reml {gremlin} | R Documentation |
REML optimization algorithms for mixed-effect models.
Description
Evaluate the REML likelihood and algorithms for iterating to find maximum REML estimates.
Usage
reml(
nu,
skel,
thetaG,
sLc,
modMats,
W,
Bpinv,
nminffx,
nminfrfx,
rfxlvls,
rfxIncContrib2loglik,
thetaR = NULL,
tWW = NULL,
RHS = NULL
)
em(
nuvin,
thetaG,
thetaR,
conv,
rfxlvls,
tugug,
trace,
y = NULL,
r = NULL,
nminffx = NULL
)
ai(nuvin, skel, thetaG, modMats, W, sLc, sln, r, thetaR = NULL, sigma2e = NULL)
tugug_trace(thetaG, nb, rfxlvls, listGeninv, Cinv, sln, pinv = NULL)
gradFun(
nuvin,
thetaG,
rfxlvls,
sln,
tugug,
trace,
sigma2e = NULL,
r = NULL,
nminfrfx = NULL
)
gradFun_fd(nuvin, grObj, lL, fd = c("fdiff", "cdiff", "bdiff"))
Arguments
nu , nuvin |
A |
skel |
A skeleton for reconstructing the list of (co)variance parameters. |
thetaG , thetaR |
|
sLc |
A sparse |
modMats |
A |
W , tWW |
A sparse |
Bpinv |
A matrix inverse of the matrix containing the prior specification for fixed effects. |
nminffx , nminfrfx , rfxlvls |
|
rfxIncContrib2loglik |
A |
RHS |
A sparse |
conv |
A |
tugug |
A list of numeric values for the $u_g' u_g$ products of the
solution vector for each of the |
trace |
A list of traces of the inverse coefficient matrix for each of
of the |
y |
The response vector. |
sln , r |
Sparse |
sigma2e |
A |
nb |
The number of columns in X. |
listGeninv |
A list of generalized inverse matrices. |
Cinv |
A sparse |
pinv |
An integer vector of the matrix permutation. |
grObj |
An list of class |
lL |
A numeric value for REML log-likelihood value. |
fd |
A character indicating whether forward, combined, or backward finite differences (“fdiff”, “cdiff”, or “bdiff”, respectively) are to be calculated. |
Value
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.