Multivariate-Homework02/Homework02.R at master · kevinpiger/Multivariate-Homework02 · GitHub

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# Classification tree -----------------------------------------------------
fish<-read.csv("~/Desktop/碩一下/多變量/Fishdata.csv")
library(rpart)
fish.control<-rpart.control(minisplit=10,minbucket=3,xval=0)
fish.treeorig<-rpart(Species~Weight+L1+L2+L3+Height+Width,data=fish,method="class",control=fish.control)
# Let’s now plot the tree:
plot(fish.treeorig)
text(fish.treeorig)
# Also check out the complexity parameter (CP):
printcp(fish.treeorig)
summary(fish.treeorig)
fish.prunetree<-prune.rpart(fish.treeorig,cp=0.02)
plot(fish.prunetree)
text(fish.prunetree)
L21<-fish$L2-fish$L1
L32<-fish$L3-fish$L2
L31<-fish$L3-fish$L1
newfish<-cbind(fish,L21,L32,L31)
newfish.treenew<-rpart(Species~., data=newfish,method="class",parms=list(split="information"),control=fish.control)
printcp(newfish.treenew)
plot(newfish.treenew)
text(newfish.treenew)
fish.control<-rpart.control(minbucket=3,minsplit=10,xval=148)
newfish.treenewcv<- rpart(Species~., data=newfish,method="class",parms=list(split='information'),control=fish.control)
printcp(newfish.treenewcv)
newfish.test<-read.csv("~/Desktop/碩一下/多變量/Fish_test_data.csv")
L31<-newfish.test$L3- newfish.test$L1
L32<-newfish.test$L3- newfish.test$L2
L21<-newfish.test$L2- newfish.test$L1
newfish.test<-cbind(newfish.test,L21,L32,L31)
newfish.tpred<-predict(newfish.treenewcv,newfish.test)
newfish.tpred


# Linear Discriminant Analysis --------------------------------------------
library(MASS)
newfish.lda<-lda(Species~.,data=newfish)
# 有共線性存在
newfish.lda<-lda(Species~Weight+L1+Height+Width+L21+L32,data=newfish)
newfish.lda
newfish.ldapred<-predict(newfish.lda,newfish[,-1])
table(newfish$Species,newfish.ldapred$class)
newfish.ldacv<-lda(Species~Weight+L1+Height+Width+L21+L32,data=newfish,CV=T)
table(newfish$Species,newfish.ldacv$class)
eqscplot(newfish.ldapred$x,type='n',xlab="1st LD",ylab="2nd LD")
fish.species<-c(rep("B",33),rep("W",5),rep("R",18),rep("Pa",10),rep("S",12),rep("Pi",16),rep("Pe",54))
fish.colors<-c(rep(1,33),rep(2,5),rep(3,18),rep(4,10),rep(5,12),rep(6,16),rep(7,54))
text(newfish.ldapred$x[,1:2],fish.species,col=fish.colors)
newfish.ldatest<-predict(newfish.lda,newfish.test)
newfish.ldatest$class


# Quadratic Discriminant Analysis -----------------------------------------
# Let us examine how to apply QDA to this dataset.
# Let us pretend that the multivariate normal assumption is valid (you can check for this by using the R package “MVN”, as shown in the lab of CCA)

#Check Normailty
library(MVN)
fish1 <- newfish[,2:6]
mvn(fish1,mvnTest = "hz")
newfish.qda<-qda(Species~.,data=newfish)
newfish.q<-read.csv("~/Desktop/碩一下/多變量/QDA.csv")
newfish.qda<-qda(Species~.,data=newfish.q)
newfish.qda<-qda(Species~Weight+L1+Height+Width+L21+L32,data=newfish.q)
newfish.qdapred<-predict(newfish.qda,newfish.q)
table(newfish.q$Species,newfish.qdapred$class)
predict(newfish.qda,newfish.test)$class
newfish.qda<-qda(Species~Weight+L1+Height+Width+L21+L32,data=newfish.q,CV=T)
table(newfish.q$Species,newfish.qda$class)


# Nearest Neighbor Methdos ------------------------------------------------
# k = 3
library(class)
newfish.knn<-knn(newfish[,2:10],newfish[,2:10],newfish[,"Species"],k=3,prob=T)
table(newfish$Species,newfish.knn)

# k = 2
newfish.knn<-knn(newfish[,2:10],newfish[,2:10],newfish[,"Species"],k=2,prob=T)
table(newfish$Species,newfish.knn)

# k = 1
newfish.knn<-knn(newfish[,2:10],newfish[,2:10],newfish[,"Species"],k=1,prob=T)
table(newfish$Species,newfish.knn)
newfish1<-newfish[,c(1,2,3,6,8,9)]
newfish.knncv<-knn.cv(newfish1[,2:6],newfish1[,"Species"],k=1,prob=T)
table(newfish1$Species,newfish.knncv)
newfish1.test<-newfish.test[,c(1,2,5,7,8)]
newfish.knntest<-knn(newfish1[,2:6],newfish1.test,newfish1[,"Species"],k=1,prob=T)
newfish.knntest


#  Logistic Discrimination ------------------------------------------------
# Again, let us pretend that all assumptions (including “multivariate normal” and “common covariance matrix” over all classes) are valid (need to check that in practice).
# In order to use this method in R, we need another package.

library(nnet)
newfish.logd<-multinom(Species~.,data=newfish,maxit=250)
newfish.logd
table(newfish$Species,predict(newfish.logd,newfish))
# Now we examine the true error rate by cross-validation. To do this, we use another package called “glmnet”:
library(glmnet)
x <- as.matrix(newfish[,-1])
y <- newfish$Species
cvfit <- cv.glmnet(x, y, family='multinomial', type.measure='class', nfolds=148)
predict.value <- predict(cvfit, x, s = "lambda.min", type = "class")
table(predict.value,newfish$Species)
predict(newfish.logd,newfish.test)