These are wrapper functions around various qp functions, which will evaluate a model on multiple subdivisions of the data.
The subdivisions are formed by leaving out one or more SNP block at a time (see boot for details).
qp3pop_resample_snps(f2_blocks, boot = FALSE, ...)
qpdstat_resample_snps(f2_blocks, boot = FALSE, ...)
qpwave_resample_snps(f2_blocks, boot = FALSE, ...)
qpadm_resample_snps(f2_blocks, boot = FALSE, ...)
qpgraph_resample_snps(f2_blocks, boot = FALSE, ...)a 3d array of blocked f2 statistics
If FALSE (the default), block-jackknife resampling will be used to compute standard errors.
Otherwise, block-bootstrap resampling will be used to compute standard errors. If boot is an integer, that number
will specify the number of bootstrap resamplings. If boot = TRUE, the number of bootstrap resamplings will be
equal to the number of SNP blocks.
named arguments which are passed to the qp function.
print progress updates
a nested data frame where each model is a row, and the columns are model parameters and model outputs
if (FALSE) {
res = qpadm_resample_snps(example_f2_blocks, target = target, left = left, right = right)
unnest(res, weights)
}