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sommer (version 3.7.1)

Solving Mixed Model Equations in R

Description

Structural multivariate-univariate linear mixed model solver for multiple random effects allowing the specification of variance-covariance structures for random effects and allowing the fit of heterogeneous and special variance models (Covarrubias-Pazaran, 2016 ; Maier et al., 2015 ). ML/REML estimates can be obtained using the Direct-Inversion Newton-Raphson, Direct-Inversion Average Information and Efficient Mixed Model Association algorithms. Designed for genomic prediction and genome wide association studies (GWAS), particularly focused in the p > n problem (more coefficients than observations) to include multiple relationship matrices or other covariance structures. Spatial models can be fitted using the two-dimensional spline functionality in sommer.

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Version

Install

install.packages('sommer')

Monthly Downloads

4,273

Version

3.7.1

License

GPL (>= 2)

Maintainer

Giovanny Covarrubias-Pazaran

Last Published

December 7th, 2018

Functions in sommer (3.7.1)

Autocorrelation matrix of order 1.

Letter to number converter

at covariance structure

Genotypic and Phenotypic data for a potato polyploid population

Obtain heritabilities with three different methods

Legendre polynomial matrix

fixed indication matrix

Gryphon data from the Journal of Animal Ecology

Broad sense heritability calculation.

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

Data for different experimental designs

Epistatic relationship matrix

Yield of oats in a split-block experiment

Multivariate Efficient Mixed Model Association Algorithm

Expectation Maximization Algorithm

Full diallel data for corn hybrids

list or vector to unstructured matrix

wheat lines dataset

Rice lines dataset

Genome wide association study

half diallel data for corn hybrids

extracting random effects

Simulated data for random regression

Predict form a LMM fitted with mmer

Generate a sequence of colors for plotting bathymetric data.

coef form a GLMM fitted with mmer

Genome wide association study

customized covariance structure

Binds arrays corner-to-corner

anova form a GLMM fitted with mmer

diagonal covariance structure

Filling the design of an experiment

unstructured covariance structure

Build a hybrid marker matrix using parental genotypes

Imputing a numeric or character vector

Generate a sequence of colors alog the jet colormap.

fitted form a GLMM fitted with mmer

mixed model equations in R

Creating a manhattan plot

Creating a genetic map plot

summary form a GLMM fitted with mmer

mixed model equations in R

variance structure

Residuals form a GLMM fitted with mmer

fixed effect constraint indication matrix

Pedigree to matrix

Solving Mixed Model Equations in R Figure: mai.png

Figure: mai.png

Creating color with transparency

unconstrained indication matrix

unstructured indication matrix

pin functionality

plot form a LMM plot with mmer

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

Dominance relationship matrix

Genotypic and Phenotypic data for a CP population

DT_augment design example.

Broad sense heritability calculation.

Blue Tit Data for a Quantitative Genetic Experiment

Autocorrelation Moving average.

Compound symmetry matrix

Corn crosses and markers

Additive relationship matrix