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b/man/ClassVectoringDT.Rd |
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% Generated by roxygen2: do not edit by hand |
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% Please edit documentation in R/DIscBIO-generic-ClassVectoringDT.R |
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\name{ClassVectoringDT} |
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\alias{ClassVectoringDT} |
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\alias{ClassVectoringDT,DISCBIO-method} |
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\title{Generating a class vector to be used for the decision tree analysis.} |
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\usage{ |
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ClassVectoringDT( |
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object, |
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Clustering = "K-means", |
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K, |
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First = "CL1", |
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Second = "CL2", |
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sigDEG, |
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quiet = FALSE |
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) |
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\S4method{ClassVectoringDT}{DISCBIO}( |
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object, |
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Clustering = "K-means", |
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K, |
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First = "CL1", |
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Second = "CL2", |
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sigDEG, |
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quiet = FALSE |
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) |
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} |
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\arguments{ |
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\item{object}{\code{DISCBIO} class object.} |
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\item{Clustering}{Clustering has to be one of the following: ["K-means", |
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"MB"]. Default is "K-means"} |
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\item{K}{A numeric value of the number of clusters.} |
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\item{First}{A string vector showing the first target cluster. Default is |
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"CL1"} |
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\item{Second}{A string vector showing the second target cluster. Default is |
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"CL2"} |
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\item{sigDEG}{A data frame of the differentially expressed genes (DEGs) |
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generated by running "DEGanalysis()" or "DEGanalysisM()".} |
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\item{quiet}{If `TRUE`, suppresses intermediary output} |
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} |
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\value{ |
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A data frame. |
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} |
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\description{ |
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This function generates a class vector for the input dataset so |
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the decision tree analysis can be implemented afterwards. |
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} |