pcalg (version 2.2-0)

gmInt: Graphical Model 8-Dimensional Interventional Gaussian Example Data

Description

This data set contains a matrix with an ensemble of observational and interventional data from eight Gaussian variables. The corresponding (data generating) DAG model is also stored.

Usage

data(gmInt)

source

The data set is identical to the one generated by set.seed(40) p <- 8 n <- 5000 gGtrue <- randomDAG(p, prob = 0.3) pardag <- as(gGtrue, "GaussParDAG") pardag$set.err.var(rep(1, p)) targets <- list(integer(0), 3, 5) target.index <- c(rep(1, 0.6*n), rep(2, n/5), rep(3, n/5)) x1 <- rmvnorm.ivent(0.6*n, pardag) x2 <- rmvnorm.ivent(n/5, pardag, targets[[2]], matrix(rnorm(n/5, mean = 4, sd = 0.02), ncol = 1)) x3 <- rmvnorm.ivent(n/5, pardag, targets[[3]], matrix(rnorm(n/5, mean = 4, sd = 0.02), ncol = 1)) gmInt <- list(x = rbind(x1, x2, x3), targets = targets, target.index = target.index, g = gGtrue)

Details

The data was generated as indicated below. First, a random DAG model was generated, then 5000 samples were drawn from this model: 3000 observational ones, and 1000 each from an intervention at vertex 3 and 5, respectively (see gmInt$target.index).

Examples

data(gmInt)
str(gmInt, max = 3)
pairs(gmInt$x, gap = 0, pch = ".")