# mim

From mRMRe v2.0.5
by Benjamin HaibeKains

##### Accessor function for the 'mim' information in a mRMRe.Data, mRMRe.Filter and mRMRe.Network object

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.

- Keywords
- methods

##### Usage

```
"mim"(object, prior_weight, continuous_estimator, outX, bootstrap_count)
"mim"(object, method)
"mim"(object)
```

##### Arguments

- object
- a
`mRMRe.Data, mRMRe.Filter or mRMRe.Network`

object. - 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))).

##### Examples

```
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)
```

*Documentation reproduced from package mRMRe, version 2.0.5, License: Artistic-2.0*

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