95 lines
No EOL
3.4 KiB
R
95 lines
No EOL
3.4 KiB
R
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# Internal function that takes a formula and processes it for use in the Java
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# code. existingCovariateList is optional; if not provided then a new one is
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# created internally.
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processFormula <- function(formula, data, covariateList.java = NULL, na.penalty = NULL){
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# Having an R copy of the data loaded at the same time can be wasteful; we
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# also allow users to provide an environment of the data which gets removed
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# after being imported into Java
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if(class(data) == "environment"){
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if(is.null(data$data)){
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stop("When providing an environment with the dataset, the environment must contain an item called 'data'")
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}
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env <- data
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data <- env$data
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env$data <- NULL
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gc()
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}
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yVar <- formula[[2]]
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responses <- NULL
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variablesToDrop <- character(0)
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# yVar is a call object; as.character(yVar) will be the different components, including the parameters.
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# if the length of yVar is > 1 then it's a function call. If the length is 1, and it's not in data,
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# then we also need to explicitly evaluate it
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if(class(yVar) == "call" || !(as.character(yVar) %in% colnames(data))){
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# yVar is a function like CR_Response
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responses <- eval(expr=yVar, envir=data)
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if(class(formula[[3]]) == "name" && as.character(formula[[3]])=="."){
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# do any of the variables match data in data? We need to track that so we can drop them later
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variablesToDrop <- as.character(yVar)[as.character(yVar) %in% names(data)]
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}
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formula[[2]] <- NULL
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} else if(class(yVar) == "name"){ # and implicitly yVar is contained in data
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variablesToDrop <- as.character(yVar)
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}
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# Includes responses which we may need to later cut out if `.` was used on the
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# right-hand-side
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filteredData <- stats::model.frame(formula=formula, data=data, na.action=stats::na.pass)
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if(is.null(responses)){ # If this if-statement runs then we have a simple (i.e. numeric) response
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responses <- stats::model.response(filteredData)
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}
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# remove any response variables on the right-hand-side
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covariateData <- filteredData[, !(names(filteredData) %in% variablesToDrop), drop=FALSE]
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# Now that we know how many predictor variables we have, we should check na.penalty
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if(!is.null(na.penalty)){
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if(!is.numeric(na.penalty) & !is.logical(na.penalty)){
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stop("na.penalty must be either logical or numeric.")
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}
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if(is.logical(na.penalty) & length(na.penalty) != 1 & length(na.penalty) != ncol(covariateData)){
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stop("na.penalty must have length of either 1 or the number of predictor variables if logical.")
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}
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if(is.numeric(na.penalty) & length(na.penalty) != 1){
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stop("na.penalty must have length 1 if logical.")
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}
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if(anyNA(na.penalty)){
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stop("na.penalty cannot contain NAs.")
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}
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# All good; now to transform it.
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if(is.numeric(na.penalty)){
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na.threshold <- na.penalty
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na.penalty <- apply(covariateData, 2, function(x){mean(is.na(x))}) >= na.threshold
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}
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else if(is.logical(na.penalty) & length(na.penalty) == 1){
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na.penalty <- rep(na.penalty, times = ncol(covariateData))
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}
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# else{} - na.penalty is logical and the correct length; no need to do anything to it
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}
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dataset <- loadData(
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covariateData,
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colnames(covariateData),
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responses,
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covariateList.java = covariateList.java,
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na.penalty = na.penalty
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)
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return(dataset)
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} |