ex2.dag.data {abn} | R Documentation |
Synthetic validation data set for use with abn library examples
Description
10000 observations simulated from a DAG with 18 variables three sets each from Poisson, Bernoulli and Gaussian distributions.
Usage
ex2.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, 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
Examples
## 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")