DT_gryphon {sommer}

Gryphon data from the Journal of Animal Ecology

Description

This is a dataset that was included in the Journal of animal ecology by Wilson et al. (2010; see references) to help users understand how to use mixed models with animal datasets with pedigree data.

The dataset contains 3 elements:

gryphon; variables indicating the animal, the mother of the animal, sex of the animal, and two quantitative traits named 'BWT' and 'TARSUS'.

pedi; dataset with 2 columns indicating the sire and the dam of the animals contained in the gryphon dataset.

A; additive relationship matrix formed using the 'getA()' function used over the pedi dataframe.

Usage

data("DT_gryphon")

Format

The format is: chr "DT_gryphon"

Source

This data comes from the Journal of Animal Ecology. Please, if using this data cite Wilson et al. publication. If using our mixed model solver please cite Covarrubias' publication.

References

Wilson AJ, et al. (2010) An ecologist's guide to the animal model. Journal of Animal Ecology 79(1): 13-26.

Covarrubias-Pazaran G (2016) Genome assisted prediction of quantitative traits using the R package sommer. PLoS ONE 11(6): doi:10.1371/journal.pone.0156744

See Also

The core functions of the package mmer and mmec

Examples

####=========================================####
#### For CRAN time limitations most lines in the 
#### examples are silenced with one '#' mark, 
#### remove them and run the examples using
#### command + shift + C |OR| control + shift + C
####=========================================####
# data(DT_gryphon)
# DT <- DT_gryphon
# A <- A_gryphon
# P <- P_gryphon
# #### look at the data
# head(DT)
# #### fit the model with no fixed effects (intercept only)
# mix1 <- mmer(BWT~1,
#              random=~vsr(ANIMAL,Gu=A),
#              rcov=~units,
#              data=DT)
# summary(mix1)$varcomp
# 
# ## mmec uses the inverse of the relationship matrix
# Ai <- as(solve(A + diag(1e-4,ncol(A),ncol(A))), Class="dgCMatrix")
# mix1b <- mmec(BWT~1,
#              random=~vsc(isc(ANIMAL),Gu=Ai),
#              rcov=~units, tolParConv = 1e-5,
#              data=DT)
# summary(mix1b)$varcomp
# 
# #### fit the multivariate model with no fixed effects (intercept only)
# mix2 <- mmer(cbind(BWT,TARSUS)~1,
#              random=~vsr(ANIMAL,Gu=A),
#              rcov=~vsr(units),
#              na.method.Y = "include2",
#              data=DT)
# summary(mix2)
# cov2cor(mix2$sigma$`u:ANIMAL`)
# cov2cor(mix2$sigma$`u:units`)