Solving Mixed Model Equations in R
Creating color with transparency
anova form a GLMM fitted with mmer
Autocorrelation matrix of order 1.
Additive relationship matrix
add.diallel.vars
Binds arrays corner-to-corner
Average Information Algorithm
anova form a GLMM fitted with mmec
Autocorrelation Moving average.
atc covariance structure
Letter to number converter
Letter to number converter
atr covariance structure
Generate a sequence of colors for plotting bathymetric data.
Function for creating B-spline basis functions (Eilers & Marx, 2010)
bivariateRun functionality
Build a hybrid marker matrix using parental genotypes from inbred indi...
coef form a GLMM fitted with mmec
coef form a GLMM fitted with mmer
Imputing a matrix using correlations
covariance between random effects
customized covariance structure
Compound symmetry matrix
customized covariance structure
Dominance relationship matrix
data frame to matrix
diagonal covariance structure
diagonal covariance structure
Generate a sequence of colors alog the jet colormap.
Epistatic relationship matrix
Expectation Maximization Algorithm
Multivariate Efficient Mixed Model Association Algorithm
fixed effect constraint indication matrix
fitted form a LMM fitted with mmec
fitted form a LMM fitted with mmer
fixed indication matrix
general variance structure specification
Genome wide association study analysis
Combined relationship matrix H
Two-way id by features table
Imputing a numeric or character vector
identity covariance structure
Calculation of linkage disequilibrium decay
Legendre polynomial matrix
list or vector to unstructured matrix
Decreasing logarithmic trend
Creating a manhattan plot
Creating a genetic map plot
m ixed m odel e quations for c coefficients
m ixed m odel e quations for r records
Proportion of missing data
Effective population size based on marker matrix
Overlay Matrix
plot form a LMM plot with mmec
plot form a LMM plot with mmer
plot the change of VC across iterations
Predict form of a LMM fitted with mmec
Predict form of a LMM fitted with mmer
Reliability
extracting random effects
Reduced Model Matrix
Residuals form a GLMM fitted with mmec
Residuals form a GLMM fitted with mmer
reduced rank covariance structure
Create a GE correlation matrix for simulation purposes.
transformConstraints
So lving M ixed M odel E quations in R
Two-dimensional penalised tensor-product of marginal B-Spline basis.
Two-dimensional penalised tensor-product of marginal B-Spline basis.
Two-dimensional penalised tensor-product of marginal B-Spline basis.
Get Tensor Product Spline Mixed Model Incidence Matrices
Stacking traits in a dataset
summary form a GLMM fitted with mmec
summary form a GLMM fitted with mmer
Get Tensor Product Spline Mixed Model Incidence Matrices
Get Tensor Product Spline Mixed Model Incidence Matrices
unstructured indication matrix
unstructured covariance structure
unstructured covariance structure
vpredict form of a LMM fitted with mmer
variance structure specification
variance structure specification
variance structure specification
Wald Test for Model Coefficients
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.