Nothing
R/data.R
In abn: Modelling Multivariate Data with Additive Bayesian Networks
#' @title Dataset related to average daily growth performance and abattoir findings in pigs commercial production.
#' @usage adg
#' @description
#' The case study dataset is about growth performance and abattoir findings in pigs commercial production in a selected set of 15 Canadian farms collected in March 1987.
#' @format
#' An adapted data frame of the original dataset which consists of 341 observations of 8 variables and a grouping variable (farm).
#' \describe{
#' \item{AR}{presence of atrophic rhinitis.}
#' \item{pneumS}{presence of moderate to severe pneumonia.}
#' \item{female}{sex of the pig (1=female, 0=castrated). }
#' \item{livdam}{presence of liver damage (parasite-induced white spots).}
#' \item{eggs}{presence of fecal/gastrointestinal nematode eggs at time of slaughter.}
#' \item{wormCount}{count of nematodes in small intestine at time of slaughter.}
#' \item{age}{days elapsed from birth to slaughter (days).}
#' \item{adg}{average daily weight gain (grams).}
#' \item{farm}{farm ID.}
#' }
#' @details
#' When using the data to fit an additive Bayesian network,
#' the variables \code{AR}, \code{pneumS}, \code{female}, \code{livdam},
#' \code{eggs} are considered binomial, \code{wormcount} Poisson,
#' \code{age} and \code{adg} Gaussian.
#' The variable \code{farm} can be used to adjust for grouping.
#'
#' @references
#' Kratzer, G., Lewis, F.I., Comin, A., Pittavino, M. and Furrer, R. (2019). "Additive Bayesian Network Modelling with the R Package abn". arXiv preprint arXiv:1911.09006.
#' Dohoo, Ian Robert, Wayne Martin, and Henrik Stryhn. Veterinary epidemiologic research. No. V413 DOHv. Charlottetown, Canada: AVC Incorporated, 2003.
#' @keywords datasets internal
"adg"
#' @title 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).
#'
#' \describe{
#' \item{b1}{binary, logit()=1 }
#' \item{b2}{binary, logit()=1 }
#' \item{b3}{binary, logit()=1 }
#' \item{b4}{binary, logit()=1 }
#' \item{b5}{binary, logit()=1 }
#' \item{b6}{binary, logit()=1 }
#' \item{b7}{binary, logit()=1 }
#' \item{b8}{binary, logit()=1 }
#' \item{b9}{binary, logit()=1 }
#' \item{b10}{binary, logit()=1 }
#' \item{g1}{gaussian, identity()=1 }
#' \item{g2}{gaussian, identity()=1 }
#' \item{g3}{gaussian, identity()=1 }
#' \item{g4}{gaussian, identity()=1 }
#' \item{g5}{gaussian, identity()=1 }
#' \item{g6}{gaussian, identity()=1 }
#' \item{g7}{gaussian, identity()=1 }
#' \item{g8}{gaussian, identity()=1 }
#' \item{g9}{gaussian, identity()=1 }
#' \item{g10}{gaussian, identity()=1 }
#' \item{p1}{poisson, log()=1 }
#' \item{p2}{poisson, log()=1 }
#' \item{p3}{poisson, log()=1 }
#' \item{p4}{poisson, log()=1 }
#' \item{p5}{poisson, log()=1 }
#' \item{p6}{poisson, log()=1 }
#' \item{p7}{poisson, log()=1 }
#' \item{p8}{poisson, log()=1 }
#' \item{p9}{poisson, log()=1 }
#' \item{p10}{poisson, log()=1 }
#'}
#' @examples
#' \dontrun{
#' ## 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))
#' }
#' @keywords datasets internal
"ex0.dag.data"
#' @title 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).
#' \describe{
#' \item{b1}{binary, logit()=1 }
#' \item{p1}{poisson, log()=1 }
#' \item{g1}{gaussian, identity()=1 }
#' \item{b2}{binary, logit()=1}
#' \item{p2}{poisson, log()=1+b1+p1 }
#' \item{b3}{binary, logit()=1+b1+g1+b2 }
#' \item{g2}{gaussian, identify()=1+p1+g1+b2 }
#' \item{b4}{binary, logit()=1+g1+p2}
#' \item{b5}{binary, logit()=1+g1+g2 }
#' \item{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")
#' @keywords datasets internal
"ex1.dag.data"
#' @title 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).
