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As a compromise between these two approaches, a rejection-sampling algorithm can be used to simulate samples without simulating a large population (for example, genomeSIMLA [ 11]).
In Fig. 2, some synthetic data are used to simulate samples in the ELM feature space.
The gs program simulated SNP genotypes and an R script was used to simulate samples for quantitative traits with a standardized distribution (mean of 0 and variance of 1) for each epistatic scenario (Supplementary Note 2).
If a forward-time approach is used to simulate samples with the same set of markers, sample frequencies of the DPL will vary because of the impact of random genetic drift, unless special algorithms are used to control allele frequencies [ 7].
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Based on this parent data, bootstrapping was used to simulate sampling with 80 different combinations of the number of subjects (10, 20), total sampling time per subject (60, 120, 240, 480 minutes), number of days per subject (2, 4), and size of sampling periods (blocks) within days (1, 15, 60, 240 minutes).
Simulation studies typically involve the generation of a disease model (i.e. a penetrance function) that is subsequently used to simulate the samples of a dataset.
Various different methods have been used to simulate case-control samples based on penetrance models.
Therefore, an axisymmetric geometry is used to simulate the CPB sample (see Fig. 12).
A calibrated finite element model is used to simulate damaged bridge GDF samples under four damage scenarios.
These policies can be used to simulate the distribution of sample paths for all firms from any initial industry structure.
This approach was used to simulate a spatially biased sample by selecting stems based on their proximity to random points around the boundary of the stand.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com