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 |                        EXPECTATION EXAMPLE                         |
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 These are examples of the use of the Marginal (MRG_) data item.  Exam-
 ple 1

 Suppose that the probability that a particular subject  experiences  a
 pain relief score of 2 is computed.  Suppose also one wants to compute
 the (posterior population) expectation of the probability with each of
 4  different  bolus  doses,  not  all of which are among those used to
 obtain observations.  A fragment of the control file follows.

 $INPUT ID DOSE MDV MRG_ ...

 $PRED
  ....
  Y = likelihood of observation given ETA

 $EST METH=COND LAPLACE LIKELIHOOD
 $TABLE DOSE

 A fragment of the data follows, with comments following ";".

 #ID DOSE DV MDV MRG_
  100 5    2  1   1    ; PRED item is set to expectation for DOSE=5
  100 10   2  1   1    ; PRED item is set to expectation for DOSE=10
  100 20   2  1   1    ; PRED item is set to expectation for DOSE=20
  100 40   2  1   1    ; PRED item is set to expectation for DOSE=40
  1   3    1  0   0
  1   10   1  0   0
  1   25   2  0   0
  1   30   2  0   0
  2   3    1  0   0
  2   10   1  0   0
  2   25   1  0   0
  2   30   1  0   0
  3   3    1  0   0
  3   10   2  0   0
  3   25   2  0   0
  3   30   3  0   0
  ... etc ...

 Example 2

 This example produces a plot of four residuals, formed by the  differ-
 ences  between  the raw-data-averages and their (posterior population)
 expectations, versus the doses used to obtain the data.

 $INPUT ID DOSE MDV MRG_ RAW_
 $PRED
  ...
 IF (ICALL.EQ.5) THEN
   Y = expectation of observation given ETA
 ELSE
   Y = likelihood of observation given ETA
 ENDIF
 $EST METHOD=COND LAPLACE LIKELIHOOD
 $SCAT RES VS DOSE

 #ID DOSE DV MDV MRG_ RAW_
  100 5    .  1   1    1
  100 10   .  1   1    1
  100 20   .  1   1    1
  100 40   .  1   1    1
  1   5    1  0   0    0
  1   10   1  0   0    0
  1   20   2  0   0    0
  1   40   2  0   0    0
  ... etc ...

 Example 3

 This example produces a plot of four residuals, formed by the  differ-
 ences  between  the  proportion  of subjects in the data set with pain
 relief score 2 and the (posterior population) expectation of the prob-
 ability that a subject experiences a pain score of 2, versus the doses
 used to obtain the data.

 $INPUT ID DOSE MDV MRG_ RAW_
 $PRED
  ...
   Y = likelihood of observation given ETA
   IF (ICALL.EQ.6) THEN
      DVR=DV
      DV=0
      IF (DVR.EQ.2) DV=1
    ENDIF
 $EST METHOD=COND LAPLACE LIKELIHOOD
 $SCAT RES VS DOSE

 #ID DOSE DV MDV MRG_ RAW_
  100 5    2  1   1    1
  100 10   2  1   1    1
  100 20   2  1   1    1
  100 40   2  1   1    1
  1   5    1  0   0    0
  1   10   1  0   0    0
  1   20   2  0   0    0
  1   40   2  0   0    0
  2   5    1  0   0    0
  2   10   1  0   0    0
  2   20   1  0   0    0
  2   40   3  0   0    0
  3   5    1  0   0    0
  3   10   2  0   0    0
  3   20   2  0   0    0
  3   40   3  0   0    0
  ... etc ...

 (See mrg, expectation block, data average block, raw).

 REFERENCES: none.


  
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