10000 observations simulated from a DAG with 10 variables from Poisson, Bernoulli and Gaussian distributions.

1 |

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, like in glm(), e.g. logit()=1+p1 means a logit link function and comprises of an intercept term and a term involving p1).

- b1
binary, logit()=1

- p1
poisson, log()=1

- g1
gaussian, identity()=1

- b2
binary, logit()=1

- p2
poisson, log()=1+b1+p1

- b3
binary, logit()=1+b1+g1+b2

- g2
gaussian, identify()=1+p1+g1+b2

- b4
binary, logit()=1+g1+p2

- b5
binary, logit()=1+g1+g2

- g3
gaussian, identity()=1+g1+b2

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
## Not run:
## the true underlying stochastic model has DAG - this data is one realisation from this.
ex1.true.dag<-matrix(data=c(
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,
1,1,0,0,0,0,0,0,0,0,
1,0,1,1,0,0,0,0,0,0,
0,1,1,1,0,0,0,0,0,0,
0,0,1,0,1,0,0,0,0,0,
0,0,1,0,0,0,1,0,0,0,
0,0,1,1,0,0,0,0,0,0
), ncol=10,byrow=TRUE);
colnames(ex1.true.dag)<-rownames(ex1.true.dag)<-c("b1","p1","g1","b2","p2","b3","g2","b4",
"b5","g3");
## End(Not run)
``` |

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