Nothing
cov.sel.high.sim <- function(N, Setting, rep, Models){
x111213 <- mvrnorm(n=N, mu=c(0,0,0), Sigma=cbind(c(1,0,0.25),c(0,1,0.25),c(0.25,0.25,1)) )
x11 <- as.numeric(x111213[,1] > 0)
x12 <- x111213[,2]
x13 <- as.numeric(x111213[,3] > 0)
x1415 <- mvrnorm(n=N, mu=c(0,0), Sigma=cbind(c(1,0.5),c(0.5,1)) )
x14 <- as.numeric(x1415[,1] > 0)
x15 <- x1415[,2]
x1617 <- mvrnorm(n=N, mu=c(0,0), Sigma=cbind(c(1,0.5),c(0.5,1)) )
x16 <- as.numeric(x1617[,1] > 0)
x17 <- x1617[,2]
x1819 <- rmvbin(N, bincorr=cbind(c(1,0.7),c(0.7,1)), margprob=c(0.5,0.5))
x18 <- x1819[,1]
x19 <- x1819[,2]
x20 <- rbinom(N, 1, prob = 0.5)
x212223 <- mvrnorm(n=N, mu=c(0,0,0), Sigma=cbind(c(1,0,0.25),c(0,1,0.25),c(0.25,0.25,1)) )
x21 <- as.numeric(x212223[,1] > 0)
x22 <- x212223[,2]
x23 <- as.numeric(x212223[,3] > 0)
x2425 <- mvrnorm(n=N, mu=c(0,0), Sigma=cbind(c(1,0.5),c(0.5,1)) )
x24 <- as.numeric(x2425[,1] > 0)
x25 <- x2425[,2]
x2627 <- mvrnorm(n=N, mu=c(0,0), Sigma=cbind(c(1,0.5),c(0.5,1)) )
x26 <- as.numeric(x2627[,1] > 0)
x27 <- x2627[,2]
x2829 <- rmvbin(N, bincorr=cbind(c(1,0.7),c(0.7,1)), margprob=c(0.5,0.5))
x28 <- x2829[,1]
x29 <- x2829[,2]
x30 <- rbinom(N, 1, prob = 0.5)
x313233 <- mvrnorm(n=N, mu=c(0,0,0), Sigma=cbind(c(1,0,0.25),c(0,1,0.25),c(0.25,0.25,1)) )
x31 <- as.numeric(x313233[,1] > 0)
x32 <- x313233[,2]
x33 <- as.numeric(x313233[,3] > 0)
x3435 <- mvrnorm(n=N, mu=c(0,0), Sigma=cbind(c(1,0.5),c(0.5,1)) )
x34 <- as.numeric(x3435[,1] > 0)
x35 <- x3435[,2]
x3637 <- mvrnorm(n=N, mu=c(0,0), Sigma=cbind(c(1,0.5),c(0.5,1)) )
x36 <- as.numeric(x3637[,1] > 0)
x37 <- x3637[,2]
x3839 <- rmvbin(N, bincorr=cbind(c(1,0.7),c(0.7,1)), margprob=c(0.5,0.5))
x38 <- x3839[,1]
x39 <- x3839[,2]
x40 <- rbinom(N, 1, prob = 0.5)
x414243 <- mvrnorm(n=N, mu=c(0,0,0), Sigma=cbind(c(1,0,0.25),c(0,1,0.25),c(0.25,0.25,1)) )
x41 <- as.numeric(x414243[,1] > 0)
x42 <- x414243[,2]
x43 <- as.numeric(x414243[,3] > 0)
x4445 <- mvrnorm(n=N, mu=c(0,0), Sigma=cbind(c(1,0.5),c(0.5,1)) )
x44 <- as.numeric(x4445[,1] > 0)
x45 <- x4445[,2]
x4647 <- mvrnorm(n=N, mu=c(0,0), Sigma=cbind(c(1,0.5),c(0.5,1)) )
x46 <- as.numeric(x4647[,1] > 0)
x47 <- x4647[,2]
x4849 <- rmvbin(N, bincorr=cbind(c(1,0.7),c(0.7,1)), margprob=c(0.5,0.5))
x48 <- x4849[,1]
