View source: R/other_corr_tests.R

sle.score | R Documentation |

Score impact of each sample on sparse leading eigen-value. Compute correlation using all samples (i.e. C), then compute correlation omitting sample i (i.e. Ci). The score the sample i is based on sparse leading eigen-value of the diffrence between C and Ci.

```
sle.score(
Y,
method = c("pearson", "kendall", "spearman"),
rho = 0.05,
sumabs = 1
)
```

`Y` |
data matrix with samples on rows and variables on columns |

`method` |
specify which correlation method: "pearson", "kendall" or "spearman" |

`rho` |
a positive constant such that cor(Y) + diag(rep(rho,p)) is positive definite. |

`sumabs` |
regularization paramter. Value of 1 gives no regularization, sumabs*sqrt(p) is the upperbound of the L_1 norm of v,controling the sparsity of solution. Must be between 1/sqrt(p) and 1. |

score for each sample measure impact on correlation structure

sle.test

```
# load iris data
data(iris)
# Evalaute score on each sample
sle.score( iris[,1:4] )
```

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