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sommer (version 2.5)

Solving Mixed Model Equations in R

Description

Multivariate mixed model solver for multiple random effects allowing the specification of variance covariance structures. ML/REML estimates are obtained using the Average Information (AI), Expectation-Maximization (EM), Newton-Raphson (NR), or Efficient Mixed Model Association (EMMA) algorithms. Designed for genomic prediction and genome wide association studies (GWAS) to include additive, dominance and epistatic relationship structures or other covariance structures in R, but also functional as a regular mixed model program. Multivariate models (multiple responses) can be fitted currently with NR, AI and EMMA algorithms allowing multiple random effects as well. Covariance structures for the residual component is currently supported only for balanced univariate Newton Raphson models.

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Version

Install

install.packages('sommer')

Monthly Downloads

4,370

Version

2.5

License

GPL-3

Maintainer

Giovanny Covarrubias-Pazaran

Last Published

January 3rd, 2017

Functions in sommer (2.5)

Binds arrays corner-to-corner

Average Information Algorithm

anova form a GLMM fitted with mmer

Letter to number converter

anova form a GLMM fitted with mmer

Generate a sequence of colors for plotting bathymetric data.

Average Information Algorithm

Generate a sequence of colors for groups.

augment design example.

Expectation Maximization Algorithm

Expectation Maximization Algorithm

coef form a GLMM fitted with mmer

Efficient Mixed Model Association Algorithm

Data for different experimental designs

fitted form a GLMM fitted with mmer

fitted form a GLMM fitted with mmer

Multivariate Average Information Algorithm

Creating a manhattan plot

Genotypic and Phenotypic data for a CP population

Unraveling selection signatures with eigenGWAS

Broad sense heritability calculation.

Hadamard product of two matrices

Half Diallel Matrix

Generate a sequence of colors alog the jet colormap.

Multivariate Average Information Algorithm

half diallel data for corn hybrids

renaming a vector by adding zeros

Gryphon data from the Journal of Animal Ecology

Multivariate Efficient Mixed Model Association Algorithm

Newton-Raphson Algorithm

Mixed Model Equations in R univariate

plot form a GLMM plot with mmer

Short poems from Latin America, and other places why not?

Newton-Raphson Algorithm including Residual structures

Newton-Raphson Algorithm

residuals.mmerM

Residuals form a GLMM fitted with mmer

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

Selecting the best training population for genomic selection

plot form a GLMM plot with mmer

Rice lines dataset

Corn crosses and markers

summary form a GLMM fitted with mmer

False Discovery Rate calculation

False Discovery Rate calculation

Creating a fixed effect matrix with significant GWAS markers

Creating a genetic map plot

Imputing a numeric or character vector

The trace of a matrix

Genotypic and Phenotypic data for a potato polyploid population

Residuals form a GLMM fitted with mmer

Yield of oats in a split-block experiment

wheat lines dataset

coef form a GLMM fitted with mmer

Genotypes from an F1(CP) cross to show phasing

Full diallel data for corn hybrids

is.diagonal.matrix

Test for diagonal square matrix

Multivariate Newton-Raphson Algorithm

is.square.matrix

Test for square matrix

All typical colors in R easy to access.

Selecting the best training population for genomic selection

Phasing F1 (CP) data in biparental populations

summary form a GLMM fitted with mmer