DT_gryphon: 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")

Arguments

Format

The format is: chr "DT_gryphon"

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

Examples

Run this code


####=========================================####
#### 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 <- mmes(BWT~1,
#              random=~vsm(ism(ANIMAL),Gu=A),
#              rcov=~units,
#              data=DT)
# summary(mix1)$varcomp
# 
# ## mmes algorithm uses the inverse of the relationship matrix
# ## if you select henderson=TRUE
# Ai <- solve(A + diag(1e-4,ncol(A),ncol(A)))
# Ai <- as(as(as( Ai,  "dMatrix"), "generalMatrix"), "CsparseMatrix")
# attr(Ai, 'inverse')=TRUE
# ####====================####
# #### multivariate model ####
# ####     2 traits       ####
# ####====================####
# head(DT)
# 
# traits <- c("BWT","TARSUS")
# DT[,traits] <- apply(DT[,traits],2,scale)
# DTL <- reshape(DT[,c("ANIMAL","MOTHER","BYEAR","SEX", traits)],
#                idvar = c("ANIMAL","MOTHER","BYEAR","SEX"),
#                varying = traits,
#                v.names = "value", direction = "long",
#                timevar = "trait", times = traits )
# DTL <- DTL[with(DTL, order(trait,ANIMAL)), ]
# head(DTL)
# 
# # #### fit the multivariate model with no fixed effects (intercept only)
# mix2 <- mmes(value~trait, # henderson=TRUE,
#              random=~vsm(usm(trait),ism(ANIMAL),Gu=A),
#              rcov=~vsm(dsm(trait),ism(units)),
#              data=DTL)
# summary(mix2)$varcomp
# cov2cor(mix2$theta[[1]])