Harvey dataset

Description

Real data for average daily gain (Adg) of each of 65 Hereford steers, with age (Age) and initial weight (Weight) as covariates. First used by Walter Harvey in the publication listed below, on page 101 and following pages.

Usage

1

Format

A data frame with 139 observations on the following 9 variables.

Id

Identifier for individuals

SId

Identifier for sires of individuals

DId

Identifier for dams of individuals

Line

A numeric vector: breeding line for each individual

Agedam

A numeric vector: age of dam for each individual

Age

A numeric vector: age at weaning for each individual

Weight

A numeric vector: initial weight at beginning of test feeding in a feedlot

Adg

A numeric vector: average daily gain in weight in the feedlot

Sex

A numeric vector: code for Sex of each individual

Details

It has been assumed that all individuals have a unique dam, that is there are no twins or repeat matings. This is not clear in the original presentation. The nonzero relationships in this pedigree are therefore entirely due to individuals having a common sire.

This dataframe is close to meeting the requirements for function dmm(). The pedigree Id's are OK, the base animals are present, and there is only one trait to be analysed, so we do not need a traits matrix. However the Line and Agedam need to ba made into factors. We can either fix this by hand, or use function mdf().

Source

Harvey W.R.(1960) "Least Squares Analysis of Data with Unequal Subclass Numbers" United States Department of Agriculture Publication ARS-20-8, July 1960.

Examples

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library(dmm)
data(harv101.df)
str(harv101.df)
# preprocess, keeping Weight and Adg for use as covariates
# we need the keep=T agrument to preserve the covariates
harv.mdf <- mdf(harv101.df, pedcols=c(1:3), factorcols=c(4,5,9), ycols=3, 
            keep=TRUE, sexcode=c(1,2))
str(harv.mdf)
#cleanup
rm(harv101.df)
rm(harv.mdf)
#
# There is a full analysis of this dataset in 'dmmOverview.pdf'.
#

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