Causal network analysis methods


[Up] [Top]

Documentation for package ‘CausalR’ version 1.12.0

Help Pages

CausalR-package The CausalR package
AddIDsToVertices add IDs to vertices
AddWeightsToEdges add weights to edges
AnalyseExperimentalData analyse experimental data
AnalysePredictionsList analyse predictions list
CalculateEnrichmentPValue calculates an enrichment p-value
CalculateSignificance calculate overall significance p-value
CalculateSignificanceUsingCubicAlgorithm calculate significance using the cubic algorithm
CalculateSignificanceUsingCubicAlgorithm1b Calculate Significance Using Cubic Algorithm
CalculateSignificanceUsingQuarticAlgorithm calculate significance using the quartic algorithm
CalculateTotalWeightForAllContingencyTables calculate total weight for all contingency tables
CalculateWeightGivenValuesInThreeByThreeContingencyTable calculate weight given values in three-by-three contingency table
CausalR The CausalR package
CheckPossibleValuesAreValid check possible values are valid
CheckRowAndColumnSumValuesAreValid check row and column sum values are valid
CompareHypothesis compare hypothesis
ComputeFinalDistribution compute final distribution
ComputePValueFromDistributionTable compute a p-value from the distribution table
CreateCCG create a Computational Causal Graph (CCG)
CreateCG create a Computational Graph (CG)
CreateNetworkFromTable create network from table
DetermineInteractionTypeOfPath determine interaction type of path
FindApproximateValuesThatWillMaximiseDValue find approximate values that will maximise D value
FindIdsOfConnectedNodesInSubgraph find Ids of connected nodes in subgraph
FindMaximumDValue find maximum D value
GetAllPossibleRoundingCombinations get score for numbers of correct and incorrect predictions
GetApproximateMaximumDValueFromThreeByTwoContingencyTable returns approximate maximum D value or weight for a 3x2 superfamily
GetApproximateMaximumDValueFromTwoByTwoContingencyTable computes an approximate maximum D value or weight
GetCombinationsOfCorrectandIncorrectPredictions returns table of correct and incorrect predictions
GetExplainedNodesOfCCG Get explained nodes of CCG
GetInteractionInformation returns interaction information from input data
GetMatrixOfCausalRelationships compute causal relationships matrix
GetMaxDValueForAFamily get maximun D value for a family
GetMaxDValueForAThreeByTwoFamily get maximum D value for three-by-two a family
GetMaximumDValueFromTwoByTwoContingencyTable get maximum D value from two-by-two contingency table
GetNodeID get CCG node ID
GetNodeName get node name
GetNumberOfPositiveAndNegativeEntries counts the number of positive and negative entries
GetPathsInSifFormat Get paths in Sif format
GetRegulatedNodes get regulated nodes
GetRowAndColumnSumValues get row and column sum values
GetScoreForNumbersOfCorrectandIncorrectPredictions returns the score for a given number of correct and incorrect predictions
GetScoresForSingleNode Get scores for single node
GetScoresWeightsMatrix get scores weight matrix
GetScoresWeightsMatrixByCubicAlg get scores weights matrix by the cubic algorithm
GetSetOfDifferentiallyExpressedGenes get set of differientially expressed genes
GetSetOfSignificantPredictions get set of significant predictions
GetShortestPathsFromCCG get shortest paths from CCG
GetWeightForNumbersOfCorrectandIncorrectPredictions get weight for numbers of correct and incorrect predictions
GetWeightsAboveHypothesisScoreAndTotalWeights get weights above hypothesis score and total weights
GetWeightsAboveHypothesisScoreForAThreeByTwoTable updates weights for contingency table and produce values for p-value calculation
GetWeightsFromInteractionInformation get weights from interaction information
MakePredictions make predictions
MakePredictionsFromCCG make predictions from CCG
MakePredictionsFromCG make predictions from CG
OrderHypotheses order hypotheses
PlotGraphWithNodeNames plot graph with node names
PopulateTheThreeByThreeContingencyTable populate the three-by-three contingency table
PopulateTwoByTwoContingencyTable Populate Two by Two Contingency Table
ProcessExperimentalData process experimental data
RankTheHypotheses rank the hypotheses
ReadExperimentalData read experimental data
ReadSifFileToTable read .sif to Table
RemoveIDsNotInExperimentalData remove IDs not in experimental data
runRankHypothesis run rank the hypothesis
runSCANR run ScanR
ScoreHypothesis score hypothesis
ValidateFormatOfDataTable validate format of the experimental data table
ValidateFormatOfTable validate format of table
WriteAllExplainedNodesToSifFile Write all explained nodes to Sif file
WriteExplainedNodesToSifFile Write explained nodes to Sif file