deriv2LagrangianFeatures: The score function to estimate the latent variables

In CenterForStatistics-UGent/compIntegrate: Compositional omics model based visual integration

The score function to estimate the latent variables

Description

The score function to estimate the latent variables

Usage

deriv2LagrangianFeatures(
  x,
  data,
  distribution,
  offSet,
  latentVars,
  numVar,
  paramEstsLower,
  mm,
  Jac,
  meanVarTrend,
  weights,
  compositional,
  indepModel,
  ...
)

Arguments

x	parameter estimates
data	A list of data matrices
distribution, compositional, meanVarTrend, offSet, numVar	Characteristics of the view
latentVars	A vector of latent variables
paramEstsLower	lower dimension estimates
mm	the current dimension
Jac	a prefab jacobian
weights	The normalization weights
indepModel	the independence model
...	Additional arguments passed on to the score and jacobian functions

Value

A vector of length n, the evaluation of the score functions of the latent variables

CenterForStatistics-UGent/compIntegrate documentation built on Aug. 4, 2023, 1:08 p.m.