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sommer: Solving Mixed Model Equations in R

Structural multivariate-univariate linear mixed model solver for estimation of multiple random effects and unknown variance-covariance structures (i.e. heterogeneous and unstructured variance models) (Covarrubias-Pazaran, 2016; Maier et al., 2015). REML estimates can be obtained using the Direct-Inversion Newton-Raphson and Direct-Inversion Average Information algorithms. Designed for genomic prediction and genome wide association studies (GWAS), particularly focused in the p > n problem (more coefficients than observations) and dense known covariance structures for levels of random effects. Spatial models can also be fitted using i.e. the two-dimensional spline functionality available in sommer.

Installation

You can install the development version of sommer from GitHub:

devtools::install_github('covaruber/sommer')

Vignettes

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Version

Install

install.packages('sommer')

Monthly Downloads

7,425

Version

4.1.6

License

GPL (>= 2)

Maintainer

Giovanny Covarrubias-Pazaran

Last Published

April 17th, 2022

Functions in sommer (4.1.6)

Autocorrelation matrix of order 1.

Compound symmetry matrix

DT_augment design example.

Autocorrelation Moving average.

Additive relationship matrix

Dominance relationship matrix

Broad sense heritability calculation.

Genotypic and Phenotypic data for a CP population

Blue Tit Data for a Quantitative Genetic Experiment

Corn crosses and markers

Data for different experimental designs

Genotypic and Phenotypic data for a potato polyploid population

Multivariate Efficient Mixed Model Association Algorithm

Genome wide association study analysis

Full diallel data for corn hybrids

Gryphon data from the Journal of Animal Ecology

Broad sense heritability calculation.

Epistatic relationship matrix

Build a hybrid marker matrix using parental genotypes from inbred individuals

Expectation Maximization Algorithm

bivariateRun functionality

Rice lines dataset

fixed indication matrix

Simulated data for random regression

Full diallel data for corn hybrids

Generate a sequence of colors for plotting bathymetric data.

Genotypic and Phenotypic data from single cross hybrids (Technow et al.,2014)

general variance structure specification

anova form a GLMM fitted with mmer

Binds arrays corner-to-corner

Reaction times in a sleep deprivation study

Creating a genetic map plot

Generate a sequence of colors alog the jet colormap.

Letter to number converter

at covariance structure

half diallel data for corn hybrids

Function for creating B-spline basis functions (Eilers & Marx, 2010)

Legendre polynomial matrix

data frame to matrix

Creating color with transparency

diagonal covariance structure

Create a GE correlation matrix for simulation purposes.

vpredict form of a LMM fitted with mmer

variance structure specification

Solving Mixed Model Equations in R Figure: mai.png

Figure: mai.png

Get Tensor Product Spline Mixed Model Incidence Matrices

Obtain heritabilities with three different methods

Yield of oats in a split-block experiment

Data to fit indirect genetic effects.

mixed model equations in R

Genome wide association study

coef form a GLMM fitted with mmer

summary form a GLMM fitted with mmer

wheat lines dataset

unconstrained indication matrix

Combined relationship matrix H

add.diallel.vars

add.diallel.vars

fitted form a LMM fitted with mmer

list or vector to unstructured matrix

Calculation of linkage disequilibrium decay

fixed effect constraint indication matrix

customized covariance structure

plot form a LMM plot with mmer

Imputing a numeric or character vector

mixed model equations in R

Predict form of a LMM fitted with mmer

Get Tensor Product Spline Mixed Model Incidence Matrices

Residuals form a GLMM fitted with mmer

Wald Test for Model Coefficients

extracting random effects

Two-dimensional penalised tensor-product of marginal B-Spline basis.

Creating a manhattan plot

Two-dimensional penalised tensor-product of marginal B-Spline basis.

transformConstraints

transformConstraints

unstructured indication matrix

unstructured covariance structure