computeLoglikeFromPartition: Compute the log-likelihood of the model

In shock: Slope Heuristic for Block-Diagonal Covariance Selection in High Dimensional Gaussian Graphical Models

Description

This function computes the log-likelihood of a multivariate Gaussian model with a block-diagonal covariance matrix.

Usage

computeLoglikeFromPartition(labels, expdata)

Arguments

labels	vector of block labels for each variable
expdata	matrix of data

Details

This function computes the log-likelihood of a multivariate Gaussian model with a block-diagonal covariance matrix described in the labels vector.

Value

loglike	loglikehood of the model
df	degree of freedom of the model
labels	labels provided as input

Examples

## load data to test
 data(dataTest)
 
## threshold of absS matrix
myLABELS <- thresholdAbsSPath(dataTest)$partitionList

## compute loglikelihood 
logLikePath <- lapply(myLABELS, function(x) computeLoglikeFromPartition(x,dataTest))

shock documentation built on May 2, 2019, 8:55 a.m.