get.eigentraits. $\beta_{i}^{j} = V \cdot W^{T}$. It also uses the permutation data from the pairwise scan (pairscan) to calculate an empirical p value for the influence of each marker pair on each phenotype. The empirical p values are then adjusted for multiple testing using Holm's step-down procedure.
direct.influence(data.obj, pairscan.obj, transform.to.phenospace = TRUE,
pval.correction = c("holm", "fdr", "lfdr"), verbose = FALSE,
save.permutations = FALSE)read.population.
pairscan.
transform.to.phenospace) is added to the data object to indicate whether variant influences were transformed to phenotype space.The results from the pairwise scan and the permutations of the pairwise scan are converted to phenospace if specified. These actions each add one object each to the data object (var.to.pheno.influence and
var.to.pheno.influence.perm). Each element is itself a list of matrices corresponding to the original phenotypes. Each matrix contains one row per marker pair (or permutation of a marker pair) and contains the influence coefficient and standard error of the influence coefficient for each pair.After the coefficients have been transformed to phenotype space, each marker is considered individually and its influence on each phenotype across all marker pair contexts is tabulated. This is done for both the pairwise scan and the permutations of the pairwise scans and adds two new objects (var.to.pheno.test.stat and var.to.pheno.test.stat.perm) to the data object. Each object is a list containing one element for each of the original phenotypes. Each element contains a table in which all instances of each marker are listed along with that marker's direct phenotypic influence, the standard error of the influence, and the t statistic ($\beta/\sigma$) of the influence.Finally, because each marker can only have one influence on each phenotype, the influences from each marker pair context are filtered to report only the maximum influence of each marker on each phenotype across all marker pair contexts. This process adds an object to the data object called max.var.to.pheno.influence. This object is a list containing one element per phenotype. It tabulates the maximum influence of each marker on each phenotype, as well as the empirical and Holm's corrected p values associated with each influence.