FitBeta: Update the beta weights according to the gradient ascent

In Rmomal/EMtree: Infers Direct Species Association Networks using Tree Averaging

Update the beta weights according to the gradient ascent

Description

Update the beta weights according to the gradient ascent

Usage

FitBeta(
  beta.init,
  psi,
  maxIter = 50,
  eps = 1e-06,
  unlinked = NULL,
  sum.weights
)

Arguments

beta.init	Initial beta weight matrix
psi	Psi matrix, filled with ratios of bivariate probabilities over marginals, which can in the Gaussian case be deduced from the correlation matrix.
maxIter	Maximum number of iterations
eps	Precision parameter controlling the convergence of weights beta
unlinked	An optional vector of nodes which are not linked with each other
sum.weights	Sum constraint for the weight matrix

Value

• edges_prob: p x p matrix of edges probabilities

• edges_weight: p x p matrix of edges weights for any spanning tree

• logpY: vector of log-likelihoods

• maxIter: final number of iterations EMtree has ran

• timeEM: EMtree computation time

Examples

set.seed(1)
n=50
p=10
Y=data_from_scratch("tree",p=p,n=n)$data
mean.val=exp((-(p-2)*log(p))/(p-1))
beta = matrix(mean.val, p, p);  diag(beta)=0
psi=Psi_alpha(cor(Y), n)$psi
FitEM = FitBeta(beta.init=beta, psi=psi, maxIter = 6, sum.weights=sum(beta))

Rmomal/EMtree documentation built on Dec. 14, 2024, 8:16 a.m.