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abn

dag_ex0

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

Modelling Multivariate Data with Additive Bayesian Networks

v2.5-0

GPL (>= 2)

Authors

Gilles Kratzer [aut, cre] (<https://orcid.org/0000-0002-5929-8935>), Fraser Iain Lewis [aut] (<https://orcid.org/0000-0003-4580-2712>), Reinhard Furrer [ctb] (<https://orcid.org/0000-0002-6319-2332>), Marta Pittavino [ctb] (<https://orcid.org/0000-0002-1232-1034>)

Initial release

2021-04-21

dag_ex0

Description

Usage

Format

Examples

abn

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