Synthetic validation data set for use with abn library examples

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Description

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

Usage

1
ex1.dag.data

Format

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

Examples

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## 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)