pca_mip_data: PCA of MIP Data

In mrc-ide/mipmapper: Filtering and analysis of MIP panel genetic datasets

Description

Using the output of impute_mip_data, principal component analysis will be condcuted and the resultant components returned, with the variance in the data explained by each component and the loadings of each component also returned.

Usage

pca_mip_data(dat)

Arguments

dat	output of impute_mip_data

Value

Invisibly returns a list of class 'prcomp' with the following components

• "sdev" the standard deviations of the principal components (i.e., the square roots of the eigenvalues of the covariance/correlation matrix, though the calculation is actually done with the singular values of the data matrix).

• "rotation" the matrix of variable loadings (i.e., a matrix whose columns contain the eigenvectors). The function princomp returns this in the element loadings.

• "center, scale" the centering and scaling used

• "x" the value of the rotated data (the centred data multiplied by the rotation matrix) is returned. Hence, cov(x) is the diagonal matrix diag(sdev^2).

• "var" the variance in the data explained by each component

• "loadings" the loadings of each component

• "dat" the raw data used to conduct pca

Examples

dat <- dummy_data()
dat <- filter_misc(dat = dat)
dat <- filter_coverage(dat = dat, min_coverage = 2)
dat <- melt_mip_data(dat = dat)
dat <- impute_mip_data(dat = dat)
pca <- pca_mip_data(dat = dat)

mrc-ide/mipmapper documentation built on May 20, 2019, 3:27 p.m.