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sommer: Solving Mixed Model Equations in R

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; Maier et al., 2015; 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.

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

4,275

Version

4.3.7

License

GPL (>= 2)

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/covaruber/sommer

Maintainer

Giovanny Covarrubias-Pazaran

Last Published

February 3rd, 2025

Functions in sommer (4.3.7)

Broad sense heritability calculation.

Full diallel data for corn hybrids

Data for different experimental designs

Blue Tit Data for a Quantitative Genetic Experiment

Broad sense heritability calculation.

Gryphon data from the Journal of Animal Ecology

half diallel data for corn hybrids

Simulated data for random regression

Data to fit indirect genetic effects.

Genome wide association study analysis

Two-way id by features table

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

wheat lines dataset

Expectation Maximization Algorithm

Full diallel data for corn hybrids

Yield of oats in a split-block experiment

Genotypic and Phenotypic data for a potato polyploid population

Epistatic relationship matrix

Rice lines dataset

Multivariate Efficient Mixed Model Association Algorithm

Binds arrays corner-to-corner

add.diallel.vars

add.diallel.vars

Generate a sequence of colors for plotting bathymetric data.

covariance between random effects

Imputing a matrix using correlations

Calculation of linkage disequilibrium decay

Combined relationship matrix H

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

atc covariance structure

anova form a GLMM fitted with mmer

Imputing a numeric or character vector

anova form a GLMM fitted with mmec

Letter to number converter

customized covariance structure

diagonal covariance structure

fitted form a LMM fitted with mmec

data frame to matrix

identity covariance structure

Effective population size based on marker matrix

fitted form a LMM fitted with mmer

Reaction times in a sleep deprivation study

atcg1234BackTransform

Letter to number converter

atr covariance structure

Build a hybrid marker matrix using parental genotypes from inbred individuals

list or vector to unstructured matrix

bivariateRun functionality

Legendre polynomial matrix

Generate a sequence of colors alog the jet colormap.

Decreasing logarithmic trend

plot the change of VC across iterations

plot form a LMM plot with mmec

mixed model equations for r records

mixed model equations for c coefficients

customized covariance structure

coef form a GLMM fitted with mmec

Get Tensor Product Spline Mixed Model Incidence Matrices

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

fixed indication matrix

diagonal covariance structure

coef form a GLMM fitted with mmer

Predict form of a LMM fitted with mmec

plot form a LMM plot with mmer

Create a GE correlation matrix for simulation purposes.

general variance structure specification

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

Solving Mixed Model Equations in R
Figure: mai.png

Figure: mai.png

Creating color with transparency

unstructured indication matrix

fixed effect constraint indication matrix

Predict form of a LMM fitted with mmer

Creating a manhattan plot

Creating a genetic map plot

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

Proportion of missing data

transformConstraints

transformConstraints

Get Tensor Product Spline Mixed Model Incidence Matrices

Residuals form a GLMM fitted with mmec

Reduced Model Matrix

unstructured covariance structure

Residuals form a GLMM fitted with mmer

unstructured covariance structure

reduced rank covariance structure

extracting random effects

vpredict form of a LMM fitted with mmer

variance structure specification

variance structure specification

Stacking traits in a dataset

summary form a GLMM fitted with mmer

variance structure specification

summary form a GLMM fitted with mmec

Get Tensor Product Spline Mixed Model Incidence Matrices

Wald Test for Model Coefficients

Corn crosses and markers

Genotypic and Phenotypic data for a CP population

Compound symmetry matrix

Autocorrelation matrix of order 1.

Dominance relationship matrix

Autocorrelation Moving average.

DT_augment design example.

Additive relationship matrix

Average Information Algorithm