targetD

The $p \times p$ diagonal target D is computed from the $n
\times p$ data matrix. It is defined as follows ($i,j =
1,...,p$): 
$$t_{ij}=\begin{cases}s_{ii} &amp; \text{ if } i=j \\ 0 &amp; \text{ otherwise }\end{cases}$$
where
$s_{ij}$ denotes the entry of the unbiased covariance matrix in row
$i$, column $j$.

multivariate

methods

Implements estimation methods for shrinkage covariance matrices
using user-specified covariance targets. The covariance target is a structured
matrix towards which the unbiased sample covariance is shrunk, optionally
incorporating prior knowledge. Shrinkage intensity is computed analytically.
The method is described and applied to microarray gene expression data in
Jelizarow et al. (2010) <doi:10.1093/bioinformatics/btq323>.

Vincent Guillemot

SHIP

Shrinkage Covariance Incorporating Prior Knowledge

Monika Jelizarow

targetD function

<dl><dt>x</dt>
<dd>A $n \times p$ data matrix.</dd>
<dt>genegroups</dt>
<dd>The genegroups are not used for this target.</dd></dl>

Arguments

Monika Jelizarow and Vincent Guillemot

Author

Computation of the diagonal target D ('diagonal, unequal variances'). — targetD

<dl>

<dt>x</dt>
<dd>A $n \times p$ data matrix.</dd>


<dt>genegroups</dt>
<dd>The genegroups are not used for this target.</dd>

</dl>

targetD: Computation of the diagonal target D ('diagonal, unequal variances').

Description

Usage

Value

Arguments

Author

References

See Also

Examples