beta <- data.frame(beta1=0,beta2=0,beta3=0,beta4=0,beta5=0,beta6=0,beta7=0,beta8=0)
gdata <- function(beta){
a <- matrix(,nrow=200,ncol=200)
for(i in 1:200){
p=0.6
for(j in 1:200){
a[i,j] <- p^(abs(i-j))
}
}
mean <- rep(0,200)
library(MASS)
mydata <- mvrnorm(10000,mean,a)
mydata<-as.data.frame(mydata)
names(mydata) <- paste("X",1:200)
}
gdata <- function(beta){
a <- matrix(,nrow=200,ncol=200)
for(i in 1:200){
p=0.6
for(j in 1:200){
a[i,j] <- p^(abs(i-j))
}
}
mean <- rep(0,200)
library(MASS)
mydata <- mvrnorm(10000,mean,a)
mydata <- as.data.frame(mydata)
names(mydata) <- paste("X",1:200)
X201 <- rbinom(10000,1,0.3)
x202 <- rbinom(10000,1,0.7)
X202 <- rbinom(10000,1,0.7)
X203 <- rbinom(10000,1,0.5)
X204 <- rpois(10000,2)
X205 <- rpois(10000,0.9)
X206 <- runif(10000)
X207 <- runif(10000,min=1,max=2)
X208 <- rexp(10000,rate=3)
X209 <- rchisq(10000,4)
X210 <- rexp(10000,rate=5)
e <- rnorm(10000)
Z1 <- mydata[,1:10]
Z2 <- mydata[,11:20]
Z3 <- mydata[,21:30]
Z4 <- mydata[,31:40]
Z5 <- mydata[,41:50]
Z6 <- mydata[,51:60]
Z7 <- mydata[,61:70]
Z8 <- mydata[,71:80]
attach(beta)
Y<-6.1+beta1*Z1+beta2*Z2+beta[3]*Z3+beta4*Z4+beta5*Z5+beta6*Z6+beta7*Z7+beta8*Z8+0.51*X201+0.52*X203+0.53*X205+0.54*X207+0.55*X209+e
return(Y)
}
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