var33: simulated dataset from a DAG comprising of 33 variables

var33R 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.

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 April 25, 2022, 9:06 a.m.