Hi there,
I am trying to compute the ANCOVA sensitivity indices using UQlab.
In my problem, I have correlated inputs, and the input dimension is M=20. My output is also dynamic, meaning I have different time instances. But for now, I will consider N_{out} = 1 for simplicity. I collected the data from a computational study of one colleague of mine. Therefore, I do not possess the model for the computation of more samples. I got my list of input and output with sample size N_s = 800.
If I understood correctly, the ANCOVA analysis needs the input and output domain characteristics (not the samples), the model, and the sample size to compute the PCE. If correct, can I perform an ANCOVA without the model definition? I try to run it without the model definition, but I keep getting the same error:
Error: There is no MODEL provided!
While initializing the analysis
Error in uq_initialize_uq_sensitivity (line 730)
error('While initializing the analysis');
I paste here part of the code from which I get the error:
%% loading the data
% input files
inputMatrix= load('FE_Results_23-XP005-V7-inputData1.csv');
% output files
output_sim = load('FE_Results_23-XP005-V7-output.csv');
%% Input module
ModelOpts.Marginals(1).Type = 'Uniform';
ModelOpts.Marginals(1).Parameters = [22.99, 24.00];
ModelOpts.Marginals(2).Type = 'Beta';
ModelOpts.Marginals(2).Parameters = [1268.35, 1270.81];
ModelOpts.Marginals(3).Type = 'Logistic';
...
% Dependence
ModelOpts.Copula.Type = 'Gaussian';
ModelOpts.Copula.Parameters = [1, 0.0099, -0.0042, 0.05...] % 20 times 20 array
myInput = uq_createInput(ModelOpts);
%% ANCOVA sensitivity indices
clear('ANCOVAAnalysis','ANCOVASensOpts')
ANCOVASensOpts.Type = 'Sensitivity';
ANCOVASensOpts.Method = 'ANCOVA';
ANCOVASensOpts.ANCOVA.Samples.X = inputMatrix;
ANCOVASensOpts.ANCOVA.Samples.Y = output_sim;
ANCOVAAnalysis = uq_createAnalysis(ANCOVASensOpts);
I also tried to compute a PCE surrogate from my data (input and output) and use it as a model for the ANCOVA. In this case, I do not get the error I mentioned above. However, I am not sure that this approach really makes sense.
Thank you for reading this far, and for any help you can provide
Greetings
Gian