tronco.capri {TRONCO} | R Documentation

## tronco capri

### Description

Reconstruct a progression model using CAPRI algorithm. 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.capri(
      data,
      command = "hc",
      regularization = c("bic", "aic"),
      do.boot = TRUE,
      nboot = 100,
      pvalue = 0.05,
      min.boot = 3,
      min.stat = TRUE,
      boot.seed = NULL,
      silent = FALSE,
      epos = 0,
      eneg = 0,
      restart = 100
    )

### Arguments

`data` | A TRONCO compliant dataset.
`command` | Parameter to define to heuristic search to be performed. Hill Climbing and Tabu search are currently available.
`regularization` | Select the regularization for the likelihood estimation, e.g., BIC, AIC.
`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.
`restart` | An integer, the number of random restarts.

### Value

A TRONCO compliant object with reconstructed model

### Examples

    data(test_dataset)
    recon = tronco.capri(test_dataset, nboot = 1)

[Package TRONCO version 2.26.0 Index]