tronco.gabow {TRONCO} | R Documentation

## Tronco Gabow

### Description

Reconstruct a progression model using Gabow algorithm combined with probabilistic causation. For details and examples regarding the inference process and on the algorithm implemented in the package, we refer to the Vignette Section 6.

### Usage

    tronco.gabow(
      data,
      regularization = "no_reg",
      score = "pmi",
      do.boot = TRUE,
      nboot = 100,
      pvalue = 0.05,
      min.boot = 3,
      min.stat = TRUE,
      boot.seed = NULL,
      silent = FALSE,
      epos = 0,
      eneg = 0,
      do.raising = TRUE
    )

### Arguments

`data` | A TRONCO compliant dataset.
`regularization` | Select the regularization for the likelihood estimation, e.g., BIC, AIC.
`score` | Select the score for the estimation of the best tree, e.g., pointwise mutual information (pmi), conditional entropy (entropy).
`do.boot` | A parameter to disable/enable the estimation of the error rates give the reconstructed model.
`nboot` | Number of bootstrap sampling (with rejection) to be performed when estimating the selective advantage scores.
`pvalue` | Pvalue to accept/reject the valid selective advantage relations.
`min.boot` | Minimum number of bootstrap sampling to be performed.
`min.stat` | A parameter to disable/enable the minimum number of bootstrap sampling required besides nboot if any sampling is rejected.
`boot.seed` | Initial seed for the bootstrap random sampling.
`silent` | A parameter to disable/enable verbose messages.
`epos` | Error rate of false positive errors.
`eneg` | Error rate of false negative errors.
`do.raising` | Whether to use or not the raising condition as a prior.

### Value

A TRONCO compliant object with reconstructed model

### Examples

    data(test_dataset_no_hypos)
    recon = tronco.gabow(test_dataset_no_hypos, nboot = 1)

[Package TRONCO version 2.24.0 Index]