learn: Fit netprioR model

In netprioR: A model for network-based prioritisation of genes

Description

Infer parameters and hidden data using the EM algorithm of netprioR

Usage

learn(Yobs, X, G, l, u, a = 0.1, b = 0.1, sigma2 = 1, tau2 = 10,
  eps = 1e-11, max.iter = 500, thresh = 0.001, use.cg = TRUE,
  thresh.cg = 1e-05, nrestarts = 5, max.cores = detectCores(),
  verbose = FALSE)

Arguments

Yobs	Observed labels (NA, if not observed)
X	Phenotypes
G	Graph Laplacians
l	Indices of labelled instances
u	Indices of unlabelled instances
a	Shape parameter of Gamma prior for W
b	Scale parameter of Gamma prior for W
sigma2	Cariance for Gaussian labels
tau2	Variance for Gaussian prior for beta
eps	Small value added to diagonal of Q in order to make it non-singular
max.iter	Maximum number of iterations for EM
thresh	Threshold for termination of EM with respect to change in parameters
use.cg	Flag whether to use conjugate gradient instead of exact computation of expectations
thresh.cg	Threshold for the termination of the conjugate gradient solver
nrestarts	Number of restarts for EM
max.cores	Maximum number of cores to use for parallel computation
verbose	Print verbose output

Value

List containing: Predicted labels Yhat and inferred parameters W and beta

Author(s)

Fabian Schmich

netprioR documentation built on Nov. 8, 2020, 5:40 p.m.