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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

7,620

Version

4.3.5

License

GPL (>= 2)

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/covaruber/sommer

Maintainer

Giovanny Covarrubias-Pazaran

Last Published

July 31st, 2024

Functions in sommer (4.3.5)

Full diallel data for corn hybrids

Data for different experimental designs

Full diallel data for corn hybrids

Broad sense heritability calculation.

Broad sense heritability calculation.

Data to fit indirect genetic effects.

Gryphon data from the Journal of Animal Ecology

Genotypic and Phenotypic data for a potato polyploid population

half diallel data for corn hybrids

Simulated data for random regression

Epistatic relationship matrix

Multivariate Efficient Mixed Model Association Algorithm

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

Expectation Maximization Algorithm

Two-way id by features table

Yield of oats in a split-block experiment

Genome wide association study analysis

wheat lines dataset

Reaction times in a sleep deprivation study

Rice lines dataset

anova form a GLMM fitted with mmec

Calculation of linkage disequilibrium decay

Letter to number converter

add.diallel.vars

add.diallel.vars

Combined relationship matrix H

atc covariance structure

atcg1234BackTransform

Letter to number converter

atr covariance structure

anova form a GLMM fitted with mmer

Binds arrays corner-to-corner

Imputing a matrix using correlations

customized covariance structure

Generate a sequence of colors for plotting bathymetric data.

coef form a GLMM fitted with mmec

covariance between random effects

bivariateRun functionality

Build a hybrid marker matrix using parental genotypes from inbred individuals

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

fitted form a LMM fitted with mmec

identity covariance structure

customized covariance structure

Imputing a numeric or character vector

fitted form a LMM fitted with mmer

diagonal covariance structure

coef form a GLMM fitted with mmer

fixed effect constraint indication matrix

diagonal covariance structure

data frame to matrix

general variance structure specification

fixed indication matrix

Legendre polynomial matrix

list or vector to unstructured matrix

Effective population size based on marker matrix

Generate a sequence of colors alog the jet colormap.

mixed model equations for c coefficients

Decreasing logarithmic trend

Creating a manhattan plot

Creating a genetic map plot

mixed model equations for r records

Proportion of missing data

Predict form of a LMM fitted with mmer

Reduced Model Matrix

plot form a LMM plot with mmer

Predict form of a LMM fitted with mmec

plot form a LMM plot with mmec

plot the change of VC across iterations

Residuals form a GLMM fitted with mmec

extracting random effects

Stacking traits in a dataset

Create a GE correlation matrix for simulation purposes.

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

Solving Mixed Model Equations in R
Figure: mai.png

Figure: mai.png

Residuals form a GLMM fitted with mmer

summary form a GLMM fitted with mmec

Get Tensor Product Spline Mixed Model Incidence Matrices

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

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

reduced rank covariance structure

vpredict form of a LMM fitted with mmer

variance structure specification

unstructured covariance structure

transformConstraints

transformConstraints

Get Tensor Product Spline Mixed Model Incidence Matrices

Get Tensor Product Spline Mixed Model Incidence Matrices

summary form a GLMM fitted with mmer

unstructured indication matrix

unstructured covariance structure

Creating color with transparency

Wald Test for Model Coefficients

variance structure specification

variance structure specification

Autocorrelation matrix of order 1.

Genotypic and Phenotypic data for a CP population

Dominance relationship matrix

Compound symmetry matrix

Additive relationship matrix

Average Information Algorithm

Autocorrelation Moving average.

Blue Tit Data for a Quantitative Genetic Experiment

DT_augment design example.

Corn crosses and markers