Chapter Contents |
Previous |
Next |

The DISCRIM Procedure |

- TYPE=CORR data sets created by PROC CORR using a BY statement
- TYPE=COV data sets created by PROC PRINCOMP using both the COV option and a BY statement
- TYPE=CSSCP data sets created by PROC CORR using the CSSCP option and a BY statement, where the OUT= data set is assigned TYPE=CSSCP with the TYPE= data set option
- TYPE=SSCP data sets created by PROC REG using both the OUTSSCP= option and a BY statement
- TYPE=LINEAR, TYPE=QUAD, and TYPE=MIXED data sets produced by previous runs of PROC DISCRIM that used both METHOD=NORMAL and OUTSTAT= options

When the input data set is TYPE=CORR, TYPE=COV, or TYPE=CSSCP, PROC DISCRIM reads the number of observations for each class from the observations with _TYPE_='N' and reads the variable means in each class from the observations with _TYPE_='MEAN'. PROC DISCRIM then reads the within-class correlations from the observations with _TYPE_='CORR' and reads the standard deviations from the observations with _TYPE_='STD' (data set TYPE=CORR), the within-class covariances from the observations with _TYPE_='COV' (data set TYPE=COV), or the within-class corrected sums of squares and cross products from the observations with _TYPE_='CSSCP' (data set TYPE=CSSCP).

When you specify POOL=YES and the data set does not include any observations with _TYPE_='CSSCP' (data set TYPE=CSSCP), _TYPE_='COV' (data set TYPE=COV), or _TYPE_='CORR' (data set TYPE=CORR) for each class, PROC DISCRIM reads the pooled within-class information from the data set. In this case, PROC DISCRIM reads the pooled within-class covariances from the observations with _TYPE_='PCOV' (data set TYPE=COV) or reads the pooled within-class correlations from the observations with _TYPE_='PCORR' and the pooled within-class standard deviations from the observations with _TYPE_='PSTD' (data set TYPE=CORR) or the pooled within-class corrected SSCP matrix from the observations with _TYPE_='PSSCP' (data set TYPE=CSSCP).

When the input data set is TYPE=SSCP, the DISCRIM procedure reads the
number of observations for each class from the observations
with _TYPE_='N', the sum of weights of observations for
each class from the variable INTERCEP in observations with
_TYPE_='SSCP' and _NAME_='INTERCEPT', the variable sums
from the variable=*variablenames* in observations with
_TYPE_='SSCP' and _NAME_='INTERCEPT', and the uncorrected
sums of squares and cross products from the
variable=*variablenames* in observations with
_TYPE_='SSCP' and _NAME_='variablenames'.

When the input data set is TYPE=LINEAR, TYPE=QUAD, or TYPE=MIXED, PROC DISCRIM reads the prior probabilities for each class from the observations with variable _TYPE_='PRIOR'.

When the input data set is TYPE=LINEAR, PROC DISCRIM reads the coefficients of the linear discriminant functions from the observations with variable _TYPE_='LINEAR'.

When the input data set is TYPE=QUAD, PROC DISCRIM reads the coefficients of the quadratic discriminant functions from the observations with variable _TYPE_='QUAD'.

When the input data set is TYPE=MIXED, PROC DISCRIM reads the coefficients of the linear discriminant functions from the observations with variable _TYPE_='LINEAR'. If there are no observations with _TYPE_='LINEAR', PROC DISCRIM then reads the coefficients of the quadratic discriminant functions from the observations with variable _TYPE_='QUAD'.

Chapter Contents |
Previous |
Next |
Top |

Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.