Description Usage Arguments Value References Examples

View source: R/correlation_matrices.R

A simple wrapper around the irlba() function which computes a partial SVD
efficiently. This function's run time depends on the number of eigenvectors
requested but scales well. Use this function to generate covariates for use
with the `okriging`

or `krigr_cross_validation`

functions.

1 | ```
make_PCs_irlba(X, n.top = 2)
``` |

`X` |
A correlation matrix. |

`n.top` |
Number of top principal compenents to return |

A matrix of Principal Components of dimension (# of samples) x (n.top). As expected, eigenvectors are ordered by eigenvalue. Rownames are given as sample IDs.

library(irlba)

1 2 3 4 5 6 7 8 | ```
## compute PC's using the gene expression correlation matrix from vignette
## load gene expression values from vignette
expressionFile <- system.file(package = "OmicKriging",
"doc/vignette_data/ig_gene_subset.txt.gz")
## compute correlation matrix
geneCorrelationMatrix <- make_GXM(expressionFile)
## find top ten PC's of this matrix using SVD
topPcs <- make_PCs_irlba(geneCorrelationMatrix, n.top=10)
``` |

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