ex0.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

300 observations simulated from a DAG with 10 binary variables, 10 Gaussian variables and 10 poisson variables.

Usage

ex0.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

b2

binary, logit()=1

b3

binary, logit()=1

b4

binary, logit()=1

b5

binary, logit()=1

b6

binary, logit()=1

b7

binary, logit()=1

b8

binary, logit()=1

b9

binary, logit()=1

b10

binary, logit()=1

g1

gaussian, identity()=1

g2

gaussian, identity()=1

g3

gaussian, identity()=1

g4

gaussian, identity()=1

g5

gaussian, identity()=1

g6

gaussian, identity()=1

g7

gaussian, identity()=1

g8

gaussian, identity()=1

g9

gaussian, identity()=1

g10

gaussian, identity()=1

p1

poisson, log()=1

p2

poisson, log()=1

p3

poisson, log()=1

p4

poisson, log()=1

p5

poisson, log()=1

p6

poisson, log()=1

p7

poisson, log()=1

p8

poisson, log()=1

p9

poisson, log()=1

p10

poisson, log()=1

Examples

## Not run: 
## The dataset was (essentially) generated using the following code:
datasize <- 300
tmp <- c(rep("y", as.integer(datasize/2)), rep("n", as.integer(datasize/2)))
set.seed(1)

ex0.dag.data <- data.frame(b1=sample(tmp, size=datasize, replace=TRUE),
                           b2=sample(tmp, size=datasize, replace=TRUE),
                           b3=sample(tmp, size=datasize, replace=TRUE),
                           b4=sample(tmp, size=datasize, replace=TRUE),
                           b5=sample(tmp, size=datasize, replace=TRUE),
                           b6=sample(tmp, size=datasize, replace=TRUE),
                           b7=sample(tmp, size=datasize, replace=TRUE),
                           b8=sample(tmp, size=datasize, replace=TRUE),
                           b9=sample(tmp, size=datasize, replace=TRUE),
                           b10=sample(tmp, size=datasize, replace=TRUE),
                           g1=rnorm(datasize, mean=0,sd=1),
                           g2=rnorm(datasize, mean=0,sd=1),
                           g3=rnorm(datasize, mean=0,sd=1),
                           g4=rnorm(datasize, mean=0,sd=1),
                           g5=rnorm(datasize, mean=0,sd=1),
                           g6=rnorm(datasize, mean=0,sd=1),
                           g7=rnorm(datasize, mean=0,sd=1),
                           g8=rnorm(datasize, mean=0,sd=1),
                           g9=rnorm(datasize, mean=0,sd=1),
                           g10=rnorm(datasize, mean=0,sd=1),
                           p1=rpois(datasize, lambda=10),
                           p2=rpois(datasize, lambda=10),
                           p3=rpois(datasize, lambda=10),
                           p4=rpois(datasize, lambda=10),
                           p5=rpois(datasize, lambda=10),
                           p6=rpois(datasize, lambda=10),
                           p7=rpois(datasize, lambda=10),
                           p8=rpois(datasize, lambda=10),
                           p9=rpois(datasize, lambda=10),
                           p10=rpois(datasize, lambda=10))

## End(Not run)

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