mi_logreg_algorithm function

Main algorithm to calculate mutual information by SLEMI approach

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 mi_logreg_main.R.

mi_logreg_algorithm(
  data,
  signal = "signal",
  response = "response",
  side_variables = NULL,
  pinput = NULL,
  formula_string = NULL,
  lr_maxit = 1000,
  MaxNWts = 5000,
  model_out = TRUE
)

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
• pinput: is a numeric vector with prior probabilities of the input values. Uniform distribution is assumed as default (pinput=NULL).
• 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.
• 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.
• model_out: is the logical indicating if the calculated logistic regression model should be included in output list

Returns

a list with three elements:

• output$mi - mutual information in bits
• output$pinput - prior probabilities used in estimation
• output$regression - confusion matrix of logistic regression model
• output$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

## Estimate mutual information directly
temp_data=data_example1
output=mi_logreg_algorithm(data=data_example1,
                   signal = "signal",
                   response = "response")