tronco.capri | R Documentation |
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.
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
)
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. |
A TRONCO compliant object with reconstructed model
data(test_dataset)
recon = tronco.capri(test_dataset, nboot = 1)
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