _TYPE_='CORR' and _NAME_='X'.

You can create special SAS data steps directly in a DATA step by specifying the TYPE= option in parentheses after the data set name in the DATA statement. See Example A.2 for an example.

The special data sets created by SAS/STAT procedures can generally be used directly by other procedures without modification. However, if you create an output data set with PROC CORR and use the NOCORR option to omit the correlation matrix from the OUT= data set, you need to set the TYPE= option either in parentheses following the OUT= data set name in the PROC CORR statement or in parentheses following the DATA= option in any other procedure that recognizes the special TYPE= attribute. In either case, the TYPE= option should be set to COV, CSSCP, or SSCP according to what type of matrix is stored in the data set and what data set types are accepted as input by the other procedures you plan to use. If you do not follow these steps and you use the TYPE=CORR data set with no correlation matrix as input to another procedure, the procedure may issue an error message indicating that the correlation matrix is missing from the data set.

If you use a DATA step with a SET statement to modify a special SAS
data set, you must specify the TYPE= option in the DATA statement.
The TYPE= attribute of the data set in the SET statement is *not *
automatically copied to the data set being created.

You can determine the TYPE= attribute of a data set by using
the CONTENTS procedure
(see Example A.1 and refer
to the *SAS Procedures Guide* for details).

Table A.1 summarizes the TYPE= data sets that can be used as input to SAS/STAT procedures and the TYPE= data sets that are created by SAS/STAT procedures. The essential parts of the statements each procedure uses to create its output data set or data sets are shown.

Formulas useful for illustrating differences between corrected and uncorrected matrices in some special SAS data sets are shown in the "Definitional Formulas" section.

Procedure |
Input Data Sets TYPE= as shown ^{*} |
Output Data Sets (TYPE=null or as shown) |
Created by Statement and Specification |

ACECLUS | INITIAL= INPUT= data set may be of type: ACE, CORR, COV, SSCP, UCORR, UCOV | ACE | PROC ACECLUS OUTSTAT= PROC ACECLUS OUT= |

ANOVA | PROC ANOVA OUTSTAT= | ||

CALIS | CORR, COV, FACTOR, RAM, SSCP, UCORR, UCOV, WEIGHT | CORR
COV EST UCORR UCOV RAM WEIGHT | PROC CALIS OUTSTAT=
PROC CALIS COV OUTSTAT= PROC CALIS OUTEST= PROC CALIS NOINT OUTSTAT= PROC CALIS NOINT COV OUTSTAT= PROC CALIS OUTRAM= PROC CALIS OUTWGT= |

CANCORR | CORR, COV, FACTOR, SSCP, UCORR, UCOV | CORR
UCORR | PROC CANCORR OUTSTAT=
PROC CANCORR NOINT OUTSTAT= PROC CANCORR OUT= |

CANDISC | CORR, COV, SSCP, CSSCP | CORR | PROC CANDISC OUTSTAT=
PROC CANDISC OUT= |

CATMOD | EST | EST | RESPONSE / OUTEST=
RESPONSE /OUT= |

CLUSTER | DISTANCE | TREE | PROC CLUSTER OUTTREE= |

CORRESP | PROC CORRESP OUTC=
PROC CORRESP OUTF= | ||

DISCRIM | CORR, COV, SSCP, CSSCP, LINEAR, QUAD, MIXED | LINEAR
QUAD MIXED CORR | PROC DISCRIM POOL=YES OUTSTAT=
PROC DISCRIM POOL=NO OUTSTAT= PROC DISCRIM POOL=TEST OUTSTAT= PROC DISCRIM METHOD=NPAR OUTSTAT= PROC DISCRIM OUT= PROC DISCRIM OUTCROSS= PROC DISCRIM OUTD= PROC DISCRIM TESTOUT= PROC DISCRIM TESTOUTD= |

