Re: Variance of the predictions of the etas.
Date: Thu, 23 Sep 1999 08:31:06 -0700 (PDT)
From: ABoeckmann <alison@c255.ucsf.edu>
Subject: Re: Variance of the predictions of the etas.
RE: two recent emails:
Here is a copy of an email that I sent to nmusers in 1996 telling how to implement the Bayesian estimates using NM-TRAN.
-- Alison Boeckmann
=================
To: nmusers
Subject: Users Guide II
Here is an NM-TRAN control stream and a data file for the Bayesian regression example of Guide II Section C.
Please refer to that manual for a discussion.
Two things to note (from Stuart Beal):
(1) Use MAT=R when wanting SE's with single subject Bayesian regression.
(2) With single subject Bayesian regression, the SE is called the "standard deviation of the posterior variance", and not really an SE.
Alison Boeckmann
----
$PROBLEM BAYESIAN NONLIN REG OF CP VS TIME DATA FROM ONE SUBJECT - PREDPP
$INPUT DOSE=AMT TIME DV TYPE ID=L1
$INFI DATA.bayes
$SUBROUTINES ADVAN2 TRANS1
$THETAS (.4 1.7 7) (.025 .102 .4) (.3 3 30)
$OMEGA BLOCK(3) 5.55 .00524 .00024 -.128 .00911 .515 FIXED
$OMEGA BLOCK(1) .388 FIXED
$PK
KA=THETA(1)
K=THETA(2)
CL=THETA(3)
V=CL/K
S2=V
$ERROR
M0=0
M1=0
M2=0
M3=0
IF (TYPE.EQ.0) M0=1
IF (TYPE.EQ.1) M1=1
IF (TYPE.EQ.2) M2=1
IF (TYPE.EQ.3) M3=1
Y0=F+ERR(4) ; WHEN TYPE=0, TRUE VALUE OF CP
Y1=THETA(1)+ERR(1) ; WHEN TYPE=1, TRUE VALUE OF THETA(1)
Y2=THETA(2)+ERR(2) ; WHEN TYPE=2, TRUE VALUE OF THETA(2)
Y3=THETA(3)+ERR(3) ; WHEN TYPE=3, TRUE VALUE OF THETA(3)
Y=M0*Y0+M1*Y1+M2*Y2+M3*Y3
$ESTIMATION
$COVARIANCE MATRIX=R
$TABLE TYPE TIME
$SCAT (DV PRED RES) * TIME BY TYPE
$SCAT PRED VS DV BY TYPE UNIT
--------- DATA.bayes
320 0 0 0 0
0 0 2.77 1 0
0 0 .0781 2 0
0 0 2.63 3 0
0 .27 1.71 0 1
0 .52 7.91 0 2
0 1.0 8.31 0 3
0 1.92 8.33 0 4
0 3.5 6.85 0 5
0 5.02 6.08 0 6
0 7.03 5.4 0 7
0 9.0 4.55 0 8
0 12.0 3.01 0 9
0 24.3 .903 0 10
---------------
Results from the run:
THETA - VECTOR OF FIXED EFFECTS
2.12E+00 8.97E-02 3.02E+00
STANDARD ERROR OF ESTIMATE
THETA - VECTOR OF FIXED EFFECTS
2.81E-01 8.81E-03 2.34E-01