x49 <- x4849[,2]
x50 <- rbinom(N, 1, prob = 0.5)
x515253 <- mvrnorm(n=N, mu=c(0,0,0), Sigma=cbind(c(1,0,0.25),c(0,1,0.25),c(0.25,0.25,1)) )
x51 <- as.numeric(x515253[,1] > 0)
x52 <- x515253[,2]
x53 <- as.numeric(x515253[,3] > 0)
x5455 <- mvrnorm(n=N, mu=c(0,0), Sigma=cbind(c(1,0.5),c(0.5,1)) )
x54 <- as.numeric(x5455[,1] > 0)
x55 <- x5455[,2]
x5657 <- mvrnorm(n=N, mu=c(0,0), Sigma=cbind(c(1,0.5),c(0.5,1)) )
x56 <- as.numeric(x5657[,1] > 0)
x57 <- x5657[,2]
x5859 <- rmvbin(N, bincorr=cbind(c(1,0.7),c(0.7,1)), margprob=c(0.5,0.5))
x58 <- x5859[,1]
x59 <- x5859[,2]
x60 <- rbinom(N, 1, prob = 0.5)
x616263 <- mvrnorm(n=N, mu=c(0,0,0), Sigma=cbind(c(1,0,0.25),c(0,1,0.25),c(0.25,0.25,1)) )
x61 <- as.numeric(x616263[,1] > 0)
x62 <- x616263[,2]
x63 <- as.numeric(x616263[,3] > 0)
x6465 <- mvrnorm(n=N, mu=c(0,0), Sigma=cbind(c(1,0.5),c(0.5,1)) )
x64 <- as.numeric(x6465[,1] > 0)
x65 <- x6465[,2]
x6667 <- mvrnorm(n=N, mu=c(0,0), Sigma=cbind(c(1,0.5),c(0.5,1)) )
x66 <- as.numeric(x6667[,1] > 0)
x67 <- x6667[,2]
x6869 <- rmvbin(N, bincorr=cbind(c(1,0.7),c(0.7,1)), margprob=c(0.5,0.5))
x68 <- x6869[,1]
x69 <- x6869[,2]
x70 <- rbinom(N, 1, prob = 0.5)
x717273 <- mvrnorm(n=N, mu=c(0,0,0), Sigma=cbind(c(1,0,0.25),c(0,1,0.25),c(0.25,0.25,1)) )
x71 <- as.numeric(x717273[,1] > 0)
x72 <- x717273[,2]
x73 <- as.numeric(x717273[,3] > 0)
x7475 <- mvrnorm(n=N, mu=c(0,0), Sigma=cbind(c(1,0.5),c(0.5,1)) )
x74 <- as.numeric(x7475[,1] > 0)
x75 <- x7475[,2]
x7677 <- mvrnorm(n=N, mu=c(0,0), Sigma=cbind(c(1,0.5),c(0.5,1)) )
x76 <- as.numeric(x7677[,1] > 0)
x77 <- x7677[,2]
x7879 <- rmvbin(N, bincorr=cbind(c(1,0.7),c(0.7,1)), margprob=c(0.5,0.5))
x78 <- x7879[,1]
x79 <- x7879[,2]
x80 <- rbinom(N, 1, prob = 0.5)
x818283 <- mvrnorm(n=N, mu=c(0,0,0), Sigma=cbind(c(1,0,0.25),c(0,1,0.25),c(0.25,0.25,1)) )
x81 <- as.numeric(x818283[,1] > 0)
x82 <- x818283[,2]
x83 <- as.numeric(x818283[,3] > 0)
x8485 <- mvrnorm(n=N, mu=c(0,0), Sigma=cbind(c(1,0.5),c(0.5,1)) )
x84 <- as.numeric(x8485[,1] > 0)
x85 <- x8485[,2]
x8687 <- mvrnorm(n=N, mu=c(0,0), Sigma=cbind(c(1,0.5),c(0.5,1)) )
x86 <- as.numeric(x8687[,1] > 0)
x87 <- x8687[,2]
x8889 <- rmvbin(N, bincorr=cbind(c(1,0.7),c(0.7,1)), margprob=c(0.5,0.5))
x88 <- x8889[,1]
x89 <- x8889[,2]
