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O-learning-simulations.R
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O-learning-simulations.R
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source('O-learning-functions.r')
source('KAL-functions.r')
ind = 5
allloop = 0
datagen = Scenario1
epsilon = 0.1
########################################################################
######################O-learning for setting1-4 ###########################
########################################################################
##############################
#LOL SETTING 1-4
##############################
#Randomized trials
beta0 = c(0,0.5) #beta0 = c(0,1) for setting 2 4
numberofsets = 500
for (samplesize in c(400,800)){
results_lol_s1 <- list()
N = samplesize
lol_beta_hat = matrix(nrow = numberofsets,ncol = length(beta0))
cat("\n")
for (i in 1:numberofsets){
#generate data
cat(i," ")
set.seed(1000+i)
dataset = generate_sample_random1(beta = beta0, N = samplesize) #mu0 = mu2 for setting 3 4
Y = dataset$Y
X = as.matrix(dataset$X)
A = dataset$A
N = dataset$N
p = dataset$p
#O-learning
constant = min(quantile(Y, 0.6), 0)
trainweight = Y - constant
index = which(Y > quantile(Y, 0.6))
#estimate propensity score
### We use the penalized quantile regression to enhance the model fitting.
#rqmodel = rq(trainA[index] ~ trainX[index,],.5,method = "lasso",weights = trainweight[index],lambda = 1)
#predict dose
############### propensity score part
mydata = data.frame(T = A,X = X)
model.num = lm(T~1,data = mydata)
ps.num = dnorm((mydata$T - model.num$fitted) / (summary(model.num))$sigma,0,1)
model.den = gbm(T~.,data = mydata, shrinkage = 0.0005,
interaction.depth = 4, distribution = "gaussian",n.trees = 20000)
opt = optimize(F.aac.iter,interval = c(1,20000), data = mydata, ps.model =
model.den,
ps.num = ps.num,rep = 50,criterion = "pearson")
best.aac.iter = opt$minimum
best.aac = opt$objective
# Calculate the inverse probability weights
model.den$fitted = predict(model.den,newdata = mydata,
n.trees = floor(best.aac.iter), type = "response")
ps.den = dnorm((mydata$T - model.den$fitted) / sd(mydata$T-model.den
$fitted),0,1)
weight.gbm = ps.num / ps.den
### model with estimated propensity score times reward/outcome as weigths
rqmodel = rq(A[index] ~ X[index,],.5,method = "lasso", w = (trainweight*weight.gbm)[index],lambda = 1)
lol_beta_hat[i,] = rqmodel$coefficients
}
cat("\n Sample Size:",samplesize,"\n")
#calculate value
v = sim_value1(betahat = lol_beta_hat,
dose_function = dose_lol,
beta = beta0,
testN = 1000) #mu0 = mu2 for setting 3 4
}
#########
#Observational Studies
beta0 = c(0,1) #beta0 = c(0,1) for setting 2 4
numberofsets = 500
for (samplesize in c(400,800)){
results_lol_s1 <- list()
N = samplesize
lol_beta_hat = matrix(nrow = numberofsets,ncol = length(beta0))
#lol_value_hat = rep(NA,numberofsets)
#kol_value_hat = rep(NA,numberofsets)
cat("\n")
for (i in 1:numberofsets){
#generate data
cat(i," ")
set.seed(1000+i)
dataset = generate_sample_obs1(beta = beta0,
N = samplesize,
mu0 = mu2) #mu0 = mu2 for setting 3 4
Y = dataset$Y
X = as.matrix(dataset$X)
A = dataset$A
N = dataset$N
p = dataset$p
#O-learning
constant = min(quantile(Y,0.6),0)
trainweight = Y - constant
index = which(Y > quantile(Y,0.6))
