capacity_logreg_algorithm: Main algorithm to calculate channel capacity by SLEMI...

In SLEMI: Statistical Learning Based Estimation of Mutual Information

Main algorithm to calculate channel capacity by SLEMI approach

Description

Additional parameters: lr_maxit and maxNWts are the same as in definition of multinom function from nnet package. An alternative model formula (using formula_string arguments) should be provided if data are not suitable for description by logistic regression (recommended only for advanced users). It is recommended to conduct estimation by calling capacity_logreg_main.R.

Usage

capacity_logreg_algorithm(
  data,
  signal = "signal",
  response = "response",
  side_variables = NULL,
  formula_string = NULL,
  model_out = TRUE,
  cc_maxit = 100,
  lr_maxit = 1000,
  MaxNWts = 5000
)

Arguments

data	must be a data.frame object. Cannot contain NA values.
signal	is a character object with names of columns of dataRaw to be treated as channel's input.
response	is a character vector with names of columns of dataRaw to be treated as channel's output
side_variables	(optional) is a character vector that indicates side variables' columns of data, if NULL no side variables are included
formula_string	(optional) is a character object that includes a formula syntax to use in logistic regression model. If NULL, a standard additive model of response variables is assumed. Only for advanced users.
model_out	is the logical indicating if the calculated logistic regression model should be included in output list
cc_maxit	is the number of iteration of iterative optimisation of the algorithm to estimate channel capacity. Default is 100.
lr_maxit	is a maximum number of iteration of fitting algorithm of logistic regression. Default is 1000.
MaxNWts	is a maximum acceptable number of weights in logistic regression algorithm. Default is 5000.

Value

a list with three elements:

• output$cc - channel capacity in bits

• output$p_opt - optimal probability distribution

• output$regression - confusion matrix of logistic regression predictions

• output$model - nnet object describing logistic regression model (if model_out=TRUE)

References

[1] Jetka T, Nienaltowski K, Winarski T, Blonski S, Komorowski M, Information-theoretic analysis of multivariate single-cell signaling responses using SLEMI, PLoS Comput Biol, 15(7): e1007132, 2019, https://doi.org/10.1371/journal.pcbi.1007132.

Examples

tempdata=data_example1 
outputCLR1=capacity_logreg_algorithm(data=tempdata, signal="signal",
response="response",cc_maxit=3,model_out=FALSE,
formula_string = "signal~response")

SLEMI documentation built on Nov. 20, 2023, 1:06 a.m.