x90 <- rbinom(N, 1, prob = 0.5)
x919293 <- mvrnorm(n=N, mu=c(0,0,0), Sigma=cbind(c(1,0,0.25),c(0,1,0.25),c(0.25,0.25,1)) )
x91 <- as.numeric(x919293[,1] > 0)
x92 <- x919293[,2]
x93 <- as.numeric(x919293[,3] > 0)
x9495 <- mvrnorm(n=N, mu=c(0,0), Sigma=cbind(c(1,0.5),c(0.5,1)) )
x94 <- as.numeric(x9495[,1] > 0)
x95 <- x9495[,2]
x9697 <- mvrnorm(n=N, mu=c(0,0), Sigma=cbind(c(1,0.5),c(0.5,1)) )
x96 <- as.numeric(x9697[,1] > 0)
x97 <- x9697[,2]
x9899 <- rmvbin(N, bincorr=cbind(c(1,0.7),c(0.7,1)), margprob=c(0.5,0.5))
x98 <- x9899[,1]
x99 <- x9899[,2]
x100 <- rbinom(N, 1, prob = 0.5)
##Unconfoundedness holds given X
if(Setting==1){
if(Models=="Linear"){
x_12 <- mvrnorm(n=N, mu=c(0,0), Sigma=cbind(c(1,0.5),c(0.5,1)) )
x1 <- as.numeric(x_12[,1] > 0)
x2 <- x_12[,2]
x3 <- rbinom(N, 1, prob = 0.5)
x4 <- rnorm(N, mean = 0, sd = 1)
x_56 <- mvrnorm(n=N, mu=c(0,0), Sigma=cbind(c(1,0.5),c(0.5,1)) )
x5 <- x_56[,2]
x6 <- as.numeric(x_56[,1] > 0)
x_78 <- rmvbin(N, bincorr=cbind(c(1,0.7),c(0.7,1)), margprob=c(0.5,0.5))
x7 <- x_78[,1]
x8 <- x_78[,2]
x9 <- rnorm(N, mean = 0, sd = 1)
x10 <- rbinom(N, 1, prob = 0.5)
e0<-rnorm(N)
e1<-rnorm(N)
p <- 1/(1+exp(3 -2*x1 -1*x2 -2*x3 -1*x4 -2*x7))
treat <- rbinom(N, 1, prob = p)
y0 <- 2 + 4*x1 + 2*x2 + 2*x5 + 4*x6 + 4*x8 +e0
y1 <- 4 + 4*x1 + 2*x2 + 2*x5 + 4*x6 + 4*x8 +e1
Y <-ifelse(treat == 1, y1, y0)
T<-treat
}
if(Models=="Nonlinear"){
x_12 <- mvrnorm(n=N, mu=c(0,0), Sigma=cbind(c(1,0.5),c(0.5,1)) )
x1 <- as.numeric(x_12[,1] > 0)
x2 <- x_12[,2]
x3 <- rbinom(N, 1, prob = 0.5)
x4 <- rnorm(N, mean = 0, sd = 1)
x_56 <- mvrnorm(n=N, mu=c(0,0), Sigma=cbind(c(1,0.5),c(0.5,1)) )
x5 <- x_56[,2]
x6 <- as.numeric(x_56[,1] > 0)
x_78 <- rmvbin(N, bincorr=cbind(c(1,0.7),c(0.7,1)), margprob=c(0.5,0.5))
x7 <- x_78[,1]
x8 <- x_78[,2]
x9 <- rnorm(N, mean = 0, sd = 1)
x10 <- rbinom(N, 1, prob = 0.5)
e0<-rnorm(N)
e1<-rnorm(N)
p <- 1/(1+exp(3 -2*x1 -1*x2 -2*x3 -1*x4 -2*x7))
treat <- rbinom(N, 1, prob = p)
#y0 <- 2 - 6*x6/log((x1 + 1.4)^2) + 7*x2 + 2*x5 + 4*x8 + e0
#y1 <- 4 - 9*x6/log((x1 + 1.4)^3) + 2*x2 + 2*x5 + 4*x8 + e1
y0 <- 2 - 6*x6/(0.5+(x2 + 1.4)^2) + 7*x1 + 2*x5^2 + 4*x8 + e0
y1 <- 6.4 - 9*x6/(0.5+(x2 + 1.4)^4) + 4*x1 + 2*x5^2 + 4*x8 + e1
Y <-ifelse(treat == 1, y1, y0)
T<-treat
