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sommer (version 1.5)

Solving Mixed Model Equations in R

Description

Mixed model equation solver allowing the specification of variance covariance structures of random effects and residual structures. ML/REML estimates are obtained using the Average Information, Expectation-Maximization or Efficient Mixed Model Association algorithms. Designed for genomic prediction and genome wide association studies (GWAS) to include additive, dominant and epistatic relationship structures or other covariance structures in R, but functional as a regular mixed model program.

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Version

Install

install.packages('sommer')

Monthly Downloads

4,273

Version

1.5

License

GPL-3

Maintainer

Giovanny CovarrubiasPazaran

Last Published

May 3rd, 2016

Functions in sommer (1.5)

D.mat

Dominance relationship matrix
AI

Average Information Algorithm
AI2

Average Information Algorithm
atcg1234

Letter to number converter
HDdata

half diallel data for corn hybrids
E.mat

Epistatic relationship matrix
EM

Expectation Maximization Algorithm
randef

extracting random effects
sommer-package

Solving Mixed Model Equations in R
design.score

design score for the model to be tested
brewer.pal

Generate a sequence of colors for groups.
poe

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

Corn crosses and markers
coef.mmer

coef form a GLMM fitted with mmer
jet.colors

Generate a sequence of colors alog the jet colormap.
anova.mmer

anova form a GLMM fitted with mmer
residuals.mmer

Residuals form a GLMM fitted with mmer
PolyData

Genotypic and Phenotypic data for a potato polyploid population
A.mat

Additive relationship matrix
RICE

wheat lines dataset
adiag1

Binds arrays corner-to-corner
fdr

False Discovery Rate calculation
AI3

Average Information Algorithm
plot.mmer

plot form a GLMM plot with mmer
hdm

Half Diallel Matrix
EMMA

Efficient Mixed Model Association Algorithm
EM2

Expectation Maximization Algorithm
fitted.mmer

fitted form a GLMM fitted with mmer
summary.mmer

summary form a GLMM fitted with mmer
wheatLines

wheat lines dataset
bathy.colors

Generate a sequence of colors for plotting bathymetric data.
big.peaks.col

Peak search by first derivatives
bag

Creating a fixed effect matrix with significant GWAS markers
TP.prep

Selecting the best training population for genomic selection
transp

Creating color with transparency
mmer2

Mixed Model Equations in R 2
mmer

Mixed Model Equations in R
score.calc

Score calculation for markers
CPdata

Genotypic and Phenotypic data for a CP population (F1; cross between 2 highly heterozygote individuals; i.e. humans, fruit crops, bredding populations in recurrent selection).
map.plot2

Creating a genetic map
PEV

Selecting the best training population for genomic selection
Technow_data

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