cpp.airwls.update: AIRWLS update for GMF models

In sgdGMF: Estimation of Generalized Matrix Factorization Models via Stochastic Gradient Descent

AIRWLS update for GMF models

Description

Internal function implementing one step of AIRWLS for the estimation of GMF models.

Usage

cpp.airwls.update(
  beta,
  Y,
  X,
  familyname,
  linkname,
  varfname,
  idx,
  offset,
  weights,
  penalty,
  transp = FALSE,
  nsteps = 100L,
  stepsize = 0.1,
  print = FALSE,
  parallel = FALSE,
  nthreads = 1L
)

Arguments

beta	initial value of the regression coefficients to be estimated
Y	response vector
X	design matrix
familyname	model family name
linkname	link function name
varfname	variance function name
idx	index identifying the parameters to be updated in beta
offset	vector of constants to be added to the linear predictor
weights	vector of constants non-negative weights
penalty	penalty parameter of a ridge-type penalty
transp	if TRUE, transpose the data
nsteps	number of iterations
stepsize	stepsize parameter of the Fisher scoring algorithm
print	if TRUE, print the algorithm history
parallel	if TRUE, run the updates in parallel using openMP
nthreads	number of threads to be run in parallel (only if parallel=TRUE)

sgdGMF documentation built on April 3, 2025, 7:37 p.m.