+--------------------------------------------------------------------+
 |                                                                    |
 |                           PRED_RES_WRES                            |
 |                                                                    |
 +--------------------------------------------------------------------+
 MEANING: PRED, RES, WRES
 CONTEXT: NONMEM output

 DISCUSSION:
 NONMEM tables usually include special items: prediction, residual, and
 weighted residual, which are generated by NONMEM.  The default  labels
 for these items are PRED, RES, and WRES, respectively. Synonyms may be
 specified on the $TABLE record for one or more of these labels.   With
 the  NOAPPEND  option on this record, the three items are not included
 in a table unless explicity listed on the $TABLE record.  With the use
 of the user-routine SPTWO, the values of the RES and WRES items can be
 defined differently from the values described below.
 (See sptwo).

 These items may also be displayed in scatterplots.   Synonyms  may  be
 specified  on  the  $SCATTERPLOT record as well; synonyms specified on
 either the $TABLE or $SCATTERPLOT  record  also  apply  to  the  other
 record.

 PRED Prediction items are the predictions computed by the PRED subrou-
      tine.  For population data, prediction items are  always  popula-
      tion  predictions,  i.e.,  they are computed at the mean value of
      eta (0).

 RES  The residual is defined as DV - PRED; that is, the observed value
      minus the prediction item.

 WRES The weighted residuals for an individual are formed by transform-
      ing the individual's residuals so that under the model,  assuming
      the  true  values of the parameters are given by the estimates of
      those parameters, all weighted residuals have mean 0, unit  vari-
      ance  and  are  uncorrelated.  For population data, the "weights"
      are computed at eta = 0.

 For odd-type data, the prediction items are likelihoods  (for  popula-
 tion  data, using the estimated values of the parameters, and computed
 at eta = 0).  These may not be of much interest.   The  RES  and  WRES
 items are 0.

 With  a  mixture  model, each individual is classified into one of the
 subpopulations of the mixture according to a computation based on  the
 individual's  data  and  on the final parameter estimates.  For a data
 record from the  individual  record,  the  prediction,  residual,  and
 weighted  residual items in the corresponding row of a table (or point
 on a scatterplot) are based on the submodel defining the subpopulation
 into which the individual is classified.

 If  the  Marginal  data item (MRG_) is 1 or 2 for a given data record,
 then PRED is an expected prediction, rather than the prediction at the
 mean  value  of eta.  When the Raw-data data item (RAW_) is 1, then DV
 is a raw-data average and RES is the difference between the PRED  item
 and this average.
 (See displayed PRED-defined items).

REFERENCES: Guide I Section C.3.5.3, C.3.5.4
REFERENCES: Guide IV Section III.B.16, III.B.17
REFERENCES: Guide V Section 9.5, 10.7, 11.4.4.2


  
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