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#' @title make predictions
#' @description
#' Creates a matrix of predictions for a particular hypothesis.
#' The output is an array containing the relationship between each node and the hypothesis.
#' The hypothesis provided will be the vertex id of one of the nodes in the network
#' (as an integer node ID or name, including + or - for up/down regulation in the case of a CCG).
#' The signOfHypothesis variable should be a 1 or -1, indicating up/down regulation.
#' @export
#' @concept CausalR
#' @param hypothesisnode the node in the causal graph from which predictions should be made. Can be either a (numerical) node ID or a (string) node name.
#' @param signOfHypothesis whether the hypothesis node is up- or down-regulated. Should be +1 or -1.
#' @param network a (Computational) Causal Graph, as an igraph.
#' @param delta the distance to search within the causal graph.
#' @param nodesInExperimentalData optional. Nodes to include in the output. Should be a list of node IDs.
#' @return a matrix of predictions for the given particular hypothesis
#' @examples
#' network <- system.file(package='CausalR', 'extdata', 'testNetwork.sif')
#' ccg <- CreateCCG(network)
#' predictions <- MakePredictions('NodeA', +1, ccg, 2)
MakePredictions <- function(hypothesisnode, signOfHypothesis, network, delta, nodesInExperimentalData = NULL) {
# Create a matrix of predictions for a particular hypothesis. The output is an array containing the relationship between each node and the
# hypothesis. The hypothesis provided will be the vertex id of one of the nodes in the network (as an integer node ID or name, including + or - for
# up/down regulation in the case of a CCG). The signOfHypothesis variable should be a 1 or -1, indicating up/down regulation.
if (network$isCCG) {
predictions <- MakePredictionsFromCCG(hypothesisnode, signOfHypothesis, network, delta, nodesInExperimentalData)
} else {
predictions <- MakePredictionsFromCG(hypothesisnode, signOfHypothesis, network, delta, nodesInExperimentalData)
}
return(predictions)
}
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