# CalculateSignificanceUsingCubicAlgorithm: calculate significance using the cubic algorithm In CausalR: Causal network analysis methods

## Description

Calculates the p-value of a score given the hypothesis score and the distribution table (calculated using the cubic algorithm)

## Usage

 ```1 2``` ```CalculateSignificanceUsingCubicAlgorithm(hypothesisScore, predictionListStats, experimentalDataStats, epsilon) ```

## Arguments

 `hypothesisScore` the score whose p-value we want to find. `predictionListStats` numbers of predicted up-regulated, predicted down-regulated and ambiguous predictions. `experimentalDataStats` numbers of up-regulated, down-regulated and not significantly changed transcripts in the experimental data. `epsilon` an epsilon threshold that is used when calculating the p-value using the cubic algorithm. Defaults to 1e-5.

p-value

## References

L Chindelevitch et al. Assessing statistical significance in causal graphs. BMC Bioinformatics, 13(35), 2012.

## Examples

 ```1 2 3 4 5 6 7``` ```CalculateSignificance(5, c(7,4,19), c(6,6,18)) CalculateSignificance(5, c(7,4,19), c(6,6,18), useCubicAlgorithm=TRUE) CalculateSignificanceUsingQuarticAlgorithm(5, c(7,4,19), c(6,6,18)) CalculateSignificance(5, c(7,4,19), c(6,6,18), useCubicAlgorithm=FALSE) CalculateSignificance(5, c(7,4,19), c(6,6,18), 1e-5) CalculateSignificance(5, c(7,4,19), c(6,6,18), epsilon=1e-5, useCubicAlgorithm=TRUE) CalculateSignificanceUsingCubicAlgorithm(5, c(7,4,19), c(6,6,18), 1e-5) ```

CausalR documentation built on May 31, 2017, 11:34 a.m.