Data to fit indirect genetic effects.
This dataset contains phenotpic data for 98 individuals where they are measured with the purpose of identifying the effect of the neighbour in a focal individual.
data("DT_ige")The format is: chr "DT_ige"
This data was masked from a shared study.
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
The core functions of the package mmer
####=========================================#### #### For CRAN time limitations most lines in the #### examples are silenced with one '#' mark, #### remove them and run the examples ####=========================================#### ####=========================================#### #### EXAMPLES #### Different models with sommer ####=========================================#### data(DT_ige) DT <- DT_ige Af <- A_ige An <- A_ige ### Direct genetic effects model # modDGE <- mmer(trait ~ block, # random = ~ focal, # rcov = ~ units, # data = DT) # summary(modDGE)$varcomp # ### Indirect genetic effects model without covariance between DGE and IGE # modDGE <- mmer(trait ~ block, # random = ~focal + neighbour, # rcov = ~ units, # data = DT) # summary(modDGE)$varcomp # ### Indirect genetic effects model with covariance between DGE and IGE # modIGE <- mmer(trait ~ block, # random = ~ gvs(focal, neighbour), # rcov = ~ units, iters=4, # data = DT) # summary(modIGE)$varcomp # ### Indirect genetic effects model with covariance between DGE and IGE using relationship matrices # modIGEb <- mmer(trait ~ block, # random = ~ gvs(focal, neighbour, Gu=list(Af,An)), # rcov = ~ units, # data = DT) # summary(modIGEb)$varcomp
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