Synthetic validation data set for use with abn library examples
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).
binary,logit()=1+g1+b2+b3+p3+b4+g4+b5
gaussian,identity()=1
poisson,log()=1+g6
binary,logit()=1+p3+b4+p6
gaussian,identify()=1+b2
poisson,log()=1+b2
binary,logit()=1+g1+g2+p2+g3+p3+g4
gaussian,identify()=1+g1+p3+b4
poisson,log()=1
binary,logit()=1+g1+p3+p5
gaussian,identify()=1+b4;
poisson,log()=1+g1+b2+g2+b5
binary,logit()=1+b2+g2+b3+p3+g4
gaussian,identify()=1
poisson,log()=1+g1+g5+b6+g6
binary,logit()=1
gaussian,identify()=1
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")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.