reml: REML optimization algorithms for mixed-effect models.
In matthewwolak/gremlin: Mixed-Effects REML Incorporating Generalized Inverses

Description Usage Arguments Value Author(s)

View source: R/remlOptimization_algorithms.R

Evaluate the REML likelihood and algorithms for iterating to find maximum REML estimates.

reml(
  nu,
  skel,
  thetaG,
  sLc,
  modMats,
  W,
  Bpinv,
  nminffx,
  nminfrfx,
  rfxlvls,
  rfxIncContrib2loglik,
  thetaR = NULL,
  tWW = NULL,
  RHS = NULL
)

em(nuvin, thetaG, thetaR, conv, modMats, nminffx, sLc, ndgeninv, sln, r)

ai(nuvin, skel, thetaG, modMats, W, sLc, sln, r, thetaR = NULL, sigma2e = NULL)

gradFun(
  nuvin,
  thetaG,
  modMats,
  Cinv,
  sln,
  sigma2e = NULL,
  r = NULL,
  nminfrfx = NULL
)

`nu, nuvin`	A `list` or `vector` of (co)variance parameters to estimate on the transformed, or nu, scale.
`skel`	A skeleton for reconstructing the list of (co)variance parameters.
`thetaG, thetaR`	`Integer` vectors indexing the G-structure or R-structure of the (co)variance parameters.
`sLc`	A sparse `Matrix` containing the symbolic Cholesky factorization of the coefficient matrix of the Mixed Model Equations.
`modMats`	A `list` of the model matrices used to construct the mixed model equations.
`W, tWW`	A sparse `Matrix` containing the design matrices for the fixed and random effects (`W`) and the cross-product of this (`tWW`).
`Bpinv`	A matrix inverse of the matrix containing the prior specification for fixed effects.
`nminffx, nminfrfx, rfxlvls`	`Integers` specifying: (1) the difference between the number of observations and fixed effects (of the full rank fixed effects design matrix (X), (2) `nminffx` minus the total number of random effects, and (3) a `vector` of levels for each term in the random effects.
`rfxIncContrib2loglik`	A `numeric` indicating the sum of constraint contributions to the log-likelihood across all terms in the random effects that have non-diagonal generalized inverse matrices (`ginverse`).
`RHS`	A sparse `Matrix` containing the Right-Hand Side to the Mixed Model Equations.
`conv`	A `character` vector of (co)variance parameter constraints.
`ndgeninv`	A `logical` vector indicating if each random term is associated with a generalized inverse (`ginverse`).
`sln, r`	Sparse `Matrices` containing the solutions or residuals of the Mixed Model Equations.
`sigma2e`	A `numeric` value for the residual variance estimate when it has been factored out of the Coefficient matrix of the Mixed Model Equations, thus converting the (co)variance components to ratios (represented by the variable lambda).
`Cinv`	A sparse `Matrix` containing the inverse of the Coefficient matrix to the Mixed Model Equations.

A list or matrix containing any of the previous parameters described above, or the following that are in addition to or instead of parameters above:

loglik: The REML log-likelihood.
tyPy,logDetC: Components of the REML log-likelihood derived from the Cholesky factor of the Coefficient matrix to the Mixed Model Equations.
Cinv_ii: A vector containing the diagonal elements of the inverse of the Coefficient matrix to the Mixed Model Equations (i.e., the diagonal entries of Cinv).
AI: A matrix of values containing the Average Information matrix, or second partial derivatives of the likelihood with respect to the transformed (co)variance components (nu). The inverse of this matrix gives the sampling variances of these transformed (co)variance components.
dLdnu: A single column matrix of first derivatives of the transformed (co)variance parameters (nu) with respect to the log-Likelihood.