ex1.dag.data: Synthetic validation data set for use with abn library...

In abn: Modelling Multivariate Data with Additive Bayesian Networks

Synthetic validation data set for use with abn library examples

Description

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

Usage

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

## The data is one realisation from the the underlying DAG:
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")

abn documentation built on Nov. 3, 2023, 5:08 p.m.