simu.data: Simulated data In RCAL: Regularized Calibrated Estimation

Description

A dataset simulated as in Tan (2020), Section 4.

Usage

 `1` ```data(simu.data) ```

Format

A data matrix with 800 rows and 202 columns.

Details

The dataset is generated as follows, where `y`, `tr`, and `x` represent an outcome, a treatment, and covariates respectively.

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50``` ```library(MASS) ### mt0 <- 1-pnorm(-1) mt1 <- dnorm(-1) mt2 <- -(2*pnorm(-1)-1)/2 - dnorm(-1) +1/2 mt3 <- 3*dnorm(-1) mt4 <- -3/2*(2*pnorm(-1)-1) - 4*dnorm(-1) +3/2 m.z1 <- mt0 + 2*mt1 + mt2 v.z1 <- mt0 + 4*mt1 + 6*mt2 + 4*mt3 + mt4 v.z1 <- v.z1 + 1 + 2*(mt1 + 2*mt2 + mt3) sd.z1 <- sqrt(v.z1 -m.z1^2) ### set.seed(123) n <- 800 p <- 200 noise <- rnorm(n) covm <- matrix(1,p,p) for (i1 in 1:p) for (i2 in 1:p) { covm[i1,i2] <- 2^(-abs(i1-i2)) } x <- mvrnorm(n, mu=rep(0,p), Sigma=covm) # transformation z <- x for (i in 1:4) { z[,i] <- ifelse(x[,i]>-1,x[,i]+(x[,i]+1)^2,x[,i]) z[,i] <- (z[,i]-m.z1) /sd.z1 # standardized } # treatment eta <- 1+ c( z[,1:4] %*% c(1, .5, .25, .125) ) tr <- rbinom(n, size=1, prob=expit(eta)) # outcome eta.y <- c( z[,1:4] %*% c(1, .5, .25, .125) ) y <- eta.y + noise # save; if using main effects of x, then both the propensity score # and outcome regression models are misspecified simu.data <- cbind(y, tr, x) save(simu.data, file="simu.data.rda") ```

References

Tan, Z. (2020) Model-assisted inference for treatment effects using regularized calibrated estimation with high-dimensional data, Annals of Statistics, 48, 811<e2><80><93>837.

RCAL documentation built on Nov. 8, 2020, 4:22 p.m.