pedMeta

datasets

Carry out principal component analysis (PCA) of very large pedigrees such as found
in breeding populations! This package, 'randPedPCA', exploits sparse matrices and randomised
linear algebra to deliver a gazillion-times speed-up compared to naive singular value decoposition (SVD)
(and eigen decomposition).

Hannes Becher

randPedPCA

Fast PCA for Large Pedigrees

Hanbin Lee

Ros Craddock

Gregor Gorjanc

pedMeta function

## `pedMeta`
A 'data.frame' of 2650 individuals (rows) with 12 variables (cols):<dl>
 <dt>id</dt>
<dd>Integer individual ID</dd> <dt>population</dt>
<dd>Population code. A, B or AB</dd> <dt>generation</dt>
<dd>Generation of the individual</dd> <dt>mid</dt>
<dd>dam ID</dd> <dt>fid</dt>
<dd>sire ID</dd> <dt>gv1</dt>
<dd>genetic value</dd> <dt>pv1</dt>
<dd>phenotypic value</dd> <dt>gv2</dt>
<dd>genetic value</dd> <dt>pv2</dt>
<dd>phenotypic value</dd> <dt>gv</dt>
<dd>genetic value</dd> <dt>pv</dt>
<dd>phenotypic value</dd> <dt>generationPlotShift</dt>
<dd>for plotting</dd>
</dl>

Format

Metadata associated with the example pedigree — pedMeta

## `pedMeta`
A 'data.frame' of 2650 individuals (rows) with 12 variables (cols):<dl>
 <dt>id</dt>
<dd>Integer individual ID</dd>

 <dt>population</dt>
<dd>Population code. A, B or AB</dd>

 <dt>generation</dt>
<dd>Generation of the individual</dd>

 <dt>mid</dt>
<dd>dam ID</dd>

 <dt>fid</dt>
<dd>sire ID</dd>

 <dt>gv1</dt>
<dd>genetic value</dd>

 <dt>pv1</dt>
<dd>phenotypic value</dd>

 <dt>gv2</dt>
<dd>genetic value</dd>

 <dt>pv2</dt>
<dd>phenotypic value</dd>

 <dt>gv</dt>
<dd>genetic value</dd>

 <dt>pv</dt>
<dd>phenotypic value</dd>

 <dt>generationPlotShift</dt>
<dd>for plotting</dd>


</dl>

Metadata associated with the example pedigree

pedMeta: Metadata associated with the example pedigree

Description

Usage

Arguments

Format