pcalg (version 2.7-1)

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)

Format

The format is a list of four components

x:

Matrix with 5000 rows (one row a measurement) and 8 columns (corresponding to the 8 variables

targets:

List of (mutually exclusive) intervention targets. In this example, the three entries integer(0), 3 and 5 indicate that the data set consists of observational data, interventional data originating from an intervention at vertex 3, and interventional data originating from an intervention at vertex 5.

target.index:

Vector with 5000 elements. Each entry maps a row of x to the corresponding intervention target. Example: gmInt$target.index[3322] == 2 means that x[3322, ] was simulated from an intervention at gmInt$targets[[2]], i.e. at vertex 3.

g:

Formal class 'graphNEL' [package "graph"] with 6 slots, representing the true DAG from which observational and interventional data was sampled.

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

# NOT RUN {
data(gmInt)
str(gmInt, max = 3)
pairs(gmInt$x, gap = 0, pch = ".")
# }

Run the code above in your browser using DataCamp Workspace