datf {CovSel} R Documentation

## Simulated Data, Factors

### Description

This data is simulated. The covariates, X, and the treatment, T, are all generated by simulating independent bernoulli distributions or from a multivariate normal distribution and then dichotomizing to get binary variables with a certain dependence structure.The code generating the data is

`library(bindata)`
`set.seed(9327529)`
`n<-500`
`x1 <- rbinom(n, 1, prob = 0.5)`
`x25 <- rmvbin(n, bincorr=cbind(c(1,0.7),c(0.7,1)), margprob=c(0.5,0.5))`
`x34 <- rmvbin(n, bincorr=cbind(c(1,0.7),c(0.7,1)), margprob=c(0.5,0.5))`
`x2 <- x25[,1]`
`x3 <- x34[,1]`
`x4 <- x34[,2]`
`x5 <- x25[,2]`
`x6 <- rbinom(n, 1, prob = 0.5)`
`x7<- rbinom(n, 1, prob = 0.5)`
`x8 <- rbinom(n, 1, prob = 0.5)`
`e0<-rnorm(n)`
`e1<-rnorm(n)`
`p <- 1/(1 + exp(3 - 1.5 * x1 - 1.5 * x2 - 1.5 * x3 - 0.1 * x4 - 0.1 * x5 - 1.3 * x8))`
`T <- rbinom(n, 1, prob = p)`
`y0 <- 4 + 2 * x1 + 3 * x4 + 5 * x5 + 2 * x6 + e0`
`y1 <- 2 + 2 * x1 + 3 * x4+ 5 * x5 + 2 * x6 + e1`
`y <- y1 * T + y0 * (1 - T)`
`datf <- data.frame(x1, x2, x3, x4, x5, x6, x7, x8, y0, y1, y, T)`
`datf[, 1:8] <- lapply(datf[, 1:8], factor)`
`datf[, 12] <- as.numeric(datf[, 12])`

### Usage

`data(datf)`

### Format

A data frame with 500 observations on the following 12 variables.

`x1 `

a factor with two levels

`x2 `

a factor with two levels

`x3 `

a factor with two levels

`x4 `

a factor with two levels

`x5 `

a factor with two levels

`x6 `

a factor with two levels

`x7 `

a factor with two levels

`x8 `

a factor with two levels

`y0 `

a numeric vector

`y1 `

a numeric vector

`y `

a numeric vector

`T `

a numeric vector

[Package CovSel version 1.2.1 Index]