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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

6,187

Version

4.3.6

License

GPL (>= 2)

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/covaruber/sommer

Maintainer

Giovanny Covarrubias-Pazaran

Last Published

October 20th, 2024

Functions in sommer (4.3.6)

Full diallel data for corn hybrids

Gryphon data from the Journal of Animal Ecology

Data to fit indirect genetic effects.

Simulated data for random regression

Data for different experimental designs

Epistatic relationship matrix

Reaction times in a sleep deprivation study

Genome wide association study analysis

Yield of oats in a split-block experiment

Two-way id by features table

Multivariate Efficient Mixed Model Association Algorithm

Expectation Maximization Algorithm

Rice lines dataset

wheat lines dataset

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

add.diallel.vars

add.diallel.vars

Combined relationship matrix H

atc covariance structure

Letter to number converter

atcg1234BackTransform

Letter to number converter

anova form a GLMM fitted with mmec

Binds arrays corner-to-corner

Calculation of linkage disequilibrium decay

atr covariance structure

Generate a sequence of colors for plotting bathymetric data.

customized covariance structure

Imputing a matrix using correlations

anova form a GLMM fitted with mmer

Build a hybrid marker matrix using parental genotypes from inbred individuals

bivariateRun functionality

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

fixed indication matrix

general variance structure specification

coef form a GLMM fitted with mmec

data frame to matrix

coef form a GLMM fitted with mmer

fixed effect constraint indication matrix

diagonal covariance structure

customized covariance structure

Imputing a numeric or character vector

identity covariance structure

diagonal covariance structure

fitted form a LMM fitted with mmec

covariance between random effects

fitted form a LMM fitted with mmer

Effective population size based on marker matrix

Legendre polynomial matrix

Creating a genetic map plot

list or vector to unstructured matrix

Generate a sequence of colors alog the jet colormap.

Creating a manhattan plot

Decreasing logarithmic trend

mixed model equations for c coefficients

mixed model equations for r records

Predict form of a LMM fitted with mmer

Proportion of missing data

plot the change of VC across iterations

plot form a LMM plot with mmec

extracting random effects

Residuals form a GLMM fitted with mmec

Reduced Model Matrix

plot form a LMM plot with mmer

Predict form of a LMM fitted with mmec

Stacking traits in a dataset

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

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

Create a GE correlation matrix for simulation purposes.

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

reduced rank covariance structure

Residuals form a GLMM fitted with mmer

Solving Mixed Model Equations in R
Figure: mai.png

Figure: mai.png

Get Tensor Product Spline Mixed Model Incidence Matrices

unstructured covariance structure

summary form a GLMM fitted with mmer

summary form a GLMM fitted with mmec

vpredict form of a LMM fitted with mmer

Get Tensor Product Spline Mixed Model Incidence Matrices

transformConstraints

transformConstraints

variance structure specification

unstructured indication matrix

Creating color with transparency

variance structure specification

variance structure specification

Wald Test for Model Coefficients

Get Tensor Product Spline Mixed Model Incidence Matrices

unstructured covariance structure

Autocorrelation matrix of order 1.

Compound symmetry matrix

Additive relationship matrix

Dominance relationship matrix

DT_augment design example.

Autocorrelation Moving average.

Average Information Algorithm

Broad sense heritability calculation.

Genotypic and Phenotypic data for a CP population

Broad sense heritability calculation.

Blue Tit Data for a Quantitative Genetic Experiment

Corn crosses and markers

Genotypic and Phenotypic data for a potato polyploid population

Full diallel data for corn hybrids

half diallel data for corn hybrids