FACTOR | ACE, CORR, COV, FACTOR, SSCP, UCORR, UCOV | FACTOR | PROC FACTOR OUTSTAT=
PROC FACTOR OUT= |

FASTCLUS | PROC FASTCLUS OUT=
PROC FASTCLUS OUTSEED= PROC FASTCLUS OUTSTAT= PROC FASTCLUS MEAN= | ||

FREQ | TABLES OUT=
OUTPUT OUT= | ||

GENMOD | OUTPUT OUT= | ||

GLM | PROC GLM OUTSTAT=
LSMEANS / OUT= OUTPUT OUT= | ||

GLMMOD | PROC GLMMOD OUTDESIGN=
PROC GLMMOD OUTPARM= | ||

INBREED | PROC INBREED OUTCOV= | ||

KRIGE2D | PROC KRIGE2D OUTEST=
PROC KRIGE2D OUTNBHD= | ||

LATTICE | |||

LIFEREG | EST | PROC LIFEREG OUTEST=
OUTPUT OUT= | |

LIFETEST | PROC LIFETEST OUTSURV=
PROC LIFETEST OUTTEST= | ||

LOGISTIC | EST | PROC LOGISTIC OUTEST=
OUTPUT OUT= MODEL / OUTROC= | |

MDS | PROC MDS OUT=
PROC MDS OUTFIT= PROC MDS OUTRES= | ||

MIXED | MODEL OUTPRED=
MODEL OUTPREDM= PRIOR OUT= PRIOR OUTG= PRIOR OUTGT= | ||

MODECLUS | DISTANCE | PROC MODECLUS OUT=
PROC MODECLUS OUTCLUS= PROC MODECLUS OUTSUM= | |

MULTTEST | PROC MULTTEST OUT=
PROC MULTTEST OUTPERM= PROC MULTTEST OUTSAMP= | ||

NESTED | |||

NLIN | EST | PROC NLIN OUTEST=
OUTPUT OUT= | |

NPAR1WAY | OUTPUT OUT= | ||

ORTHOREG | EST | PROC ORTHOREG OUTEST= | |

PHREG | EST | PROC PHREG OUTEST=
BASELINE OUT= OUTPUT OUT= | |

PLAN | OUTPUT OUT= | ||

PLS | OUTPUT OUT= | ||

PRINCOMP | ACE, CORR, COV, EST, FACTOR, SSCP, UCORR, UCOV | CORR
COV UCORR UCOV | PROC PRINCOMP OUTSTAT=
PROC PRINCOMP COV OUTSTAT= PROC PRINCOMP NOINT OUTSTAT= PROC PRINCOMP NOINT COV OUTSTAT= PROC PRINCOMP OUT= |

PRINQUAL | PROC PRINQUAL OUT= | ||

PROBIT | EST | PROC PROBIT OUTEST=
OUTPUT OUT= | |

REG | CORR, COV, SSCP, UCORR, UCOV | EST
SSCP | PROC REG OUTEST=
PROC REG OUTSSCP= OUTPUT OUT= |

RSREG | PROC RSREG OUT=
RIDGE OUTR= | ||

SCORE | SCORE= data set can be of any type | PROC SCORE OUT= | |

SIM2D | PROC SIM2D OUTSIM= | ||

SURVEYSELECT | PROC SURVEYSELECT OUT= PROC SURVEYSELECT OUTSORT= | ||

STDIZE | PROC STDIZE OUT=
PROC STDIZE OUTSTAT= | ||

STEPDISC | CORR, COV, SSCP, CSSCP | ||

TRANSREG | PROC TRANSREG OUTTEST=
OUTPUT OUT= | ||

TREE | TREE | PROC TREE OUT= | |

TTEST | |||

VARCLUS | CORR, COV, FACTOR, SSCP, UCORR, UCOV | CORR
UCORR TREE | PROC VARCLUS OUTSTAT=
PROC VARCLUS NOINT OUTSTAT= PROC VARCLUS OUTTREE= |

VARCOMP | |||

VARIOGRAM | PROC VARIOGRAM OUTDISTANCE=
PROC VARIOGRAM OUTPAIR= PROC VARIOGRAM OUTVAR= |

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