var33: simulated dataset from a DAG comprising of 33 variables
In abn: Modelling Multivariate Data with Additive Bayesian Networks

var33

R Documentation

simulated dataset from a DAG comprising of 33 variables

Description

250 observations simulated from a DAG with 17 binary variables and 16 continuous. A DAG of this data features in the vignette. Note that the conditional dependence relations given are those in the population and may differ in the realization of 250 observations.

Usage

var33

Format

A data frame with a mixture of discrete variables each of which is set as a factor and continuous variables. Joint distribution structure used to generate the data.

v1: Binary, independent.
v2: Gaussian, conditionally dependent upon v1.
v3: Binary, independent.
v4: Binary, conditionally dependent upon v3.
v5: Gaussian, conditionally dependent upon v6.
v6: Binary, conditionally dependent upon v4 and v7.
v7: Gaussian, conditionally dependent upon v8.
v8: Gaussian, conditionally dependent upon v9.
v9: Binary, conditionally dependent upon v10.
v10: Binary, independent.
v11: Binary, conditionally dependent upon v10, v12 and v19.
v12: Binary, independent.
v13: Gaussian, independent.
v14: Gaussian, conditionally dependent upon v13.
v15: Binary, conditionally dependent upon v14 and v21.
v16: Gaussian, independent.
v17: Gaussian, conditionally dependent upon v16 and v20.
v18: Binary, conditionally dependent upon v20.
v19: Binary, conditionally dependent upon v20.
v20: Binary, independent.
v21: Binary, conditionally dependent upon v20.
v22: Gaussian, conditionally dependent upon v21.
v23: Gaussian, conditionally dependent upon v21.
v24: Gaussian, conditionally dependent upon v23.
v25: Gaussian, conditionally dependent upon v23 and v26.
v26: Binary, conditionally dependent upon v20.
v27: Binary, independent.
v28: Binary, conditionally dependent upon v27, v29 and v31.
v29: Gaussian, independent.
v30: Gaussian, conditionally dependent upon v29.
v31: Gaussian, independent.
v32: Binary, conditionally dependent upon v21, v29 and v31.
v33: Gaussian, conditionally dependent upon v31.

Examples

## Constructing the DAG of the dataset:
dag33 <- matrix(0, 33, 33)
dag33[2,1] <- 1
dag33[4,3] <- 1
dag33[6,4] <- 1; dag33[6,7] <- 1
dag33[5,6] <- 1
dag33[7,8] <- 1
dag33[8,9] <- 1
dag33[9,10] <- 1
dag33[11,10] <- 1; dag33[11,12] <- 1; dag33[11,19] <- 1;
dag33[14,13] <- 1
dag33[17,16] <- 1; dag33[17,20] <- 1
dag33[15,14] <- 1; dag33[15,21] <- 1
dag33[18,20] <- 1
dag33[19,20] <- 1
dag33[21,20] <- 1
dag33[22,21] <- 1
dag33[23,21] <- 1
dag33[24,23] <- 1
dag33[25,23] <- 1; dag33[25,26] <- 1
dag33[26,20] <- 1
dag33[33,31] <- 1
dag33[33,31] <- 1
dag33[32,21] <- 1; dag33[32,31] <- 1; dag33[32,29] <- 1
dag33[30,29] <- 1
dag33[28,27] <- 1; dag33[28,29] <- 1; dag33[28,31] <- 1

abn documentation built on Nov. 3, 2023, 5:08 p.m.