HDlarsbivariate {MPAgenomics} | R Documentation |
This function transforms the two matrices CN and fracB in one matrix which is used in the lars algorithm. Each signal is weighted
HDlarsbivariate(CN, fracB, y, weightsCN = 1/apply(CN, 1, sd), weightsFracB = 1/apply(fracB, 1, sd), meanCN = 2, maxSteps, eps)
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 |
a LarsPath object
Quentin Grimonprez