Solving Mixed Model Equations in R
Additive relationship matrix
add.diallel.vars
Binds arrays corner-to-corner
anova form a GLMM fitted with mmes
Autocorrelation matrix of order 1.
Autocorrelation Moving average.
Letter to number converter
Letter to number converter
atm covariance structure
Generate a sequence of colors for plotting bathymetric data.
Function for creating B-spline basis functions (Eilers & Marx, 2010)
Build a hybrid marker matrix using parental genotypes from inbred indi...
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
m ixed m odel e quations for r records
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
Imputing a numeric or character vector
identity covariance structure
Generate a sequence of colors alog the jet colormap.
Calculation of linkage disequilibrium decay
Legendre polynomial matrix
Decreasing logarithmic trend
Creating a manhattan plot
Creating a genetic map plot
m ixed m odel e quations s olver
Effective population size based on marker matrix
Overlay Matrix
plot form a LMM plot with mmes
plot the change of VC across iterations
Predict form of a LMM fitted with mmes
Proportion of missing data
Reliability
extracting random effects
Reduced Model Matrix
Residuals form a GLMM fitted with mmes
reduced rank covariance structure
Create a GE correlation matrix for simulation purposes.
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
Stacking traits in a dataset
Standardize a vector of values in range 0 to 1
summary form a GLMM fitted with mmes
Get Tensor Product Spline Mixed Model Incidence Matrices
Get Tensor Product Spline Mixed Model Incidence Matrices
Creating color with transparency
unstructured indication matrix
unstructured covariance structure
vpredict form of a LMM fitted with mmes
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.