準(zhǔn)備的數(shù)據(jù)
affairs：numeric. How often engaged in extramarital sexual intercourse during the past year? 0 = none, 1 = once, 2 = twice, 3 = 3 times, 7 = 4–10 times, 12 = monthly, 12 = weekly, 12 = daily.
gender：factor indicating gender.
age：numeric variable coding age in years: 17.5 = under 20, 22 = 20–24, 27 = 25–29, 32 = 30–34, 37 = 35–39, 42 = 40–44, 47 = 45–49, 52 = 50–54, 57 = 55 or over
yearsmarried：numeric variable coding number of years married: 0.125 = 3 months or less, 0.417 = 4–6 months, 0.75 = 6 months–1 year, 1.5 = 1–2 years, 4 = 3–5 years, 7 = 6–8 years, 10 = 9–11 years, 15 = 12 or more years.
children ：factor. Are there children in the marriage?
religiousness：numeric variable coding religiousness: 1 = anti, 2 = not at all, 3 = slightly, 4 = somewhat, 5 = very.
education：numeric variable coding level of education: 9 = grade school, 12 = high school graduate, 14 = some college, 16 = college graduate, 17 = some graduate work, 18 = master's degree, 20 = Ph.D., M.D., or other advanced degree.
occupation：numeric variable coding occupation according to Hollingshead classification (reverse numbering).
ratingnumeric?：variable coding self rating of marriage: 1 = very unhappy, 2 = somewhat unhappy, 3 = average, 4 = happier than average, 5 = very happy岁疼。
統(tǒng)計(jì)因變量和自變量
統(tǒng)計(jì)p值
驗(yàn)證
數(shù)據(jù)可是是騙人的躯概，但是它不會(huì)說(shuō)謊驗(yàn)證如下：
table(data$affairs)/nrow(data)#全集上因變量的各個(gè)的比例
? ?0? ? ? ? 1?
0.750416 0.249584?
> #0? ? ? ? 1?
> #0.750416 0.249584?
> table(dataTrain$affairs)/nrow(dataTrain)#接近全集比例測(cè)試集上的
? 0? ? ? ? ?1?
0.7546778 0.2453222?
> table(dataTest$affairs)/nrow(dataTest)#訓(xùn)練集上的
? 0? ? ? ? ?1?
0.7333333 0.2666667?
具體如下：preProcValues <- preProcess(dataTrain,method = c('center','scale'))
trainTransformed <- predict(preProcValues,dataTrain)
testTransformed <- predict(preProcValues,dataTest)
#四投队、選擇變量
subsets <- c(2,5,8,15,20)
ctrl <- rfeControl(functions = rfFuncs,#隨機(jī)森林
? ? ? ? ? ? ? ? ? method = 'cv')#交叉驗(yàn)證
x <- trainTransformed[,-which(colnames(trainTransformed)%in%"affairs")]#不要affairs這一列
y <- trainTransformed[,"affairs"]
profile <- rfe(x,y,sizes = subsets,rfeControl = ctrl)
profile$optVariables
#五朵夏、模擬訓(xùn)練及調(diào)參
data.train <- trainTransformed[,c(profile$optVariables,'affairs')]
data.test <- testTransformed[,c(profile$optVariables,'affairs')]
##隨機(jī)森林##
set.seed(45645)
gbmFit1=train(affairs~.,data=data.train,method='rf')
#用于訓(xùn)練集
importance <- varImp(gbmFit1,scale = F)
plot(importance,xlab='重要性哈哈哈哈哈哈')
如圖所示：拿去用吧DS，ZN，事實(shí)證明嗜逻，流氓不可怕，可怕的是流氓有文化缭召，而且還不是一般的文化栈顷。哈哈哈哈~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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