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sommer (version 3.5)

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 variance models (Covarrubias-Pazaran, 2016 ; Maier et al., 2015 ). ML/REML estimates can be obtained using the Direct-Inversion Newton-Raphson, 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

3.5

License

GPL-3

Maintainer

Giovanny Covarrubias-Pazaran

Last Published

July 2nd, 2018

Functions in sommer (3.5)

False Discovery Rate calculation

Applying linear postblocking in a field

Letter to number converter

Generate a sequence of colors for groups.

Build a hybrid marker matrix using parental genotypes

Nearest neighbour adjustment

Half Diallel Matrix

Imputing a numeric or character vector

False Discovery Rate calculation

Creating a fixed effect matrix with significant GWAS markers

Broad sense heritability calculation.

All typical colors in R easy to access.

Hadamard product of two matrices

fitted form a GLMM fitted with mmer

Filling the design of an experiment

augment design example.

Generate a sequence of colors for plotting bathymetric data.

Applying transversal postblocking in a field

Phasing F1 (CP) data in biparental populations

is.diagonal.matrix

Test for diagonal square matrix

Creating a manhattan plot

design score for the model to be tested

pin functionality

coef form a GLMM fitted with mmer

Corn crosses and markers

extracting random effects

plot form a LMM plot with mmer

Creating a genetic map plot

Broad sense heritability calculation.

residuals.MMERM

Residuals form a GLMM fitted with mmer

Peak search by first derivatives

Pedigree to matrix

Generate a sequence of colors alog the jet colormap.

is.square.matrix

Test for square matrix

plot.variogram.MMERM

Default variogram.MMERM plotting

vctable functionality

summary form a GLMM fitted with mmer

Obtain heritabilities with three different methods

Sample variogram

Applying postblocking in a field

Gryphon data from the Journal of Animal Ecology

Creating color with transparency

Peak search by first derivatives

The trace of a matrix

mixed model equations in R

Score calculation for markers

vctable.help functionality

mixed model equations in R

Solving Mixed Model Equations in R Figure: mai.png

Figure: mai.png

Yield of oats in a split-block experiment

wheat lines dataset

Full diallel data for corn hybrids

Additive relationship matrix

Genotypes from an F1(CP) cross to show phasing

Blue Tit Data for a Quantitative Genetic Experiment

Genome Wide Association Analysis

Expectation Maximization Algorithm

Data for different experimental designs

Multivariate Efficient Mixed Model Association Algorithm

Genotypic and Phenotypic data for a CP population

Epistatic relationship matrix

Multivariate Newton-Raphson Algorithm

Binds arrays corner-to-corner

Rice lines dataset

Genotypic and Phenotypic data for a potato polyploid population

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

Dominance relationship matrix

Genome Wide Association Analysis

anova form a GLMM fitted with mmer

half diallel data for corn hybrids