ex2.dag.data {abn} | R Documentation |

10000 observations simulated from a DAG with 18 variables three sets each from Poisson, Bernoulli and Gaussian distributions.

```
ex2.dag.data
```

A data frame, binary variables are factors. The relevant formulas are given below (note these do not give parameter estimates just the form of the relationships, e.g. logit()=1 means a logit link function and comprises of only an intercept term).

- b1
binary,logit()=1+g1+b2+b3+p3+b4+g4+b5

- g1
gaussian,identity()=1

- p1
poisson,log()=1+g6

- b2
binary,logit()=1+p3+b4+p6

- g2
gaussian,identify()=1+b2

- p2
poisson,log()=1+b2

- b3
binary,logit()=1+g1+g2+p2+g3+p3+g4

- g3
gaussian,identify()=1+g1+p3+b4

- p3
poisson,log()=1

- b4
binary,logit()=1+g1+p3+p5

- g4
gaussian,identify()=1+b4;

- p4
poisson,log()=1+g1+b2+g2+b5

- b5
binary,logit()=1+b2+g2+b3+p3+g4

- g5
gaussian,identify()=1

- p5
poisson,log()=1+g1+g5+b6+g6

- b6
binary,logit()=1

- g6
gaussian,identify()=1

- p6
poisson,log()=1+g5

```
## The true underlying stochastic model has DAG - this data is a single realisation.
ex2.true.dag <- matrix(data = c(
0,1,0,1,0,0,1,0,1,1,1,0,1,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,
0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,
0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,1,0,0,1,1,0,1,1,0,1,0,0,0,0,0,0,0,
0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,
0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,
0,1,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,
0,0,0,1,1,0,1,0,1,0,1,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0
), ncol = 18, byrow = TRUE)
colnames(ex2.true.dag) <- rownames(ex2.true.dag) <- c("b1","g1","p1","b2",
"g2","p2","b3","g3",
"p3","b4","g4","p4",
"b5","g5","p5","b6",
"g6","p6")
```

[Package *abn* version 3.0.4 Index]