Description Usage Arguments Author(s) Examples

In both mRMRe.Filter and mRMRe.Network objects, a sparse mutual information matrix is computed for the mRMRe procedure and this lazy-evaluated matrix is returned. In the context of a a mRMRe.Data 'mim', the full pairwise mutual information matrix is computed and returned.

1 2 3 4 5 6 |

`object` |
a |

`prior_weight` |
a numeric value [0,1] of indicating the impact of priors (mRMRe.Data only). |

`continuous_estimator` |
an estimator of the mutual information between features: either "pearson", "spearman", "kendall", "frequency" (mRMRe.Data only). |

`outX` |
a boolean used in the concordance index estimator to keep or throw out ties (mRMRe.Data only). |

`bootstrap_count` |
an integer indicating the number of bootstrap resampling used in estimation (mRMRe.Data only). |

`method` |
either "mi" or "cor"; the latter will return the correlation coefficients (rho) while the former will return the mutual information (-0.5 * log(1 - (rho^2))). |

Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains

1 2 3 4 5 6 7 8 9 10 11 | ```
set.thread.count(2)
data(cgps)
feature_data <- mRMR.data(data = data.frame(cgps.ge))
# Calculate the pairwise mutual information matrix
mim(feature_data)
filter <- mRMR.classic("mRMRe.Filter", data = feature_data, target_indices = 3:5,
feature_count = 2)
# Obtain the sparse (lazy-evaluated) mutual information matrix.
mim(filter)
``` |

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