# #——回声= FALSE,结果= "飞机 "-------------------------------------------------------------------------------------------- 选项(宽度= 130 ) ## ---- 回声= FALSE -------------------------------------------------------------------------------------------------------------- htmltools:: img src = knitr: image_uri(“functionRules.png”),风格= ' margin-left:汽车;margin-right:汽车 ') ## ---- eval = FALSE -------------------------------------------------------------------------------------------------------------- # setGeneric(“kNNinterface函数(measurementsTrain…)standardGeneric(“kNNinterface”))# # setMethod(“kNNinterface”、“DataFrame”,函数(measurementsTrain, classesTrain measurementsTest,…， verbose = 3) # {# splitDataset <- .splitDataAndOutcomes(measurementsTrain, classstrain) # trainingMatrix <- as.matrix(splitDataset[["measurements"]]) # test <- test[， isNumeric, drop = FALSE] # # if(!requireNamespace("class"， quiet = TRUE)) # stop("包'class'找不到。"请安装它。”)#如果(verbose = = 3) #消息(“拟合k最近的邻居数据分类和预测类。”)# # class::资讯(as.matrix (measurementsTrain) as.matrix (measurementsTest) measurementsTest , ...) # }) ## ---- eval = FALSE -------------------------------------------------------------------------------------------------------------- # setMethod(“kNNinterface”、“矩阵”,#函数(measurementsTrain、classesTrain measurementsTest,) # {# kNNinterface(DataFrame(measurementsTrain, check.names = FALSE)， # classesTrain， # DataFrame(measurementsTest, check.names = FALSE)，…)#})# # setMethod("kNNinterface"， "MultiAssayExperiment"， # function(measurementsTrain, measurementsTest, targets = names(measurementsTrain)， classesTrain，…)# {# tablesAndClasses <- .MAEtoWideTable(measurementsTrain, targets，classesTrain) # trainingTable < - tablesAndClasses[[“数据表”]]#类< - tablesAndClasses[["结果"]]# testingTable < - .MAEtoWideTable (measurementsTest,目标)# # .checkVariablesAndSame (trainingTable testingTable) # kNNinterface (trainingTable、类testingTable , ...) # }) ## ---- 消息= FALSE ----------------------------------------------------------------------------------------------------------- 类< -因子(代表(c(“健康”、“疾病”),每个= 5),水平= c(“健康”,"疾病"))测量值<-矩阵(c(rnorm(50,10)， rnorm(50,5))， ncol = 10) colnames(测量值)<- paste("Sample"， 1:10) rownames(测量值)<- paste("mRNA"， 1:10) library(ClassifyR) knnParams <- ModellingParams(selectParams = NULL, trainParams = trainParams (kNNinterface)， predictParams = NULL) CVparams <- CrossValParams(" leave -k- out "， leave = 1) classified <- runTests(测量值，类，CVparams, knnParams) classified cbind(预测(分类)，已知=实际结果(分类)