netprioR

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Description

Class that represents a netprioR model.

Usage

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netprioR(networks, phenotypes, labels, ...)

## S4 method for signature 'list,matrix,factor'
netprioR(networks, phenotypes, labels,
  fit.model = FALSE, a = 0.1, b = 0.1, sigma2 = 0.1, tau2 = 100,
  eps = 1e-10, max.iter = 500, thresh = 1e-06, use.cg = FALSE,
  thresh.cg = 1e-06, nrestarts = 5, max.cores = detectCores(),
  verbose = TRUE, ...)

Arguments

networks

List of NxN adjacency matrices of gene-gene similarities

phenotypes

Matrix of dimension NxP containing covariates

labels

Vector of Nx1 labels for all genes (NA if no label available)

...

Additional arguments

fit.model

Indicator whether to fit the model

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

A netprioR object

Slots

networks

List of NxN adjacency matrices of gene-gene similarities

phenotypes

Matrix of dimension NxP containing covariates

labels

Vector of Nx1 labels for all genes. NA if no label available.

is.fitted

Flag indicating if model is fitted

model

List containing estimated parameters and imputed missing data

Author(s)

Fabian Schmich

Examples

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 # runs long-ish
data(simulation)
np <- netprioR(networks = simulation$networks,
               phenotypes = simulation$phenotypes,
               labels = simulation$labels.obs,
               fit.model = TRUE)
summary(np)