}
if(Models=="Binary"){
x_12 <- mvrnorm(n=N, mu=c(0,0), Sigma=cbind(c(1,0.5),c(0.5,1)) )
x1 <- as.numeric(x_12[,1] > 0)
x2 <- x_12[,2]
x3 <- rbinom(N, 1, prob = 0.5)
x4 <- rnorm(N, mean = 0, sd = 1)
x_56 <- mvrnorm(n=N, mu=c(0,0), Sigma=cbind(c(1,0.5),c(0.5,1)) )
x5 <- x_56[,2]
x6 <- as.numeric(x_56[,1] > 0)
x_78 <- rmvbin(N, bincorr=cbind(c(1,0.7),c(0.7,1)), margprob=c(0.5,0.5))
x7 <- x_78[,1]
x8 <- x_78[,2]
x9 <- rnorm(N, mean = 0, sd = 1)
x10 <- rbinom(N, 1, prob = 0.5)
e0<-rnorm(N)
e1<-rnorm(N)
p <- 1/(1+exp(3 -2*x1 -1*x2 -2*x3 -1*x4 -2*x7))
treat <- rbinom(N, 1, prob = p)
f<-4*x1 + 2*x2 + 2*x5 + 4*x6 + 4*x8
py0 <- 1/(1+exp(-2 + f))
py1 <- 1/(1+exp(-4 + f))
y0<-rbinom(N, 1, py0)
y1<-rbinom(N, 1, py1)
Y <-ifelse(treat == 1, y1, y0)
T<-treat
}
}
#M-bias given X
if(Setting==2){
if(Models=="Linear"){
U1<-rnorm(N, mean = 0, sd = 1)
U2<-rnorm(N, mean = 0, sd = 1)
U3<-rnorm(N, mean = 0, sd = 1)
ex9<-rnorm(N,mean=0,sd=0.5)
ex4<-rnorm(N,mean=0,sd=0.5)
x_12 <- mvrnorm(n=N, mu=c(0,0), Sigma=cbind(c(1,0.5),c(0.5,1)) )
x1 <- as.numeric(x_12[,1] > 0)
x2 <- x_12[,2]
x3 <- rbinom(N, 1, prob = 0.5)
x4 <- 0.2+0.8*U3+ex4
x_56 <- mvrnorm(n=N, mu=c(0,0), Sigma=cbind(c(1,0.5),c(0.5,1)) )
x5 <- x_56[,2]
x6 <- as.numeric(x_56[,1] > 0)
x_78 <- rmvbin(N, bincorr=cbind(c(1,0.7),c(0.7,1)), margprob=c(0.5,0.5))
x7 <- x_78[,1]
x8 <- x_78[,2]
x9 <- 1+2*U1+3*U2+ex9
x10 <- rbinom(N, 1, prob = 0.5)
e0<-rnorm(N)
e1<-rnorm(N)
p <- 1/(1+exp(3 -2*x1 -1*x2 -2*x3 -1*x4 -2*x7-1*U1))
treat <- rbinom(N, 1, prob = p)
y0 <- 2 + 4*x1 + 2*x2 + 2*x5 + 4*x6 + 4*x8+7*U2+2*U3 +e0
y1 <- 4 + 4*x1 + 2*x2 + 2*x5 + 4*x6 + 4*x8 +7*U2+2*U3+e1
Y <-ifelse(treat == 1, y1, y0)
T<-treat
}
if(Models=="Nonlinear"){
U1<-rnorm(N, mean = 0, sd = 1)
U2<-rnorm(N, mean = 0, sd = 1)
U3<-rnorm(N, mean = 0, sd = 1)
ex9<-rnorm(N,mean=0,sd=0.5)
ex4<-rnorm(N,mean=0,sd=0.5)
x_12 <- mvrnorm(n=N, mu=c(0,0), Sigma=cbind(c(1,0.5),c(0.5,1)) )
x1 <- as.numeric(x_12[,1] > 0)
x2 <- x_12[,2]
x3 <- rbinom(N, 1, prob = 0.5)
x4 <- 0.2+0.8*U3+ex4
x_56 <- mvrnorm(n=N, mu=c(0,0), Sigma=cbind(c(1,0.5),c(0.5,1)) )
x5 <- x_56[,2]
x6 <- as.numeric(x_56[,1] > 0)
x_78 <- rmvbin(N, bincorr=cbind(c(1,0.7),c(0.7,1)), margprob=c(0.5,0.5))
x7 <- x_78[,1]
x8 <- x_78[,2]
