capacity_logreg_testing: Testing procedures for estimation of channel capacity

In sysbiosig/SLEMI: Statistical Learning Based Estimation of Mutual Information (SLEMI)

Description

Diagnostic procedures that allows to compute the uncertainity of estimation of channel capacity by SLEMI approach. Two main procedures are implemented: bootstrap, which execute estimation with using a fraction of data and overfitting test, which dividies data into two parts: training and testing. Each of them is repeated specified number of times to obtain a distribution of our estimators. It is recommended to conduct estimation by calling capacity_logreg_main.R.

Usage

capacity_logreg_testing(data, signal = "signal", response = "response",
  side_variables = NULL, cc_maxit = 100, lr_maxit = 1000,
  MaxNWts = 5000, formula_string = NULL, TestingSeed = 1234,
  testing_cores = 4, boot_num = 10, boot_prob = 0.8,
  sidevar_num = 10, traintest_num = 10, partition_trainfrac = 0.6)

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
cc_maxit	is the number of iteration of iterative optimisation of the algorithm to esimate channel capacity. Default is 100.
lr_maxit	is a maximum number of iteration of fitting algorithm of logistic regression. Default is 1000.
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.
TestingSeed	is the seed for random number generator used in testing procedures
testing_cores	- number of cores to be used in parallel computing (via doParallel package)
boot_num	is the number of bootstrap tests to be performed. Default is 10, but it is recommended to use at least 50 for reliable estimates.
boot_prob	is the proportion of initial size of data to be used in bootstrap. Default is 0.8.
sidevar_num	is the number of re-shuffling tests of side variables to be performed. Default is 10, but it is recommended to use at least 50 for reliable estimates.
traintest_num	is the number of overfitting tests to be performed. Default is 10, but it is recommended to use at least 50 for reliable estimates.
partition_trainfrac	is the fraction of data to be used as a training dataset. Default is 0.6.
maxNWts	is a maximum acceptable number of weights in logistic regression algorithm. Default is 5000.

Details

If side variables are added within the analysis (side_variables is not NULL), two additional procedures are carried out: reshuffling test and reshuffling with boostrap test, which are based on permutation of side variables values within the dataset. 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).

Value

a list with four elements:

• output$bootstrap - confusion matrix of logistic regression predictions

• output$resamplingMorph - channel capacity in bits

• output$traintest - optimal probability distribution

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

Each of above is a list, where an element is an output of a single repetition of the channel capacity algorithm

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.

sysbiosig/SLEMI documentation built on Oct. 6, 2019, 5:54 a.m.