testGlasso {detectR}

Test for for the equality of connectivity based on the Graphical lasso estimation.

Description

This function utilizes Dynamic Connectivity Regression (DCR) algorithm proposed by Cribben el al. (2012) to test the equality of connectivity in two fMRI signals.

Usage

testGlasso(
  subY1,
  subY2,
  p,
  lambda = "bic",
  nboot = 100,
  n.cl,
  bound = c(0.001, 1),
  gridTF = FALSE
)

Arguments

subY1

a sample of size length*dim

subY2

a sample of size length*dim

p

Gep(p) distribution controls the size of stationary bootstrap. The mean block length is 1/p

lambda

two selections possible for optimal parameter of lambda. "bic" finds lambda from bic criteria, or user can directly input the penalty value.

nboot

the number of bootstrap sample for p-value. Default is 100.

n.cl

number of cores in parallel computing. The default is (machine cores - 1)

bound

bound of bic search in "bic" rule. Default is (.001, 1)

gridTF

Utilize a grid search to optimize hyperparameters

Value

pval The empirical p-value for testing the equality of connectivity structure

rho The sequence of penalty parameter based on the combined sample, subY1 and subY2.

fit0 Output of glasso for combined sample

fit1 Output of glasso for subY1

fit2 Output of glasso for subY2

Examples

test1= testGlasso(testsim$X, testsim$Y, n.cl=1)