Re: Structural model in NONMEM!
--- 09年11月26日，周四, Nick Holford <n.holford
发件人: Nick Holford <n.holford
主题: Re: [NMusers] Structural model in NONMEM!
收件人: "ke fang" <fang.coco
日期: 2009年11月26日,周四,上午2:16
Fang Ke,
Here is an example. The $PRED code lets you work directly with any
model you want to write.
$PROB
$DATA mydata
$INPUT ID X
$EST METHOD=CONDITIONAL LAPLACE
$THETA
1 ; A
2 ; B
$OMEGA
0.5 ; ETA_A
0.5 ; ETA_B
$SIGMA
1 ; EPS
$PRED
Y=THETA(1)+ETA(1) + (THETA(2)+ETA(2))*X + EPS(1)
ke fang wrote:
Dear all!
I have a question about structural model in NONMEM! Can
NONMEM deal with linear structural model like y=a+b*x? If it will do,
how the code should be prepared?
Thanks in advance!
Fang ke
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--
Nick Holford, Professor Clinical Pharmacology
Dept Pharmacology & Clinical Pharmacology
University of Auckland,85 Park Rd,Private Bag 92019,Auckland,New Zealand
tel:+64(9)923-6730 fax:+64(9)373-7090 mobile:+64(21)46 23 53
email: n.holford
http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford
Dear Nick and Zhao,
Thanks to your reply.I'm now using WFN to implement a bootstrap analysis. I followed the instruction on this web page http://wfn.sourceforge.net/wfnbs..htm. But the result txt file did not show the THETAs and ETAs and other estimates. I tried the debugging run and found the result can be obtained in each run. Do i miss some statements in control stream to cause the problem? Here is control stream i wrote:
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
$PROB
$DATA nmsamp.dat IGNORE$INPUT ID OLD=DROP
TIME CONC=DV PPM WT DOSE AMT
$THETA
5 ; A
0.00232 ; B
0.2;C
$OMEGA
0.142 ; ETA_A
0.306 ; ETA_B
$SIGMA
1 ; EPS
$PRED
Y=(THETA(1)+ETA(1)+AMT*THETA(3))- (THETA(2)*exp(ETA(2)))*TIME+ EPS(1)
$ESTIMATION METHOD=1 MAXEVAL99 PRINT=5 POSTHOC
$COVARIANCE
$TABLE ID TIME CONC WT DOSE AMT ETA1 ETA2
NOPRINT ONEHEADER FILE=test.FIT
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
And here is the bootstrap analysis result txt file content:
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
#Rep Obj Min Cov Eval Sig Sub Obs A B C ETA_A ETA_B EPS A:se B:se C:se ETA_A:se ETA_B:se EPS:se
1 -.9777E+01 MINIMIZATION_SUCCESSFUL_sys_0:0_ NONE 264 3.7 32 32
2 -.1710E+02 MINIMIZATION_SUCCESSFUL_sys_0:0_ NONE 193 3.1 32 32
3 -.2159E+02 MINIMIZATION_SUCCESSFUL_sys_0:0_ NONE 217 3.3 32 32
4 -.9194E+01 MINIMIZATION_SUCCESSFUL_sys_0:0_ NONE 186 3.9 32 32
5 -.9531E+01 MINIMIZATION_SUCCESSFUL_sys_0:0_ NONE 178 3.8 32 32
6 -.1476E+01 MINIMIZATION_SUCCESSFUL_sys_0:0_ NONE 173 3.1 32 32
7 -.5704E+01 MINIMIZATION_SUCCESSFUL_sys_0:0_ NONE 139 3.6 32 32
8 -.2087E+02 MINIMIZATION_SUCCESSFUL_sys_0:0_ NONE 153 3.1 32 32
9 -.1332E+02 MINIMIZATION_SUCCESSFUL_sys_0:0_ NONE 173 3.9 32 32
10 -.1108E+02 MINIMIZATION_SUCCESSFUL_sys_0:0_ NONE 276 3.2 32 32
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Ke, Fang
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