kNNvs {kNNvs}

k Nearest Neighbors with Grid Search Variable Selection

Description

k Nearest Neighbors with Grid Search Variable Selection

Usage

kNNvs(
  train_x,
  test_x,
  cl_train,
  cl_test,
  k,
  model = c("regression", "classifiation")
)

Arguments

train_x

matrix or data frame of training set

test_x

matrix or data frame of test set

cl_train

factor of true classifications of training set

cl_test

factor of true classifications of test set

k

the number of neighbors

model

regression or classifiation

Details

kNNvs is simply use add one and then compare acc to pick the best variable set for the knn model

Value

ACC or MSE, best variable combination, estimate value yhat

Examples

{
   data(iris3)
   train_x <- rbind(iris3[1:25,,1], iris3[1:25,,2], iris3[1:25,,3])
   test_x <- rbind(iris3[26:50,,1], iris3[26:50,,2], iris3[26:50,,3])
   cl_train<- cl_test<- factor(c(rep("s",25), rep("c",25), rep("v",25)))
   k<- 5
   # cl_test is not null
   mymodel<-kNNvs(train_x,test_x,cl_train,cl_test,k,model="classifiation")
   mymodel
   # cl_test is null
   mymodel<-kNNvs(train_x,test_x,cl_train,cl_test=NULL,k,model="classifiation")
   mymodel
   }