Solving Mixed Model Equations in R
Additive relationship matrix
anova form a GLMM fitted with mmes
Autocorrelation matrix of order 1.
Autocorrelation Moving average.
atm covariance structure
coef form a GLMM fitted with mmes
Imputing a matrix using correlations
covariance between random effects
customized covariance structure
Compound symmetry matrix
Dominance relationship matrix
Genome wide association study analysis
summary form a GLMM fitted with mmer
variance structure specification
variance structure specification
data frame to matrix
diagonal covariance structure
Epistatic relationship matrix
fitted form a LMM fitted with mmes
fixed indication matrix
Combined relationship matrix H
identity covariance structure
m ixed m odel e quations for r records
m ixed m odel e quations s olver
Multivariate Newton-Raphson algorithm
plot form a LMM plot with mmes
plot the change of VC across iterations
Predict form of a LMM fitted with mmes
Reliability
extracting random effects
Residuals form a GLMM fitted with mmes
So lving M ixed M odel E quations in R
Two-dimensional penalised tensor-product of marginal B-Spline basis.
Get Tensor Product Spline Mixed Model Incidence Matrices
summary form a GLMM fitted with mmes
Get Tensor Product Spline Mixed Model Incidence Matrices
unstructured indication matrix
unstructured covariance structure
vpredict form of a LMM fitted with mmes
variance structure specification
Structural multivariate-univariate linear mixed model solver for estimation of multiple random effects with unknown variance-covariance structures (e.g., heterogeneous and unstructured) and known covariance among levels of random effects (e.g., pedigree and genomic relationship matrices) (Covarrubias-Pazaran, 2016 <doi:10.1371/journal.pone.0156744>; Maier et al., 2015 <doi:10.1016/j.ajhg.2014.12.006>; Jensen et al., 1997). REML estimates can be obtained using the Direct-Inversion Newton-Raphson and Direct-Inversion Average Information algorithms for the problems r x r (r being the number of records) or using the Henderson-based average information algorithm for the problem c x c (c being the number of coefficients to estimate). Spatial models can also be fitted using the two-dimensional spline functionality available.