View source: R/mi_logreg_algorithm.R

mi_logreg_algorithm | R Documentation |

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
)
```

`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 |

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)

[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.

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

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