dlt.caret_m5.R

data<-dlt
count<-dim(dlt)[1]
#dlt.caret_m5 <- function(data,count) {
library(caret)
library(rpart)

trains_1 <-tail(data,count)[1:(count-3),]
trains_2 <-tail(data,count)[2:(count-2),]
trains_3 <-tail(data,count)[3:(count-1),]
results<-tail(data,(count-3))
tests_1<-tail(data,count)[1:(count-2),]
tests_2<-tail(data,count)[2:(count-1),]
tests_3<-tail(data,(count-2))

#A:
trn1<-trains_1$n
trn2<-trains_2$n
trn3<-trains_3$n
a1.1<-trains_1$a1
a2.1<-trains_1$a2
a3.1<-trains_1$a3
a4.1<-trains_1$a4
a5.1<-trains_1$a5
a1.2<-trains_2$a1
a2.2<-trains_2$a2
a3.2<-trains_2$a3
a4.2<-trains_2$a4
a5.2<-trains_2$a5
a1.3<-trains_3$a1
a2.3<-trains_3$a2
a3.3<-trains_3$a3
a4.3<-trains_3$a4
a5.3<-trains_3$a5
resa1<-results$a1
resa2<-results$a2
resa3<-results$a3
resa4<-results$a4
resa5<-results$a5
#B:
b1.1<-trains_1$b1
b2.1<-trains_1$b2
b1.2<-trains_2$b1
b2.2<-trains_2$b2
b1.3<-trains_3$b1
b2.3<-trains_3$b2
resb1<-results$b1
resb2<-results$b2

trains.a1<-data.frame(trn1,trn2,trn3,
                      a1.1,a2.1,a3.1,a4.1,a5.1,
                      a1.2,a2.2,a3.2,a4.2,a5.2,
                      a1.3,a2.3,a3.3,a4.3,a5.3,
                      b1.1,b2.1,
                      b1.2,b2.2,
                      b1.3,b2.3,
                      resa1)
trains.a2<-data.frame(trn1,trn2,trn3,
                      a1.1,a2.1,a3.1,a4.1,a5.1,
                      a1.2,a2.2,a3.2,a4.2,a5.2,
                      a1.3,a2.3,a3.3,a4.3,a5.3,
                      b1.1,b2.1,
                      b1.2,b2.2,
                      b1.3,b2.3,
                      resa2)
trains.a3<-data.frame(trn1,trn2,trn3,
                      a1.1,a2.1,a3.1,a4.1,a5.1,
                      a1.2,a2.2,a3.2,a4.2,a5.2,
                      a1.3,a2.3,a3.3,a4.3,a5.3,
                      b1.1,b2.1,
                      b1.2,b2.2,
                      b1.3,b2.3,
                      resa3)
trains.a4<-data.frame(trn1,trn2,trn3,
                      a1.1,a2.1,a3.1,a4.1,a5.1,
                      a1.2,a2.2,a3.2,a4.2,a5.2,
                      a1.3,a2.3,a3.3,a4.3,a5.3,
                      b1.1,b2.1,
                      b1.2,b2.2,
                      b1.3,b2.3,
                      resa4)
trains.a5<-data.frame(trn1,trn2,trn3,
                      a1.1,a2.1,a3.1,a4.1,a5.1,
                      a1.2,a2.2,a3.2,a4.2,a5.2,
                      a1.3,a2.3,a3.3,a4.3,a5.3,
                      b1.1,b2.1,
                      b1.2,b2.2,
                      b1.3,b2.3,
                      resa5)
trains.b1<-data.frame(trn1,trn2,trn3,
                      a1.1,a2.1,a3.1,a4.1,a5.1,
                      a1.2,a2.2,a3.2,a4.2,a5.2,
                      a1.3,a2.3,a3.3,a4.3,a5.3,
                      b1.1,b2.1,
                      b1.2,b2.2,
                      b1.3,b2.3,
                      resb1)
trains.b2<-data.frame(trn1,trn2,trn3,
                      a1.1,a2.1,a3.1,a4.1,a5.1,
                      a1.2,a2.2,a3.2,a4.2,a5.2,
                      a1.3,a2.3,a3.3,a4.3,a5.3,
                      b1.1,b2.1,
                      b1.2,b2.2,
                      b1.3,b2.3,
                      resb2)

set.seed(100)
n.t<-15
n.r<-5
n.n<-10

m5Model.a1<-train(resa1~a1.1+a2.1+a3.1+a4.1+a5.1+
                    a1.2+a2.2+a3.2+a4.2+a5.2+
                    a1.3+a2.3+a3.3+a4.3+a5.3+
                    b1.1+b2.1+b2.2+b2.2+b2.3+b2.3,
                  data = trains.a1,
                  method="m5",
                  trControl=trainControl(method = "repeatedcv",
                                         repeats = n.r,
                                         number = n.n))
plot(m5Model.a1,main="m5.a1")

#}