sheep.df {dmm}

Demonstration sheep dataset

Description

A small dataset with an unbalanced design, three fixed effects one of which can be interpreted as a cohort, three traits, and a pedigree which permits most available variance components to be fitted. Deliberately set up to be useful for a variety of demonstrations.

Usage

data(sheep.df)

Format

A data frame with 42 observations on the following 9 variables.

Id

Identifier for individuals

SId

Identifier for sires of individuals

DId

Identifier for dams of individuals

Year

A numeric vector: year of birth of each individual

Tb

A factor with levels S (born as a single lamb) T (born as a twin lamb)

Sex

A factor with levels M (male) F (female)

Cww

A numeric vector. Clean wool weight in Kg observed for eac h individual

Diam

A numeric vector. Fibre diameter in microns observed for e ach individual

Bwt

A numeric vector. Body weight in Kg observed for each individual

Details

These data are intended for demonstration, and are extensively used in examples in the dmm package help files.

This dataframe does not meet the minimum requirements for function dmm(). The identifiers are alphanumeric, some base animals are missing, and the three traits need to be in a matrix for multivariate analysis. It requires preprocessing by function mdf().

Source

A small subset of real data from an Australian sheep flock. Not the whole flock, and not a random sample.

Examples

library(dmm)
data(sheep.df)
str(sheep.df)
#do some preprocessing
sheep.mdf <- mdf(sheep.df,pedcols=c(1:3),factorcols=c(4:6),ycols=c(7:9),
             sexcode=c("M","F"),relmat=c("E","A"))
# The above code renumbers the pedigree Id's, makes columns "Year","Tb","Sex"
#    into factors,
#    assembles columns "CWW",Diam","Bwt" into a matrix (called 'Ymat')
#    for multivariate processing,
#    and sets up the environmental, and additive genetic
#    relationship matrices.
str(sheep.mdf)
#cleanup
rm(sheep.df)
rm(sheep.mdf)