Description Usage Arguments Value Functions Author(s) References Examples
Create and fit linear mixedeffect model (Gaussian data) or checking if an object is a fitted model.
1 2 3 4 5 6 7  gremlinR(formula, random = NULL, rcov = ~units, data = NULL,
ginverse = NULL, Gstart = NULL, Rstart = NULL, Bp = NULL,
maxit = 20, algit = NULL, vit = 10, v = 1, ...)
mkModMats(formula, random = NULL, rcov = ~units, data = NULL,
subset = NULL, ginverse = NULL, na.action = na.pass,
offset = NULL, contrasts = NULL, Xsparse = TRUE, ...)

formula 
A 
random 
A 
rcov 
A 
data 
A 
ginverse 
A 
Gstart 
A 
Rstart 
A 
Bp 
A prior specification for fixed effects. 
maxit 
An 
algit 
A 
vit 
An 
v 
An 
... 
Additional arguments to be passed to control the model fitting. 
subset 
An expression for the subset of 
na.action 
What to do with NAs. 
offset 
Should an offset be specified. 
contrasts 
Specify the type of contrasts for the fixed effects. 
Xsparse 
Should sparse matrices be used for the fixed effects design matrix. 
A list
of class gremlin
or gremlinModMats
:
The model call
.
A list
of the model matrices used to construct the
mixed model equations.
The response vector.
The number of responses.
The number of columns of the original response.
The fixed effects design matrix.
The number of columns in X.
The residual design matrix.
A list of the design matrices for each random term.
The number of parameters in the G structure.
A list of generalized inverse matrices.
The logdeterminants of the generalized inverse matrices  necessary to calculate the loglikelihood.
A matrix
of details about each iteration.
A two column matrix
of solutions and their sampling
variances from the mixed model.
A vector
of residual deviations, response minus
the values expected based on the solutions, corresponding to the order
in modMats$y
.
A matrix
of (co)variance components at the last
iteration.
A matrix
of values containing the Average Information
matrix, or second partial derivatives of the likelihood with respect to
the (co)variance components. The inverse of this matrix gives the
sampling variances of the (co)variance components.
A single column matrix
of first derivatives of
the (co)variance parameters with respect to the logLikelihood.
mkModMats
: Generates model matrices.
Henderson Mrode. 2005.
1 2 3 4 5 6 7 8 9 10 
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