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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

Development

The sommer package is under active development. If you are an expert in mixed models, statistics or programming and you know how to implement of the following:

the minimum degree ordering algorithm
the symbolic cholesky factorization
factor analytic structure
generalized linear models

please help us to take sommer to the next level. Drop me an email or push some changes through github :)

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Version

Install

install.packages('sommer')

Monthly Downloads

4,080

Version

4.2.0.1

License

GPL (>= 2)

Maintainer

Giovanny Covarrubias-Pazaran

Last Published

January 8th, 2023

Functions in sommer (4.2.0.1)

Autocorrelation matrix of order 1.

Autocorrelation Moving average.

Dominance relationship matrix

Average Information Algorithm

Blue Tit Data for a Quantitative Genetic Experiment

Corn crosses and markers

Genotypic and Phenotypic data for a CP population

Compound symmetry matrix

Additive relationship matrix

DT_augment design example.

Data for different experimental designs

Broad sense heritability calculation.

half diallel data for corn hybrids

Broad sense heritability calculation.

Full diallel data for corn hybrids

Gryphon data from the Journal of Animal Ecology

Genotypic and Phenotypic data for a potato polyploid population

Full diallel data for corn hybrids

Data to fit indirect genetic effects.

Simulated data for random regression

Expectation Maximization Algorithm

Genome wide association study analysis

Yield of oats in a split-block experiment

Multivariate Efficient Mixed Model Association Algorithm

Binds arrays corner-to-corner

atr covariance structure

Epistatic relationship matrix

Letter to number converter

Combined relationship matrix H

atc covariance structure

add.diallel.vars

add.diallel.vars

anova form a GLMM fitted with mmec

wheat lines dataset

anova form a GLMM fitted with mmer

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

Calculation of linkage disequilibrium decay

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

Generate a sequence of colors for plotting bathymetric data.

bivariateRun functionality

Build a hybrid marker matrix using parental genotypes from inbred individuals

diagonal covariance structure

customized covariance structure

customized covariance structure

Rice lines dataset

coef form a GLMM fitted with mmer

coef form a GLMM fitted with mmec

Reaction times in a sleep deprivation study

data frame to matrix

Legendre polynomial matrix

diagonal covariance structure

general variance structure specification

Generate a sequence of colors alog the jet colormap.

fixed indication matrix

fitted form a LMM fitted with mmec

fixed effect constraint indication matrix

fitted form a LMM fitted with mmer

Imputing a numeric or character vector

identity covariance structure

list or vector to unstructured matrix

Creating a manhattan plot

Predict form of a LMM fitted with mmer

extracting random effects

Creating a genetic map plot

mixed model equations for c coefficients

plot form a LMM plot with mmer

mixed model equations for r records

Predict form of a LMM fitted with mmec

plot form a LMM plot with mmec

plot the change of VC across iterations

Create a GE correlation matrix for simulation purposes.

Solving Mixed Model Equations in R
Figure: mai.png

Figure: mai.png

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

summary form a GLMM fitted with mmec

Residuals form a GLMM fitted with mmer

Residuals form a GLMM fitted with mmec

summary form a GLMM fitted with mmer

unstructured covariance structure

Creating color with transparency

variance structure specification

vpredict form of a LMM fitted with mmer

Get Tensor Product Spline Mixed Model Incidence Matrices

transformConstraints

transformConstraints

unstructured indication matrix

unstructured covariance structure

variance structure specification

variance structure specification

Wald Test for Model Coefficients