Calculate linear associations

Calculate the linear model associations between dependent variables and response variable(s) of interest.

Usage

calc_assoc(X, Y)

Arguments

X

matrix dependent variables data matrix (rows are samples, columns are features). Must have the same number of rows as matrix Y or equal to length of vector Y

Y

vector or matrix experimental response data (rows are samples). When Y is a matrix must have the same number of rows as matrix X; when y is a vector - its length has to be equal to number of rows in matrix X.

Value

data.table with calculated linear associations

Note

inspired by the calc_assoc function written by James Hawley

Examples

X <- matrix(rep(1:13, length.out = 42), nrow = 6,
            dimnames = list(sprintf("row_%s", 1:6), sprintf("feat_%s", 1:7)))
Y <- matrix(c(10:15, 110:115, 210:215), ncol = 3,
            dimnames = list(sprintf("row_%s", 1:6), sprintf("met_%s", 1:3)))
tab_assoc <- calc_assoc(X, Y)