MPAgenomics (version 1.2.3)

HDlarsbivariate: lars algorithm for bivariate signal

Description

This function transforms the two matrices CN and fracB in one matrix which is used in the lars algorithm. Each signal is weighted

Usage

HDlarsbivariate(
  CN,
  fracB,
  y,
  weightsCN = 1/apply(CN, 1, sd),
  weightsFracB = 1/apply(fracB, 1, sd),
  meanCN = 2,
  maxSteps,
  eps
)

Arguments

CN

matrix containing copy-number signals. Each row corresponds to a different signal.

fracB

matrix containing copy-number signals. Each row corresponds to a different signal.

y

vector containing the response associated to each signal

weightsCN

vector of length nrow(CN); weights associated to each signal for the copy-number signal

weightsFracB

vector of length nrow(fracB); weights associated to each signal for the copy-number signal

meanCN

value for centering the copy-number signal (default value = 2)

maxSteps

maximum number of steps for the lars algorithm

eps

tolerance

Value

a LarsPath object