x9 <- 1+2*U1+3*U2+ex9
x10 <- rbinom(N, 1, prob = 0.5)
e0<-rnorm(N)
e1<-rnorm(N)
p <- 1/(1+exp(3 -2*x1 -1*x2 -2*x3 -1*x4 -2*x7-1*U1))
treat <- rbinom(N, 1, prob = p)
#y0 <- 2 - 6*x6/log((x1 + 1.4)^2) + 7*x2 + 2*x5 + 4*x8 +7*U2+2*U3 + e0
#y1 <- 4 - 9*x6/log((x1 + 1.4)^3) + 2*x2 + 2*x5 + 4*x8 +7*U2+2*U3 + e1
y0 <- 2 - 6*x6/(0.5+(x2 + 1.4)^2) + 7*x1 + 2*x5^2 + 4*x8 +7*U2+2*U3 + e0
y1 <- 6.4 - 9*x6/(0.5+(x2 + 1.4)^4) + 4*x1 + 2*x5^2 + 4*x8 +7*U2+2*U3 + e1
Y <-ifelse(treat == 1, y1, y0)
T<-treat
}
if(Models=="Binary"){
U1<-rnorm(N, mean = 0, sd = 1)
U2<-rnorm(N, mean = 0, sd = 1)
U3<-rnorm(N, mean = 0, sd = 1)
ex9<-rnorm(N,mean=0,sd=0.5)
ex4<-rnorm(N,mean=0,sd=0.5)
x_12 <- mvrnorm(n=N, mu=c(0,0), Sigma=cbind(c(1,0.5),c(0.5,1)) )
x1 <- as.numeric(x_12[,1] > 0)
x2 <- x_12[,2]
x3 <- rbinom(N, 1, prob = 0.5)
x4 <- 0.2+0.8*U3+ex4
x_56 <- mvrnorm(n=N, mu=c(0,0), Sigma=cbind(c(1,0.5),c(0.5,1)) )
x5 <- x_56[,2]
x6 <- as.numeric(x_56[,1] > 0)
x_78 <- rmvbin(N, bincorr=cbind(c(1,0.7),c(0.7,1)), margprob=c(0.5,0.5))
x7 <- x_78[,1]
x8 <- x_78[,2]
x9 <- 1+2*U1+3*U2+ex9
x10 <- rbinom(N, 1, prob = 0.5)
e0<-rnorm(N)
e1<-rnorm(N)
p <- 1/(1+exp(3 -2*x1 -1*x2 -2*x3 -1*x4 -2*x7-1*U1))
treat <- rbinom(N, 1, prob = p)
f<-4*x1 + 2*x2 + 2*x5 + 4*x6 + 4*x8+7*U2+2*U3
py0 <- 1/(1+exp(-2 + f))
py1 <- 1/(1+exp(-4 + f))
y0<-rbinom(N, 1, py0)
y1<-rbinom(N, 1, py1)
Y <-ifelse(treat == 1, y1, y0)
T<-treat
}
}
a<-data.frame(x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11,x12,x13,x14,x15,x16,x17,x18,x19,x20,x21,x22,x23,x24,x25,x26,x27,x28,x29,x30,
x31,x32,x33,x34,x35,x36,x37,x38,x39,x40,x41,x42,x43,x44,x45,x46,x47,x48,x49,x50,x51,x52,x53,x54,x55,x56,x57,x58,x59,x60,
x61,x62,x63,x64,x65,x66,x67,x68,x69,x70,x71,x72,x73,x74,x75,x76,x77,x78,x79,x80,x81,x82,x83,x84,x85,x86,x87,x88,x89,x90,
x91,x92,x93,x94,x95,x96,x97,x98,x99,x100,Y ,T)
binind<-c(1,3,6,7,8,10,11,13,14,16,18,19,20,21,23,24,26,28,29,30,31,33,34,36,38,39,40,41,43,44,46,48,49,50,
51,53,54,56,58,59,60,61,63,64,66,68,69,70,71,73,74,76,78,79,80,81,83,84,86,88,89,
90,91,93,94,96,98,99,100)
a[,binind]<-lapply(a[,binind],as.factor)
a[,101]<-as.numeric(a[,101])
a[,102]<-as.numeric(a[,102])
da <- a
ut <- list(dat=da)
ut
}
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