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
)
A list or vector of (co)variance parameters to
estimate on the transformed, or nu, scale.
A skeleton for reconstructing the list of (co)variance parameters.
Integer vectors indexing the G-structure or
R-structure of the (co)variance parameters.
A sparse Matrix containing the symbolic Cholesky
factorization of the coefficient matrix of the Mixed Model Equations.
A list of the model matrices used to construct the
mixed model equations.
A sparse Matrix containing the design matrices for the fixed
and random effects (W) and the crossproduct of this (tWW).
A matrix inverse of the matrix containing the prior specification for fixed effects.
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.
A numeric indicating the sum of constrant
contributions to the log-likelihood across all terms in the random effects
that have non-diagonal generalized inverse matrices (ginverse).
A sparse Matrix containing the Right-Hand Side to the
Mixed Model Equations.
A character vector of (co)variance parameter constraints.
A logical vector indicating if each random term is
associated with a generalized inverse (ginverse).
Sparse Matrices containing the solutions or residuals
of the Mixed Model Equations.
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).
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:
The REML log-likelihood.
Components of the REML log-likelihood derived from the Cholesky factor of the Coefficient matrix to the Mixed Model Equations.
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).
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.
A single column matrix of first derivatives of
the transformed (co)variance parameters (nu) with respect to the
log-Likelihood.