april30

library(mlbench)

## Warning: package 'mlbench' was built under R version 4.3.2

library(caret)

## Warning: package 'caret' was built under R version 4.3.2

## Loading required package: ggplot2

## Warning: package 'ggplot2' was built under R version 4.3.2

## Loading required package: lattice

## Warning: package 'lattice' was built under R version 4.3.2

data("PimaIndiansDiabetes")
View(PimaIndiansDiabetes)

#resamples()

trainControl<- trainControl(method="repeatedcv", number=10, repeats=3)
set.seed(7)

fit.cart <- train(diabetes~., data=PimaIndiansDiabetes, method= "rpart", trControl=trainControl)
set.seed(7)

fit.lda <- train(diabetes~., data=PimaIndiansDiabetes, method= "lda", trControl=trainControl)
set.seed(7)

fit.svm <- train(diabetes~., data=PimaIndiansDiabetes, method= "svmRadial", trControl=trainControl)
set.seed(7)

fit.knn <- train(diabetes~., data=PimaIndiansDiabetes, method= "knn", trControl=trainControl)
set.seed(7)

result<- resamples(list(CART=fit.cart, LDA=fit.lda, SVM=fit.svm,knn=fit.knn))
summary(result)

## 
## Call:
## summary.resamples(object = result)
## 
## Models: CART, LDA, SVM, knn 
## Number of resamples: 30 
## 
## Accuracy 
##           Min.   1st Qu.    Median      Mean   3rd Qu.      Max. NA's
## CART 0.6753247 0.7272727 0.7532468 0.7469697 0.7662338 0.7922078    0
## LDA  0.7142857 0.7508117 0.7662338 0.7791069 0.8000256 0.9078947    0
## SVM  0.7236842 0.7508117 0.7631579 0.7712919 0.7915243 0.8947368    0
## knn  0.6753247 0.7036056 0.7272727 0.7369503 0.7662338 0.8311688    0
## 
## Kappa 
##           Min.   1st Qu.    Median      Mean   3rd Qu.      Max. NA's
## CART 0.2762566 0.3620724 0.4241878 0.4151867 0.4861107 0.5250000    0
## LDA  0.3011551 0.4192537 0.4662541 0.4862025 0.5308596 0.7812500    0
## SVM  0.3391908 0.3997116 0.4460612 0.4621585 0.5234605 0.7475083    0
## knn  0.2553191 0.3406000 0.3841761 0.3984995 0.4539789 0.6195363    0

scales<-list(x=list(relation="free"), y = list(relation="free"))
bwplot(result, scales=scales)

densityplot(result, scales)

splom(result)

diffs<- diff(result)
summary(diffs)

## 
## Call:
## summary.diff.resamples(object = diffs)
## 
## p-value adjustment: bonferroni 
## Upper diagonal: estimates of the difference
## Lower diagonal: p-value for H0: difference = 0
## 
## Accuracy 
##      CART      LDA       SVM       knn      
## CART           -0.032137 -0.024322  0.010019
## LDA  0.0007117            0.007815  0.042157
## SVM  0.0069840 0.5494135            0.034342
## knn  1.0000000 4.008e-05 0.0001765          
## 
## Kappa 
##      CART      LDA       SVM       knn     
## CART           -0.07102  -0.04697   0.01669
## LDA  0.0004852            0.02404   0.08770
## SVM  0.0154847 0.2137640            0.06366
## knn  1.0000000 0.0002315 0.0024494