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sommer: Solving Mixed Model Equations in R

Structural multivariate-univariate linear mixed model solver for estimation of multiple random effects and unknown variance-covariance structures (i.e. heterogeneous and unstructured variance models) (Covarrubias-Pazaran, 2016; Maier et al., 2015). REML estimates can be obtained using the Direct-Inversion Newton-Raphson and Direct-Inversion Average Information algorithms. Designed for genomic prediction and genome wide association studies (GWAS), particularly focused in the p > n problem (more coefficients than observations) and dense known covariance structures for levels of random effects. Spatial models can also be fitted using i.e. the two-dimensional spline functionality available in sommer.

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

4,080

Version

4.2.0

License

GPL (>= 2)

Maintainer

Giovanny Covarrubias-Pazaran

Last Published

September 10th, 2022

Functions in sommer (4.2.0)

DT_fulldiallel

Full diallel data for corn hybrids
DT_gryphon

Gryphon data from the Journal of Animal Ecology
DT_cornhybrids

Corn crosses and markers
DT_technow

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

Additive relationship matrix
AI

Average Information Algorithm
DT_cpdata

Genotypic and Phenotypic data for a CP population
AR1

Autocorrelation matrix of order 1.
DT_example

Broad sense heritability calculation.
ARMA

Autocorrelation Moving average.
DT_h2

Broad sense heritability calculation.
DT_wheat

wheat lines dataset
DT_expdesigns

Data for different experimental designs
CS

Compound symmetry matrix
EM

Expectation Maximization Algorithm
coef.mmec

coef form a GLMM fitted with mmec
D.mat

Dominance relationship matrix
anova.mmer

anova form a GLMM fitted with mmer
atc

atc covariance structure
MEMMA

Multivariate Efficient Mixed Model Association Algorithm
DT_halfdiallel

half diallel data for corn hybrids
DT_rice

Rice lines dataset
DT_mohring

Full diallel data for corn hybrids
DT_polyploid

Genotypic and Phenotypic data for a potato polyploid population
adiag1

Binds arrays corner-to-corner
coef.mmer

coef form a GLMM fitted with mmer
DT_sleepstudy

Reaction times in a sleep deprivation study
bivariateRun

bivariateRun functionality
anova.mmec

anova form a GLMM fitted with mmec
bathy.colors

Generate a sequence of colors for plotting bathymetric data.
build.HMM

Build a hybrid marker matrix using parental genotypes from inbred individuals
bbasis

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

plot form a LMM plot with mmer
imputev

Imputing a numeric or character vector
GWAS

Genome wide association study analysis
isc

identity covariance structure
dfToMatrix

data frame to matrix
dsc

diagonal covariance structure
list2usmat

list or vector to unstructured matrix
jet.colors

Generate a sequence of colors alog the jet colormap.
predict.mmec

Predict form of a LMM fitted with mmec
DT_augment

DT_augment design example.
manhattan

Creating a manhattan plot
mmer

mixed model equations for r records
leg

Legendre polynomial matrix
overlay

Overlay Matrix
pmonitor

plot the change of VC across iterations
DT_btdata

Blue Tit Data for a Quantitative Genetic Experiment
H.mat

Combined relationship matrix H
atcg1234

Letter to number converter
transp

Creating color with transparency
plot.mmec

plot form a LMM plot with mmec
simGECorMat

Create a GE correlation matrix for simulation purposes.
unsm

unstructured indication matrix
sommer-package

Solving Mixed Model Equations in R
Figure: mai.png
atr

atr covariance structure
DT_ige

Data to fit indirect genetic effects.
DT_legendre

Simulated data for random regression
residuals.mmec

Residuals form a GLMM fitted with mmec
csc

customized covariance structure
summary.mmec

summary form a GLMM fitted with mmec
csr

customized covariance structure
wald.test

Wald Test for Model Coefficients
residuals.mmer

Residuals form a GLMM fitted with mmer
DT_yatesoats

Yield of oats in a split-block experiment
E.mat

Epistatic relationship matrix
fitted.mmec

fitted form a LMM fitted with mmec
fitted.mmer

fitted form a LMM fitted with mmer
map.plot

Creating a genetic map plot
LD.decay

Calculation of linkage disequilibrium decay
usc

unstructured covariance structure
mmec

mixed model equations for c coefficients
summary.mmer

summary form a GLMM fitted with mmer
spl2Dmats

Get Tensor Product Spline Mixed Model Incidence Matrices
spl2Dc

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

add.diallel.vars
vsc

variance structure specification
dsr

diagonal covariance structure
vsr

variance structure specification
usr

unstructured covariance structure
vpredict

vpredict form of a LMM fitted with mmer
fcm

fixed effect constraint indication matrix
vs

variance structure specification
fixm

fixed indication matrix
gvsr

general variance structure specification
predict.mmer

Predict form of a LMM fitted with mmer
randef

extracting random effects
spl2Da

Two-dimensional penalised tensor-product of marginal B-Spline basis.
spl2Db

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

Get Tensor Product Spline Mixed Model Incidence Matrices
transformConstraints

transformConstraints