代码如下：
library(xlsx)
data <- read.xlsx2(file="C:/Program Files/R/R-3.5.1/2251.xlsx",sheetIndex=1)
library(randomForest)
ind <- sample(2, nrow(data), replace=TRUE, prob=c(0.7, 0.3))
traindata <- data[ind==1,]
testdata <- data[ind==2,]
traindataX1<-traindata[,1]
traindataX1<-as.factor(traindataX1)
traindataX2<-traindata[,2]
traindataX2<-as.factor(traindataX2)
traindataX3<-traindata[,3]
traindataX3<-as.factor(traindataX3)
traindataX4<-traindata[,4]
traindataX4<-as.factor(traindataX4)
traindataX5<-traindata[,5]
traindataX5<-as.factor(traindataX5)
traindataX6<-traindata[,6]
traindataX6<-as.factor(traindataX6)
traindataX7<-traindata[,7]
traindataX7<-as.factor(traindataX7)
traindataX8<-traindata[,8]
traindataX8<-as.factor(traindataX8)
traindataX9<-traindata[,9]
traindataX9<-as.factor(traindataX9)
traindataX10<-traindata[,10]
traindataX10<-as.factor(traindataX10)
traindataX11<-traindata[,11]
traindataX11<-as.factor(traindataX11)
traindataX12<-traindata[,12]
traindataX12<-as.factor(traindataX12)
traindataX13<-traindata[,13]
traindataX13<-as.factor(traindataX13)
traindataX14<-traindata[,14]
traindataX14<-as.factor(traindataX14)
traindataX15<-traindata[,15]
traindataX15<-as.factor(traindataX15)
traindataX16<-traindata[,16]
traindataX16<-as.factor(traindataX16)

testdataX2<-testdata[,2]
testdataX2<-as.factor(testdataX2)
testdataX3<-testdata[,3]
testdataX3<-as.factor(testdataX3)
testdataX4<-testdata[,4]
testdataX4<-as.factor(testdataX4)
testdataX5<-testdata[,5]
testdataX5<-as.factor(testdataX5)
testdataX6<-testdata[,6]
testdataX6<-as.factor(testdataX6)
testdataX7<-testdata[,7]
testdataX7<-as.factor(testdataX7)
testdataX8<-testdata[,8]
testdataX8<-as.factor(testdataX8)
testdataX9<-testdata[,9]
testdataX9<-as.factor(testdataX9)
testdataX10<-testdata[,10]
testdataX10<-as.factor(testdataX10)
testdataX11<-testdata[,11]
testdataX11<-as.factor(testdataX11)
testdataX12<-testdata[,12]
testdataX12<-as.factor(testdataX12)
testdataX13<-testdata[,13]
testdataX13<-as.factor(testdataX13)
testdataX14<-testdata[,14]
testdataX14<-as.factor(testdataX14)
testdataX15<-testdata[,15]
testdataX15<-as.factor(testdataX15)
testdataX16<-testdata[,16]
testdataX16<-as.factor(testdataX16)

library(randomForest)
randommodel<-randomForest(X1~.,data=traindata,ntree=300,mtry=12)
prediction<-predict(randommodel,newdata=testdata,type="class")
frep<-table(prediction,testdata$X1)
frep

拟合时，报错为：Error in randomForest.default(m, y, ...) : Can't have empty classes in y.
随机森林拟合时data=2251时就可以进行，data=traindata时就报错
请问大神是什么情况啊？