Description Usage Arguments Details Value Author(s) References Examples
Rcpp implementation of the HCP algorithm
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Z |
a matrix nxd of known covariates, where n is the number of subjects and d is the number of known covariates. *must be standardize (columns have 0 mean and constant SS). |
Y |
a matrix of nxg of expression data (must be standardized (columns scaled to have constant SS and mean 0). ** use standardize function to standardize F and Y. |
k |
number of inferred hidden components (k is an integer) |
lambda1 |
model parameter 1 |
lambda2 |
model parameter 2 |
lambda3 |
model parameter 3 |
iter |
(optional) iter: number of iterations (default = 100); |
stand |
default standardize data TRUE |
log |
default log-transformation TRUE |
fast |
default use fast RcppArmadillo implementation |
verbose |
default TRUE |
Objective: This function solves the following problem: argmin_Z,B,U ||Y-Z*B||_2 + lambda1*||Z-F*U||_2 + lambda2*||B||_2 + lambda_3||U||_2
To use the residual data: Residual = Y - Z*B
Note: k>0, lambda1>0, lambda2>0, lambda3>0 must be set by the user based on the data at hand. one can set these values using cross-validation, by evaluating the "performance" of the resulting residual data on a desired task. typically, if lambda>5, then hidden factors match the known covariates closely.
list Z: matrix of hidden components, dimensionality: nxk, B: matrix of effects of hidden components, dimensionality: kxg, o: value of objective function on consecutive iterations.
mvaniterson
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0068141
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