#estimate propensity score
### We use the penalized quantile regression to enhance the model fitting.
#rqmodel = rq(trainA[index] ~ trainX[index,],.5,method = "lasso",weights = trainweight[index],lambda = 1)
#predict dose
############### propensity score part
mydata = data.frame(T = A,X = X)
model.num = lm(T~1,data = mydata)
ps.num = dnorm((mydata$T-model.num$fitted)/(summary(model.num))$sigma,0,1)
model.den = gbm(T~.,data = mydata, shrinkage = 0.0005,
interaction.depth = 4, distribution = "gaussian",n.trees = 20000)
opt = optimize(F.aac.iter,interval = c(1,20000), data = mydata, ps.model =
model.den,
ps.num = ps.num,rep = 50,criterion = "pearson")
best.aac.iter = opt$minimum
best.aac = opt$objective
# Calculate the inverse probability weights
model.den$fitted = predict(model.den,newdata = mydata,
n.trees = floor(best.aac.iter), type = "response")
ps.den = dnorm((mydata$T-model.den$fitted)/sd(mydata$T-model.den
$fitted),0,1)
weight.gbm = ps.num/ps.den
### model with estimated propensity score times reward/outcome as weigths
rqmodel = rq(A[index] ~ X[index,],.5,method = "lasso", w = (trainweight*weight.gbm)[index],lambda = 1)
lol_beta_hat[i,] = rqmodel$coefficients
}
cat("\n Sample Size:",samplesize,"\n")
v = sim_value1(betahat = lol_beta_hat,
dose_function = dose_lol,
beta = beta0,testN = 1000,
mu0 = mu2) #mu0 = mu2 for setting 3 4
}
##############################
#KOL SETTING 1-4
##############################
#Randomized trials
beta0 = c(0,1) #beta0 = c(0,1) for setting 2 4
numberofsets = 500
meanfunction = mu2
for (samplesize in c(800)){
results_lol_s1 <- list()
N = samplesize
#lol_beta_hat = matrix(nrow = numberofsets,ncol = length(beta0))
#lol_value_hat = rep(NA,numberofsets)
kol_value_hat = rep(NA,numberofsets)
cat("\n")
for (i in 1:numberofsets){
#generate data
cat(i," ")
set.seed(1000+i)
dataset = generate_sample_random1(beta = beta0,
N = samplesize,
mu0 = meanfunction) #mu0 = mu2 for setting 3 4
Y = dataset$Y
X = as.matrix(dataset$X)
A = dataset$A
N = dataset$N
p = dataset$p
#O-learning
constant = min(quantile(Y,0.6),0)
trainweight = Y - constant
index = which(Y > quantile(Y,0.6))
#estimate propensity score
### We use the penalized quantile regression to enhance the model fitting.
#rqmodel = rq(trainA[index] ~ trainX[index,],.5,method = "lasso",weights = trainweight[index],lambda = 1)
#predict dose
############### propensity score part
mydata = data.frame(T = A,X = X)
model.num = lm(T~1,data = mydata)
ps.num = dnorm((mydata$T-model.num$fitted)/(summary(model.num))$sigma,0,1)
model.den = gbm(T~.,data = mydata, shrinkage = 0.0005,
interaction.depth = 4, distribution = "gaussian",n.trees = 20000)
opt = optimize(F.aac.iter,interval = c(1,20000), data = mydata, ps.model =
model.den,
ps.num = ps.num,rep = 50,criterion = "pearson")
best.aac.iter = opt$minimum
best.aac = opt$objective
# Calculate the inverse probability weights
model.den$fitted = predict(model.den,newdata = mydata,
n.trees = floor(best.aac.iter), type = "response")
ps.den = dnorm((mydata$T-model.den$fitted)/sd(mydata$T-model.den
$fitted),0,1)
weight.gbm = ps.num/ps.den
### model with estimated propensity score times reward/outcome as weigths
#generate a new dataset for evaluating value function
set.seed(5000+i)
C_new = -10
N_new = 1000
beta_new = beta0
mbeta_new = meanfunction
p_new = length(beta_new)-1
