Takes the bootstrap score distribution of two fits on the same populations and tests whether the scores of one graph are significantly higher or lower than the scores of the other graph.
compare_fits(scores1, scores2)Scores for the first graph. Each score should be for same model evaluated on different bootstrap samples of the SNP blocks. See qpgraph_resample_multi
Scores for the second graph, evaluated on the same bootstrap samples as the first graph
A list with statistics comparing the two models
p_emp: The two-sided bootstrap p-value testing whether the scores of one model are higher or lower than the scores of the other model. It is two times the fraction of bootstrap replicates in which model 1 has a lower score than model 2 (or vice-vera, whichever is lower). This is simply a bootstrap test for testing whether some quantity (the the score difference of two models in this case) is significantly different from zero.
p_emp_nocorr: p_emp is truncated to be no less than the reciprocal of the number of bootstrap iterations (the length of the score vectors). p_emp_nocorr is not truncated and can be equal to 0.
ci_low: The 2.5% quantile of distribution of score differences
ci_high: The 97.5% quantile of distribution of score differences
The other items in this list are less important and can be ignored. In particular, p is not as reliable as p_emp because it assumes that the score differences follow a normal distribution.
if (FALSE) {
fits = qpgraph_resample_multi(f2_blocks, list(graph1, graph2), nboot = 100)
compare_fits(fits[[1]]$score, fits[[2]]$score)
}