sommer4.4.3 package

Solving Mixed Model Equations in R

A.mat

Additive relationship matrix

add.diallel.vars

add.diallel.vars

adiag1

Binds arrays corner-to-corner

anova_mmes

anova form a GLMM fitted with mmes

AR1mat

Autocorrelation matrix of order 1.

ARMAmat

Autocorrelation Moving average.

atcg1234

Letter to number converter

atcg1234BackTransform

Letter to number converter

atm

atm covariance structure

bathy.colors

Generate a sequence of colors for plotting bathymetric data.

bbasis

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

build.HMM

Build a hybrid marker matrix using parental genotypes from inbred indi...

coef_mmes

coef form a GLMM fitted with mmes

corImputation

Imputing a matrix using correlations

covm

covariance between random effects

csm

customized covariance structure

CSmat

Compound symmetry matrix

D.mat

Dominance relationship matrix

DEPRECATED_GWAS

Genome wide association study analysis

DEPRECATED_MMER

m ixed m odel e quations for r records

DEPRECATED_summary_mmer

summary form a GLMM fitted with mmer

DEPRECATED_VS

variance structure specification

DEPRECATED_VSR

variance structure specification

dfToMatrix

data frame to matrix

dsm

diagonal covariance structure

E.mat

Epistatic relationship matrix

fitted_mmes

fitted form a LMM fitted with mmes

fixm

fixed indication matrix

H.mat

Combined relationship matrix H

impute

Imputing a numeric or character vector

ism

identity covariance structure

jet.colors

Generate a sequence of colors alog the jet colormap.

LD.decay

Calculation of linkage disequilibrium decay

leg

Legendre polynomial matrix

logspace

Decreasing logarithmic trend

manhattan

Creating a manhattan plot

map.plot

Creating a genetic map plot

mmes

m ixed m odel e quations s olver

neMarker

Effective population size based on marker matrix

overlay

Overlay Matrix

plot_mmes

plot form a LMM plot with mmes

plot.monitor

plot the change of VC across iterations

predict_mmes

Predict form of a LMM fitted with mmes

propMissing

Proportion of missing data

r2

Reliability

randef

extracting random effects

redmm

Reduced Model Matrix

residuals_mmes

Residuals form a GLMM fitted with mmes

rrm

reduced rank covariance structure

simGECorMat

Create a GE correlation matrix for simulation purposes.

sommer-package

So lving M ixed M odel E quations in R

spl2Dc

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

spl2Dmats

Get Tensor Product Spline Mixed Model Incidence Matrices

stackTrait

Stacking traits in a dataset

stan

Standardize a vector of values in range 0 to 1

summary_mmes

summary form a GLMM fitted with mmes

tps

Get Tensor Product Spline Mixed Model Incidence Matrices

tpsmmbwrapper

Get Tensor Product Spline Mixed Model Incidence Matrices

transp

Creating color with transparency

unsm

unstructured indication matrix

usm

unstructured covariance structure

vpredict

vpredict form of a LMM fitted with mmes

vsm

variance structure specification

wald

Wald Test for Model Coefficients

Structural multivariate-univariate linear mixed model solver for estimation of multiple random effects with unknown variance-covariance structures (e.g., heterogeneous and unstructured) and known covariance among levels of random effects (e.g., pedigree and genomic relationship matrices) (Covarrubias-Pazaran, 2016 <doi:10.1371/journal.pone.0156744>; Maier et al., 2015 <doi:10.1016/j.ajhg.2014.12.006>; Jensen et al., 1997). REML estimates can be obtained using the Direct-Inversion Newton-Raphson and Direct-Inversion Average Information algorithms for the problems r x r (r being the number of records) or using the Henderson-based average information algorithm for the problem c x c (c being the number of coefficients to estimate). Spatial models can also be fitted using the two-dimensional spline functionality available.

  • Maintainer: Giovanny Covarrubias-Pazaran
  • License: GPL (>= 2)
  • Last published: 2025-07-30