# CalculateSignificanceUsingCubicAlgorithm1b: Calculate Significance Using Cubic Algorithm In CausalR: Causal network analysis methods

## Description

Calculate the p-value of a score given the hypothesis score and the distribution table (calculated using the cubic algorithm 1b in Assessing statistical significance in causal graphs - Chindelevitch et al)

## Usage

 ```1 2``` ```CalculateSignificanceUsingCubicAlgorithm1b(hypothesisScore, predictionListStats, experimentalDataStats, epsilon) ```

## Arguments

 `hypothesisScore` The score whose p-value we want to find. `predictionListStats` Number of predicted up-regulated, predicted down-regulated and ambiguous predictions. `experimentalDataStats` Number of up-regulated, down-regulated and not significantly changed transcripts in the experimental data. `epsilon` The threshold that is used when calculating the p-value using the cubic algorithm. (Defaults to 1e-5, only used for the cubic algorithm, ignored if useCubicAlgorithm is FALSE.)

p value

## 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) CalculateSignificanceUsingCubicAlgorithm1b(5, c(7,4,19), c(6,6,18), 1e-5) ```

CausalR documentation built on Nov. 8, 2020, 5:25 p.m.