Description Usage Arguments Details Value Examples

View source: R/computeLoglikeFromPartition.R

This function computes the log-likelihood of a multivariate Gaussian model with a block-diagonal covariance matrix.

1 | ```
computeLoglikeFromPartition(labels, expdata)
``` |

`labels` |
vector of block labels for each variable |

`expdata` |
matrix of data |

This function computes the log-likelihood of a multivariate Gaussian model with a block-diagonal covariance matrix described in the labels vector.

`loglike` |
loglikehood of the model |

`df` |
degree of freedom of the model |

`labels` |
labels provided as input |

1 2 3 4 5 6 7 8 | ```
## load data to test
data(dataTest)
## threshold of absS matrix
myLABELS <- thresholdAbsSPath(dataTest)$partitionList
## compute loglikelihood
logLikePath <- lapply(myLABELS, function(x) computeLoglikeFromPartition(x,dataTest))
``` |

shock documentation built on May 29, 2017, 11:06 p.m.

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