Adding an Error Model to a Nonlinear Mixed Effects Model

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Hello all,
First, I used SimBiology Model Builder to restore the PPK model that had been established in NONMEM. Since SimBiology Model Builder cannot add parameters for inter-individual variation and error models, I added them using code. But I found that the exponential error model has not been added to the model.
I would like to ask how to add the exponential error model to the following code and how to optimize the simulation process code? Thank you for your help!
opts = spreadsheetImportOptions("NumVariables", 3);
opts.Sheet = "Sheet1";
opts.DataRange = "A2:C237";
opts.VariableNames = ["ID", "TIME", "DV"];
opts.VariableTypes = ["double", "double", "double"];
Dose = readtable("Dose.xls", opts, "UseExcel", false)
data = groupedData(Dose);
sbiotrellis(data,@semilogy,"ID","TIME","DV",Marker="+",LineStyle="--");%%@semilogy
% Extract the model overall model file
s = sbioloadproject("Version-2-20241218.sbproj");
model = s.m1;
covModel = CovariateModel;
covModel.Expression = {'V1 = exp(theta1 + eta1)',' V2 = exp(theta2 + eta2)','F1 = exp(theta3 + eta3)','CL = exp(theta4 + eta4)'};
covModel.FixedEffectNames
covModel.FixedEffectDescription
options.ErrorModel = 'exponential';
options.ErrorModelParameters = struct('sigma', 1.0928)
thetas = table(4.0056,-20.3634,-7.9054,7.0873,'VariableNames',["theta1","theta2","theta3","theta4"])
omega = table([2.4432;0;0;0],[0;0.0534;0;0],[0;0;9.4391;0],[0;0;0;0.0729],'VariableNames',["eta1","eta2","eta3","eta4"])
phi = sbiosampleparameters(covModel.Expression,thetas,omega,Dose)
f = createSimFunction(model,covModel.ParameterNames,'Drug_Central',[])
simresults = f(phi,24)
t = sbiotrellis(simresults,[],'Time',' Drug_Central');
t.hFig.Position(3:4) = [800 500];
set(t.plots,'XGrid','on','YGrid','on');
set(t.plots,'YScale','log');
lh = findobj(t.plots,'Type','line');
set(lh,'LineWidth',2);

Risposta accettata

Jeremy Huard
Jeremy Huard il 20 Mar 2025
Assuming you want to add noise to the simulation data based on the exponential error model, you could use sbiosampleerror:
noisysimresults = sbiosampleerror(simresults, 'exponential', 1.0928);

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