Nothing
set.seed(123)
N <- 1000
p = 10
Wmat <- matrix(rnorm(N * p), ncol = p)
beta1 <- 4+2*Wmat[,1]+2*Wmat[,2]+2*Wmat[,5]+2*Wmat[,6]+2*Wmat[,8]
beta0 <- 2+2*Wmat[,1]+2*Wmat[,2]+2*Wmat[,5]+2*Wmat[,6]+2*Wmat[,8]
tau <- 2
gcoef <- matrix(c(-1,-1,rep(0,(p)-2)),ncol=1)
Wm <- as.matrix(Wmat)
g <- 1/(1+exp(Wm%*%gcoef / 3))
# hist(g)
A <- rbinom(N, 1, prob = g)
sigma <- 1
epsilon <-rnorm(N,0,sigma)
Y <- beta0 + tau * A + epsilon
#----------------------------------------------------------------
#----------------------Test for glmnet CTMLE---------------------
#----------------------------------------------------------------
# ctmleGlmnet must provide user-specified Q
W_tmp <- data.frame(Wm[,1:3])
treated<- W_tmp[which(A==1),]
untreated<-W_tmp[which(A==0),]
Y1<-Y[which(A==1)]
Y0<-Y[which(A==0)]
#Initial Q-estimate
beta1hat <- predict(lm(Y1~.,
data=treated),newdata=W_tmp)
beta0hat <- predict(lm(Y0~.,
data=untreated),newdata=W_tmp)
Q <- matrix(c(beta0hat,beta1hat),ncol=2)
W = Wm
glmnet_fit <- cv.glmnet(y = A, x = Wm, family = 'binomial', nlambda = 40)
lambdas <-glmnet_fit$lambda[(which(glmnet_fit$lambda==glmnet_fit$lambda.min)):length(glmnet_fit$lambda)]
ctmle_fit1 <- ctmleGlmnet(Y=Y, A=A,
W=data.frame(W=W),
Q = Q, lambdas = lambdas,
ctmletype=1, alpha=.995,
family="gaussian",
gbound=0.025,like_type="loglik" ,
fluctuation="logistic", verbose=FALSE,
detailed=FALSE, PEN=FALSE,
V=5, stopFactor=10^6)
ctmle_fit2 <- ctmleGlmnet(Y=Y, A=A,
W=data.frame(W=W),
Q = Q, lambdas = lambdas,
ctmletype=2, alpha=.995,
family="gaussian",
gbound=0.025,like_type="loglik" ,
fluctuation="logistic", verbose=FALSE,
detailed=FALSE, PEN=FALSE,
V=5, stopFactor=10^6)
gcv <- stats::predict(glmnet_fit, newx=Wm, s="lambda.min",type="response")
gcv <- bound(gcv,c(0.025,0.975))
s_prev <- glmnet_fit$lambda[(1:which(glmnet_fit$lambda == glmnet_fit$lambda.min))]
gcvPrev <- stats::predict(glmnet_fit,newx=Wm,s=s_prev[length(s_prev)],type="response")
gcvPrev <- bound(gcvPrev,c(0.025,0.975))
tlme_fit <- tmle(Y = Y, A = A, W = W, Q = Q, g1W = gcv)
ctmle_fit3 <- ctmleGlmnet(Y=Y, A=A,
W=W, Q = Q,
ctmletype=3, g1W = gcv, g1WPrev = gcvPrev,
alpha=.995, family="gaussian",
gbound=0.025,like_type="loglik" ,
fluctuation="logistic", verbose=FALSE,
detailed=FALSE, PEN=FALSE,
V=5, stopFactor=10^6)
tlme_fit
ctmle_fit3
ctmle_fit2
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.