I can't make predictions commands work because the sample always change. It also says non comparable errays/arguments when I try to multiplicate matrixs

General
1

I paste here my code. The problems begin in ##prediction section, hope someone can help. ##PREDICTION SECTION starts near the end

library(tseries)
library(moments)
library(car)
library(lmtest)
library(leaps)
library(MASS)
summary(CO2,cor=F)
attach(CO2)
names(CO2)
CO2REGRESS<- lm(AnCarb ~ Traffic + Wind + Wind^2 + sin((2 * pi)/24 * Hour) +cos((2 * pi)/24 * Hour) + sin((4 * pi)/24 * Hour) + cos((4 * pi)/24 * Hour))
attributes(CO2REGRESS)
coefficients(CO2REGRESS)
CO2REGRESS$coefficients
summary(CO2REGRESS)
plot (AnCarb ~ Traffic)
abline (CO2REGRESS$coeff[1],CO2REGRESS$coeff[2])
boxplot(CO2REGRESS$residuals)
CO2stdResiduals1<-residuals(CO2REGRESS)/sqrt(var(residuals(CO2REGRESS)))
CO2studResiduals1<-stdres(CO2REGRESS)
CO2studEsResiduals1<-studres(CO2REGRESS)
hist(CO2studResiduals1)
plot(density(CO2studResiduals1))
plot(fitted(CO2REGRESS), CO2studResiduals1)
abline (0,0)
qqnorm(CO2stdResiduals1) 
qqline(CO2stdResiduals1)
sort(CO2studResiduals1)
library(leaps)
leaps(x = cbind(Traffic, Wind, Wind^2, sin((2 * pi)/24 * Hour), cos((2 * pi)/24 * Hour), sin((4 * pi)/24 * Hour), cos((4 * pi)/24 * Hour)), y = AnCarb, nbest=2,method="adjr2")
crPlots(CO2REGRESS,id=TRUE)
CO2inf<-influence.measures(CO2REGRESS)
summary(CO2inf)
influencePlot(CO2REGRESS)
X<-cbind(Traffic, Wind, Wind^2, sin((2 * pi)/24 * Hour), cos((2 * pi)/24 * Hour), sin((4 * pi)/24 * Hour), cos((4 * pi)/24 * Hour))
X<-data.frame(X[-c(34,43,100,70),])
CO2REGRESS<-lm(X[,1] ~ X[,2]+X[,3]+X[,4])
summary(CO2REGRESS)
boxplot(CO2REGRESS$residuals)
CO2stdResiduals1<-residuals(CO2REGRESS)/sqrt(var(residuals(CO2REGRESS)))
CO2studResiduals1<-studres(CO2REGRESS)# residui studentizzati
hist(CO2studResiduals1)
plot(density(CO2studResiduals1))
plot(fitted(CO2REGRESS), CO2studResiduals1)
abline (0,0)
qqnorm(CO2stdResiduals1) 
qqline(CO2stdResiduals1)
sort(CO2studResiduals1)
library(moments)
skewness (CO2stdResiduals1)
kurtosis (CO2stdResiduals1) 
library(tseries)
jarque.bera.test(CO2stdResiduals1)
CO2Identita<-matrix(diag(1,10),nrow=10)
eigen(CO2Identita)
CO2CorX<-cor(X[,c(2,3,4)])
CO2eigenCorX<-eigen(CO2CorX)
CO2CI<-sqrt(CO2eigenCorX$values[1]/CO2eigenCorX$values[3])
vif(CO2REGRESS) 
vif(CO2REGRESS)>10 
library(car)
ncvTest(CO2REGRESS)
X<-cbind(Traffic, Wind, Wind^2, sin((2 * pi)/24 * Hour), cos((2 * pi)/24 * Hour), sin((4 * pi)/24 * Hour), cos((4 * pi)/24 * Hour))
X<-X[1:24,]
CO2REGRESS<-lm(X[,1] ~ X[,2]+X[,3]+X[,4])
spreadLevelPlot(CO2REGRESS)
X<-cbind(AnCarb^0.3592022, Traffic, Wind, Wind^2, sin((2 * pi)/24 * Hour), cos((2 * pi)/24 * Hour), sin((4 * pi)/24 * Hour), cos((4 * pi)/24 * Hour))
regressione1<-lm(X[,1] ~ X[,2]+X[,3]+X[,4])
###PREDICTION
co2train<-c(sample(1:50,20),sample(51:80,21),sample(81:57,21))
#co2train<-c(sample(1:24))
yX<-cbind(AnCarb, Traffic, Wind, Wind^2, sin((2 * pi)/24 * Hour), cos((2 * pi)/24 * Hour), sin((4 * pi)/24 * Hour), cos((4 * pi)/24 * Hour))
yXTr<-yX[co2train,]
yXTe<-yX[-co2train,]
yTe<-matrix(yXTe[,1],nrow=33)
XTe<-matrix(yXTe[,c(2,3,4)],nrow=33)
U7<-matrix(rep(1,33),nrow=33)
UXTe<-matrix(cbind(U7,XTe),nrow=33)
regressioneTr<-lm(yXTr[,1] ~ yXTr[,2]+yXTr[,3]+yXTr[,4])
summary(regressioneTr)
conf<-predict(regressioneTr, level=0.95, interval="confidence") 
Bhat<-matrix(regressioneTr$coefficients,nrow=4)
dim(Bhat)
pred<-predict(regressioneTr, newdata =XTe, interval="prediction")
ypred<-UXTe%*%Bhat
fpred<-yTe-ypred
RSSp<-sum(fpred^2)
MAPD<-sum(abs(fpred)/yTe)/33
2

Please read the Welcome message.
Please read the documentation of the sample and set.seed functions.

3

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