導入要分析的數(shù)據(jù)，并用切片器選擇因變量和自變量，因變量在左側(cè)，并刪除無關(guān)數(shù)據(jù)，包括索引。

testdata <- read.csv(csvfile, header = TRUE, sep = ",") testdata=testdata[3:9]

輸入可供選擇的分類方法

#3 approaches to calculate disgress.discmethod <- c("equal","natural","quantile")

輸入可供選擇的分類基本數(shù)量

#classify 4~6 discitv <- c(4:6)

篩選連續(xù)的自變量

#continuous variablescontinuous_variable <- colnames(testdata)[-1]

運行g(shù)dm函數(shù)?

#gdm functiontestgdm <- gdm(WSI ~ .,continuous_variable = continuous_variable ,data = testdata,discmethod = discmethod, discitv = discitv)

分析結(jié)果

?完整代碼

Sys.setlocale(category = 'LC_CTYPE', locale = 'C')#This is for using in pycharm. library(GD)filepath <- "E:/BaiduNetdiskDownload/Driving/Yl_naturalBreaks/" setwd(filepath)#Change the workspace temp=list.files(path=filepath,pattern="*.csv")#list csvfilenames in workspace for( csvfile in temp){print(csvfile)testdata <- read.csv(csvfile, header = TRUE, sep = ",")#load datatestdata <- read.csv("2003.csv", header = TRUE, sep = ",")testdata=testdata[3:9]#5 approach to calculate disgress.discmethod <- c("equal","natural","quantile")#select the best one among automatically 3 approaches of discrizetion #classify 4~6 automaticallydiscitv <- c(4:6)#continuous independent variables' name listcontinuous_variable <- colnames(testdata)[-1] #[-1] is for unselect dependent variable #gdm functiontestgdm <- gdm(WSI ~ .,continuous_variable = continuous_variable ,data = testdata,discmethod = discmethod, discitv = discitv)#dependent variable is WSI}print(testgdm)}

?

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