敘述性統計與相關度分析

程式碼：

rm(list=ls()) sink("RE-2.log") RE.Summary <- NULL RE.NumFields <- NULL LoadData <- function(csvFile='RE.csv') { RE.Data <- read.csv(csvFile) } SummaryData <- function(RE=RE.Data) { options(digits=1) fieldNames <- colnames(RE) i = 0 for (fieldName in fieldNames) { i = i + 1 if (class(RE[, fieldName]) == 'numeric') { assign('RE.NumFields', c(RE.NumFields, i), envir = .GlobalEnv) Summary.Field <- c(summary(RE[, fieldName]), sd(RE[, fieldName])) if (is.null(RE.Summary)) { assign('RE.Summary', Summary.Field, envir = .GlobalEnv) } else { assign('RE.Summary', rbind(RE.Summary, Summary.Field), envir = .GlobalEnv) } } } i = 1 for (fieldName in fieldNames) { if (class(RE[, fieldName]) == 'numeric') { rownames(RE.Summary)[i] <- fieldName i = i + 1 } } colnames(RE.Summary)[7] <- 'StdDev.' RE.Summary } CoVarianceData <- function(RE=RE.Data) { options(digits=2) if (is.null(RE.NumFields)) { fieldNames <- colnames(RE) i = 0 for (fieldName in fieldNames) { i = i + 1 if (class(RE[, fieldName]) == 'numeric') { assign('RE.NumFields', c(RE.NumFields, i), envir = .GlobalEnv) } } } cov(RE[, RE.NumFields]) } CoRelationData <- function(RE=RE.Data) { options(digits=2) if (is.null(RE.NumFields)) { fieldNames <- colnames(RE) i = 0 for (fieldName in fieldNames) { i = i + 1 if (class(RE[, fieldName]) == 'numeric') { assign('RE.NumFields', c(RE.NumFields, i), envir = .GlobalEnv) } } } cor(RE[, RE.NumFields]) } RE.Data <- LoadData() SummaryData(RE.Data) CoRelationData(RE.Data) CoVarianceData(RE.Data) sink()

輸出：

Min. 1st Qu. Median Mean 3rd Qu. Max. StdDev. FactorX1 4.3 5.1 6 6 6 8 0.8 FactorY1 2.0 2.8 3 3 3 4 0.4 FactorX2 1.0 1.6 4 4 5 7 1.8 FactorY2 0.1 0.3 1 1 2 2 0.8 FactorX1 FactorY1 FactorX2 FactorY2 FactorX1 1.00 -0.12 0.87 0.82 FactorY1 -0.12 1.00 -0.43 -0.37 FactorX2 0.87 -0.43 1.00 0.96 FactorY2 0.82 -0.37 0.96 1.00 FactorX1 FactorY1 FactorX2 FactorY2 FactorX1 0.686 -0.042 1.27 0.52 FactorY1 -0.042 0.190 -0.33 -0.12 FactorX2 1.274 -0.330 3.12 1.30 FactorY2 0.516 -0.122 1.30 0.58