在回归模型上使用预测

Question

我试图弄清楚如何通过我的回归模型来测试它与我的数据集的其他部分，所以我可以开始我的混乱matrix但我不知道我做错了什么。

studentreport<-read.csv("C:\\Users\\Joseph\\Downloads\\studentreport dataset full imp.csv",header=T,sep=",")
studentreport<-data.frame(studentreport)

smp_size <- floor(0.75 * nrow(studentreport))

set.seed(123)
train_ind <- sample(seq_len(nrow(studentreport)), size = smp_size)

train <- studentreport[train_ind, ]
test <- studentreport[-train_ind, ]

fitreport<-glm(train)
Fitstart=glm(Enrolling~1,data=train)

Report<-step(Fitstart,direction="forward",scope=formula(fitreport))

predict(Report, newdata = test,type ="response")

当我这样做预测我得到这个错误：

“model.frame.default中的错误（条款，newdata，na.action = na.action，xlev = object $ xlevels）：因子状态有新级别AP”

dupt：Report studentreport

Answer 1

我重新定义了您发布的代码。由于我没有在您的数据中找到Enrolling列，为了模型检查，我在glm列上使用了GPATypeWeighted。未检测到预测错误。

library(leaps)
library(caret)
studentreport <- dget("https://drive.google.com/uc?authuser=0&id=1PHpkhPpEjIt-apCJpzvAKAlWZTPX7Evv&export=download")
studentreport <- data.frame(studentreport)
smp_size <- floor(0.75 * nrow(studentreport))

set.seed(123)
train_ind <- sample(seq_len(nrow(studentreport)), size = smp_size)

train <- studentreport[train_ind, ]
test <- studentreport[-train_ind, ]

fitreport <- glm(train)
Fitstart = glm(GPATypeWeighted ~ 1, data = train)

Report <- step(Fitstart, direction="forward", scope = formula(fitreport))

predict(Report, newdata = test, type ="response")

Output:

           3            4            5            7           13           14           16           23           27           36 
1.000000e+00 1.000000e+00 1.000000e+00 1.804986e-15 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 
          37           43           44           56           57           60           62           64           66           69 
1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 2.097525e-15 
          70           79           82           86           91           92           93           96           97          100 
1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 
         101          108          112          114          115          116          117          120          123          138 
1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 2.199615e-15 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 
         140          148          155          157          158          161          164          165          174          177 
1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 
         180          185          187          200          203          204          207          214          215          216 
1.000000e+00 1.000000e+00 1.000000e+00 1.756027e-15 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.686952e-15 1.000000e+00 
         222          239          248 
1.000000e+00 1.000000e+00 1.000000e+00