Man pages for CausalR
Causal network analysis methods

AddIDsToVerticesadd IDs to vertices
AddWeightsToEdgesadd weights to edges
AnalyseExperimentalDataanalyse experimental data
AnalysePredictionsListanalyse predictions list
CalculateEnrichmentPValuecalculates an enrichment p-value
CalculateSignificancecalculate overall significance p-value
CalculateSignificanceUsingCubicAlgorithmcalculate significance using the cubic algorithm
CalculateSignificanceUsingCubicAlgorithm1bCalculate Significance Using Cubic Algorithm
CalculateSignificanceUsingQuarticAlgorithmcalculate significance using the quartic algorithm
CalculateTotalWeightForAllContingencyTablescalculate total weight for all contingency tables
CalculateWeightGivenValuesInThreeByThreeContingencyTablecalculate weight given values in three-by-three contingency...
CausalR-packageThe CausalR package
CheckPossibleValuesAreValidcheck possible values are valid
CheckRowAndColumnSumValuesAreValidcheck row and column sum values are valid
CompareHypothesiscompare hypothesis
ComputeFinalDistributioncompute final distribution
ComputePValueFromDistributionTablecompute a p-value from the distribution table
CreateCCGcreate a Computational Causal Graph (CCG)
CreateCGcreate a Computational Graph (CG)
CreateNetworkFromTablecreate network from table
DetermineInteractionTypeOfPathdetermine interaction type of path
FindApproximateValuesThatWillMaximiseDValuefind approximate values that will maximise D value
FindIdsOfConnectedNodesInSubgraphfind Ids of connected nodes in subgraph
FindMaximumDValuefind maximum D value
GetAllPossibleRoundingCombinationsget score for numbers of correct and incorrect predictions
GetApproximateMaximumDValueFromThreeByTwoContingencyTablereturns approximate maximum D value or weight for a 3x2...
GetApproximateMaximumDValueFromTwoByTwoContingencyTablecomputes an approximate maximum D value or weight
GetCombinationsOfCorrectandIncorrectPredictionsreturns table of correct and incorrect predictions
GetExplainedNodesOfCCGGet explained nodes of CCG
GetInteractionInformationreturns interaction information from input data
GetMatrixOfCausalRelationshipscompute causal relationships matrix
GetMaxDValueForAFamilyget maximun D value for a family
GetMaxDValueForAThreeByTwoFamilyget maximum D value for three-by-two a family
GetMaximumDValueFromTwoByTwoContingencyTableget maximum D value from two-by-two contingency table
GetNodeIDget CCG node ID
GetNodeNameget node name
GetNumberOfPositiveAndNegativeEntriescounts the number of positive and negative entries
GetPathsInSifFormatGet paths in Sif format
GetRegulatedNodesget regulated nodes
GetRowAndColumnSumValuesget row and column sum values
GetScoreForNumbersOfCorrectandIncorrectPredictionsreturns the score for a given number of correct and incorrect...
GetScoresForSingleNodeGet scores for single node
GetScoresWeightsMatrixget scores weight matrix
GetScoresWeightsMatrixByCubicAlgget scores weights matrix by the cubic algorithm
GetSetOfDifferentiallyExpressedGenesget set of differientially expressed genes
GetSetOfSignificantPredictionsget set of significant predictions
GetShortestPathsFromCCGget shortest paths from CCG
GetWeightForNumbersOfCorrectandIncorrectPredictionsget weight for numbers of correct and incorrect predictions
GetWeightsAboveHypothesisScoreAndTotalWeightsget weights above hypothesis score and total weights
GetWeightsAboveHypothesisScoreForAThreeByTwoTableupdates weights for contingency table and produce values for...
GetWeightsFromInteractionInformationget weights from interaction information
MakePredictionsmake predictions
MakePredictionsFromCCGmake predictions from CCG
MakePredictionsFromCGmake predictions from CG
OrderHypothesesorder hypotheses
PlotGraphWithNodeNamesplot graph with node names
PopulateTheThreeByThreeContingencyTablepopulate the three-by-three contingency table
PopulateTwoByTwoContingencyTablePopulate Two by Two Contingency Table
ProcessExperimentalDataprocess experimental data
RankTheHypothesesrank the hypotheses
ReadExperimentalDataread experimental data
ReadSifFileToTableread .sif to Table
RemoveIDsNotInExperimentalDataremove IDs not in experimental data
runRankHypothesisrun rank the hypothesis
runSCANRrun ScanR
ScoreHypothesisscore hypothesis
ValidateFormatOfDataTablevalidate format of the experimental data table
ValidateFormatOfTablevalidate format of table
WriteAllExplainedNodesToSifFileWrite all explained nodes to Sif file
WriteExplainedNodesToSifFileWrite explained nodes to Sif file
CausalR documentation built on Nov. 17, 2017, 9:19 a.m.