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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

4,273

Version

4.1.7

License

GPL (>= 2)

Maintainer

Giovanny Covarrubias-Pazaran

Last Published

June 16th, 2022

Functions in sommer (4.1.7)

DT_augment design example.

Dominance relationship matrix

Genotypic and Phenotypic data for a CP population

Autocorrelation matrix of order 1.

Compound symmetry matrix

Additive relationship matrix

Autocorrelation Moving average.

Average Information Algorithm

Corn crosses and markers

Blue Tit Data for a Quantitative Genetic Experiment

Broad sense heritability calculation.

Broad sense heritability calculation.

Gryphon data from the Journal of Animal Ecology

Full diallel data for corn hybrids

Data to fit indirect genetic effects.

Simulated data for random regression

Data for different experimental designs

half diallel data for corn hybrids

Genotypic and Phenotypic data for a potato polyploid population

Full diallel data for corn hybrids

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

Rice lines dataset

Reaction times in a sleep deprivation study

wheat lines dataset

Combined relationship matrix H

Genome wide association study analysis

Multivariate Efficient Mixed Model Association Algorithm

Expectation Maximization Algorithm

Generate a sequence of colors for plotting bathymetric data.

Letter to number converter

atr covariance structure

Yield of oats in a split-block experiment

diagonal covariance structure

data frame to matrix

Binds arrays corner-to-corner

anova form a GLMM fitted with mmec

diagonal covariance structure

Generate a sequence of colors alog the jet colormap.

anova form a GLMM fitted with mmer

Epistatic relationship matrix

atc covariance structure

fixed effect constraint indication matrix

Legendre polynomial matrix

coef form a GLMM fitted with mmec

add.diallel.vars

add.diallel.vars

Calculation of linkage disequilibrium decay

coef form a GLMM fitted with mmer

identity covariance structure

Imputing a numeric or character vector

list or vector to unstructured matrix

Creating a manhattan plot

mixed model equations for r records

Creating a genetic map plot

mixed model equations for c coefficients

customized covariance structure

fitted form a LMM fitted with mmec

customized covariance structure

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

Predict form of a LMM fitted with mmer

fitted form a LMM fitted with mmer

extracting random effects

plot the change of VC across iterations

plot form a LMM plot with mmec

Build a hybrid marker matrix using parental genotypes from inbred individuals

bivariateRun functionality

fixed indication matrix

plot form a LMM plot with mmer

general variance structure specification

Predict form of a LMM fitted with mmec

Get Tensor Product Spline Mixed Model Incidence Matrices

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

Residuals form a GLMM fitted with mmec

Residuals form a GLMM fitted with mmer

summary form a GLMM fitted with mmer

summary form a GLMM fitted with mmec

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

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

unstructured covariance structure

Create a GE correlation matrix for simulation purposes.

Creating color with transparency

Get Tensor Product Spline Mixed Model Incidence Matrices

Solving Mixed Model Equations in R
Figure: mai.png

Figure: mai.png

unstructured covariance structure

unstructured indication matrix

variance structure specification

variance structure specification

Wald Test for Model Coefficients

transformConstraints

transformConstraints

variance structure specification

vpredict form of a LMM fitted with mmer