thd.adjust: Principal component-based correction for population structure

In rasigadelab/thd: Timescaled haplotypic density

Description

Generate a matrix of principal components (PCs) significantly associated with THD. Include these PCs in multivariate models to correct for population structure.

Usage

thd.adjust(x, H, m, method = "fdr", alpha = 0.05)

Arguments

x	vector of THD values
H	genetic distance matrix used to generate x
m	maximum number of PCs to be considered. Typically, the no. of markers used to generate H.
method	P-value adjustment method passed to p.adjust. Default = false discovery rate (FDR).
alpha	significance threshold to retain a given PC.

Value

Matrix with length(x) rows and one column per PC significantly associated with x. If no PC is found, a 0-column matrix is returned.

rasigadelab/thd documentation built on April 11, 2020, 7:27 a.m.