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Table 4 shows the average of the estimated parameters obtained from the 100 replicated datasets.
Posterior predictive p-values allow us to average over the uncertainty in θ and γ using M simulated replicated datasets (x∗) from the predictive distribution of the data.
A new MaxEnt model was fit to each of these 100 replicated datasets and used to create 100 full resolution biomass maps.
One hundred thousand replicated datasets were studied under Scenarios 2 to 4 and 10,000 under Scenario 1.
In total, 25 replicated datasets were simulated.
Note that for the permutation tests we used 1000 replicated datasets and for other tests we used 5000 replicated datasets.
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Each replicated dataset was analyzed using ADMIXTURE v.122.
Patients can be picked more than once and some patients may not be represented in each replicated dataset.
Each replicated dataset was fitted using homoskedastic and heteroskedastic error versions of the selected WGP models, namely RR-BLUP, BayesA, BayesB and BayesCπ.
For bias evaluation the final model was fitted to replicate datasets using the bootstrap resampling technique in Wings for NONMEM [35], and PK parameter estimates and random effects for each of the replicate datasets were obtained.
Monte Carlo simulations are carried out to replicate datasets, estimate prediction model parameters and validate them using Lin's concordance measure.
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