X_new = matrix(rnorm(N_new*p,0,1),nrow = N_new)
dose_optim = g(cbind(1,X_new)%*% beta_new)
### model with estimated propensity score times reward/outcome as weigths
model_pen = svm(x = X[index,], y = A[index], w = (trainweight*weight.gbm)[index], type = "eps-regression",
epsilon = 0.15, scale = FALSE)
tmpvalue2 = predict(model_pen,X_new)
tmpvalue2 = pmin(pmax(tmpvalue2,0),1)
Yhat2 = mbeta_new(X_new)+C_new*(tmpvalue2-dose_optim)^2
kol_value_hat[i] = mean(Yhat2)
write.csv(kol_value_hat,paste("results/simulation/kol-sim04-value",samplesize,".csv"),row.names = FALSE)
}
cat("\n Sample Size:",samplesize,"\n")
cat("Mean of V:", mean(kol_value_hat),"\n")
cat("Sd of V:", sd(kol_value_hat),"\n")
}
########
#Observational Studies
beta0 = c(0,1) #beta0 = c(0,1) for setting 2 4
numberofsets = 500
meanfunction = mu2
for (samplesize in c(800)){
results_lol_s1 <- list()
N = samplesize
#lol_beta_hat = matrix(nrow = numberofsets,ncol = length(beta0))
#lol_value_hat = rep(NA,numberofsets)
kol_value_hat = rep(NA,numberofsets)
cat("\n")
for (i in 1:numberofsets){
#generate data
cat(i," ")
set.seed(1000+i)
dataset = generate_sample_obs1(beta = beta0,
N = samplesize,
mu0 = meanfunction) #mu0 = mu2 for setting 3 4
Y = dataset$Y
X = as.matrix(dataset$X)
A = dataset$A
N = dataset$N
p = dataset$p
#O-learning
constant = min(quantile(Y, 0.6), 0)
trainweight = Y - constant
index = which(Y > quantile(Y, 0.6))
#estimate propensity score
### We use the penalized quantile regression to enhance the model fitting.
#rqmodel = rq(trainA[index] ~ trainX[index,],.5,method = "lasso",weights = trainweight[index],lambda = 1)
#predict dose
############### propensity score part
mydata = data.frame(T = A,X = X)
model.num = lm(T~1,data = mydata)
ps.num = dnorm((mydata$T-model.num$fitted) / (summary(model.num))$sigma,0,1)
model.den = gbm(T~.,data = mydata, shrinkage = 0.0005,
interaction.depth = 4, distribution = "gaussian",n.trees = 20000)
opt = optimize(F.aac.iter,interval = c(1,20000), data = mydata, ps.model =
model.den,
ps.num = ps.num,rep = 50,criterion = "pearson")
best.aac.iter = opt$minimum
best.aac = opt$objective
# Calculate the inverse probability weights
model.den$fitted = predict(model.den,newdata = mydata,
n.trees = floor(best.aac.iter), type = "response")
ps.den = dnorm((mydata$T-model.den$fitted)/sd(mydata$T-model.den
$fitted),0,1)
weight.gbm = ps.num/ps.den
### model with estimated propensity score times reward/outcome as weigths
#generate a new dataset for evaluating value function
set.seed(5000 + i)
C_new = -10
N_new = 1000
beta_new = beta0
mbeta_new = meanfunction
p_new = length(beta_new) - 1
X_new = matrix(rnorm(N_new * p,0,1),nrow = N_new)
dose_optim = g(cbind(1,X_new) %*% beta_new)
### model with estimated propensity score times reward/outcome as weigths
model_pen = svm(x = X[index,], y = A[index], w = (trainweight * weight.gbm)[index], type = "eps-regression",
epsilon = 0.15, scale = FALSE)
tmpvalue2 = predict(model_pen,X_new)
tmpvalue2 = pmin(pmax(tmpvalue2,0),1)
Yhat2 = mbeta_new(X_new) + C_new*(tmpvalue2-dose_optim)^2
kol_value_hat[i] = mean(Yhat2)
write.csv(kol_value_hat, paste("results/simulation/kol-sim04-obs-value", samplesize, ".csv"),row.names = FALSE)
}
cat("\n Sample Size:",samplesize,"\n")
cat("Mean of V:", mean(kol_value_hat),"\n")
cat("Sd of V:", sd(kol_value_hat),"\n")
}