func_iterative_logreg_update

internal

The implementation of the algorithm for estimation of mutual information and channel capacity from experimental data by classification procedures (logistic regression). Technically, it allows to estimate information-theoretic measures between finite-state input and multivariate, continuous output. Method described in Jetka et al. (2019) <doi:10.1371/journal.pcbi.1007132>.

Tomasz Jetka

SLEMI

Statistical Learning Based Estimation of Mutual Information

Karol Nienaltowski

Michal Komorowski

func_iterative_logreg_update function

<dl><dt>prob_lr</dt>
<dd>is a matrix of class probabilities for each observation</dd>
<dt>p0</dt>
<dd>is a numeric vector of prior probabilities used for logistic regression estimation</dd>
<dt>cell_id</dt>
<dd>a list of logical vectors indicating class labels of each observation</dd>
<dt>signal_levels</dt>
<dd>is a vector of class labels</dd>
<dt>cc_maxit</dt>
<dd>is the number of iteration of procedure to be carried out</dd></dl>

Arguments

Iterative updating of prior probabilities in logistic regression estimator — func_iterative_logreg_update

<dl>

<dt>prob_lr</dt>
<dd>is a matrix of class probabilities for each observation</dd>


<dt>p0</dt>
<dd>is a numeric vector of prior probabilities used for logistic regression estimation</dd>


<dt>cell_id</dt>
<dd>a list of logical vectors indicating class labels of each observation</dd>


<dt>signal_levels</dt>
<dd>is a vector of class labels</dd>


<dt>cc_maxit</dt>
<dd>is the number of iteration of procedure to be carried out</dd>

</dl>

func_iterative_logreg_update: Iterative updating of prior probabilities in logistic regression estimator

Description

Usage

Value

Arguments