# NOT RUN {
data(EOC)
head(EOC)
# }
# NOT RUN {
# FULL data estimator
Dfull <- preDATA(EOC$D.full, EOC$CA125)
Dvec.full <- Dfull$Dvec
vus("full", T = EOC$CA125, Dvec = Dvec.full)
# }
# NOT RUN {
# Preparing the missing disease status
Dna <- preDATA(EOC$D, EOC$CA125)
Dfact.na <- Dna$D
Dvec.na <- Dna$Dvec
# FI estimator
rho.out <- rhoMLogit(Dfact.na ~ CA125 + CA153 + Age, data = EOC, test = TRUE)
vus("fi", T = EOC$CA125, Dvec = Dvec.na, V = EOC$V, rhoEst = rho.out)
# }
# NOT RUN {
# MSI estimator
vus("msi", T = EOC$CA125, Dvec = Dvec.na, V = EOC$V, rhoEst = rho.out)
# IPW estimator
pi.out <- psglm(V ~ CA125 + CA153 + Age, data = EOC, test = TRUE)
vus("ipw", T = EOC$CA125, Dvec = Dvec.na, V = EOC$V, piEst = pi.out)
# SPE estimator
vus("spe", T = EOC$CA125, Dvec = Dvec.na, V = EOC$V, rhoEst = rho.out, piEst = pi.out)
# KNN estimator, K = 1, Mahalanobis distance
XX <- cbind(EOC$CA125, EOC$CA153, EOC$Age)
rho.maha.1nn <- rhoKNN(X = XX, Dvec = Dvec.na, V = EOC$V, K = 1, type = "mahala")
vus("knn", T = EOC$CA125, Dvec = Dvec.na, V = EOC$V, rhoEst = rho.maha.1nn)
# }
# NOT RUN {
# }
Run the code above in your browser using DataCamp Workspace