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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

4.1.4

License

GPL (>= 2)

Maintainer

Giovanny Covarrubias-Pazaran

Last Published

July 29th, 2021

Functions in sommer (4.1.4)

Additive relationship matrix

Autocorrelation Moving average.

Blue Tit Data for a Quantitative Genetic Experiment

Genotypic and Phenotypic data for a CP population

Compound symmetry matrix

Autocorrelation matrix of order 1.

Dominance relationship matrix

DT_augment design example.

Corn crosses and markers

Broad sense heritability calculation.

half diallel data for corn hybrids

Gryphon data from the Journal of Animal Ecology

Broad sense heritability calculation.

Data for different experimental designs

Yield of oats in a split-block experiment

Full diallel data for corn hybrids

Multivariate Efficient Mixed Model Association Algorithm

Data to fit indirect genetic effects.

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

Simulated data for random regression

at covariance structure

Reaction times in a sleep deprivation study

wheat lines dataset

Build a hybrid marker matrix using parental genotypes from inbred individuals

coef form a GLMM fitted with mmer

Genotypic and Phenotypic data for a potato polyploid population

Letter to number converter

data frame to matrix

customized covariance structure

Combined relationship matrix H

Rice lines dataset

Calculation of linkage disequilibrium decay

Genome wide association study

Genome wide association study analysis

add.diallel.vars

add.diallel.vars

anova form a GLMM fitted with mmer

Binds arrays corner-to-corner

diagonal covariance structure

Full diallel data for corn hybrids

Create a GE correlation matrix for simulation purposes.

fixed effect constraint indication matrix

Residuals form a GLMM fitted with mmer

Generate a sequence of colors alog the jet colormap.

Imputing a numeric or character vector

extracting random effects

summary form a GLMM fitted with mmer

general variance structure specification

Epistatic relationship matrix

transformConstraints

transformConstraints

Predict form of a LMM fitted with mmer

Generate a sequence of colors for plotting bathymetric data.

Expectation Maximization Algorithm

bivariateRun functionality

fitted form a LMM fitted with mmer

fixed indication matrix

Creating color with transparency

Legendre polynomial matrix

plot form a LMM plot with mmer

Creating a manhattan plot

Creating a genetic map plot

unconstrained indication matrix

list or vector to unstructured matrix

unstructured indication matrix

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

Solving Mixed Model Equations in R Figure: mai.png

Figure: mai.png

unstructured covariance structure

Obtain heritabilities with three different methods

mixed model equations in R

mixed model equations in R

vpredict form of a LMM fitted with mmer

variance structure specification

Wald Test for Model Coefficients