#' \describe{
#' \item{b1}{binary,logit()=1+g1+b2+b3+p3+b4+g4+b5}
#' \item{g1}{gaussian,identity()=1}
#' \item{p1}{poisson,log()=1+g6}
#' \item{b2}{binary,logit()=1+p3+b4+p6}
#' \item{g2}{gaussian,identify()=1+b2}
#' \item{p2}{poisson,log()=1+b2}
#' \item{b3}{binary,logit()=1+g1+g2+p2+g3+p3+g4}
#' \item{g3}{gaussian,identify()=1+g1+p3+b4}
#' \item{p3}{poisson,log()=1}
#' \item{b4}{binary,logit()=1+g1+p3+p5}
#' \item{g4}{gaussian,identify()=1+b4;}
#' \item{p4}{poisson,log()=1+g1+b2+g2+b5}
#' \item{b5}{binary,logit()=1+b2+g2+b3+p3+g4}
#' \item{g5}{gaussian,identify()=1}
#' \item{p5}{poisson,log()=1+g1+g5+b6+g6}
#' \item{b6}{binary,logit()=1}
#' \item{g6}{gaussian,identify()=1}
#' \item{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")
#' @keywords datasets internal
"ex2.dag.data"
#' @title Validation data set for use with abn library examples
#' @description
#' 1000 observations across with 13 binary variables and one grouping variable. Real (anonymised) data of unknown structure.
#' @usage ex3.dag.data
#' @format A data frame with 14 columns, where
#' \code{b1,b2,\dots,b13} are binary variables encoded as factors and
#' \code{group} is a factor with 100 factors defining the sampling
#' groups (10 observations each).
#'
#' @keywords datasets internal
"ex3.dag.data"
#' @title Valdiation data set for use with abn library examples
#' @description
#' 2000 observations across with 10 binary variables and one grouping variable. Real (anonymised) data of unknown structure.
#' @usage ex4.dag.data
#' @format A data frame with eleven columns:
#' \code{group} factor with 85 levels defining sampling groups;
#' \code{b1,\dots,b10} binary variables encoded as factors.
#'
#' @keywords datasets internal
"ex4.dag.data"
#' @title Valdiation data set for use with abn library examples
#' @description
#' 434 observations across with 18 variables, 6 binary and 12 continuous, and one grouping variable. Real (anonymised) data of unknown structure.
#' @usage ex5.dag.data
#' @format A data frame with 19 columns: \code{b1,\dots,b6} binary
#' variables, encoded as factors;
#' \code{g1,\dots,g12} continuous variables. Finally, the column
#' \code{group} defines sampling groups (encoded as a factor as well).
#'
#' @keywords datasets internal
"ex5.dag.data"
#' @title Valdiation data set for use with abn library examples
#' @description
#' 800 observations across with 8 variables, 1 count, 2 binary and 4 continuous, and 1 grouping variable. Real (anonymised) data of unknown structure.
#' @usage ex6.dag.data
#' @format A data frame with eight columns. Binary variables are factors
#' \describe{
#' \item{p1}{count}
#' \item{g1}{continuous}
#' \item{g2}{continuous}
#' \item{b1}{binary}
#' \item{b2}{binary}
#' \item{g3}{continuous}
#' \item{g4}{continuous}
#' \item{group}{factor,defines sampling groups}
#' }
#'
#' @keywords datasets internal
"ex6.dag.data"
#' @title Valdiation data set for use with abn library examples
#' @description
#' 10648 observations across with 3 variables, 2 binary and 1 grouping variable.
#' Real (anonymised) data of unknown structure.
#' @usage ex7.dag.data
#' @format A data frame, binary variables are factors
#' \describe{
#' \item{b1}{binary}
#' \item{b2}{binary}
#' \item{group}{factor, defines sampling groups}
#' }
#'
#' @keywords datasets internal
"ex7.dag.data"
#' @title Dataset related to Feline calicivirus infection among cats in Switzerland.
#' @description
#' The dataset is about the Feline calicivirus (FCV) infection among cats in Switzerland.
#' FCV is a virus that occurs worldwide in domestic cats but also in exotic felids.
#' FCV is a highly contagious virus that is the major cause of upper respiratory
#' disease or cat flue that affects felids. This is a complex disease caused by
#' different viral and bacterial pathogens, i.e., FCV, FHV-1, \emph{Mycoplasma felis},
#' \emph{Chlamydia felis} and \emph{Bordetella bronchiseptica}.
#' It can be aggravated by retrovirus infections such as FeLV and FIV.
#' This composite dynamic makes it very interesting for a BN modeling approach.
#' The data were collected between September 2012 and April 2013.
#' @usage FCV
#' @format An adapted data frame of the original dataset, which consists of 300 observations of 15 variables.
#' \describe{
#' \item{FCV}{Feline Calici Virus status (0/1).}
#' \item{FHV_1}{Feline Herpes Virus 1 status (0/1). }
#' \item{C_felis}{C-felis and Chlamydia felis status (0/1).}
#' \item{M_felis}{Mycoplasma felis status (0/1).}
#' \item{B_bronchiseptica}{B-bronchiseptica & Bordetella bronchispetica status (0/1).}
#' \item{FeLV}{feline leukosis virus status (0/1).}
#' \item{FIV}{feline immunodeficiency virus status (0/1).}
#' \item{Gingivostomatitis}{gingivostomatitis complex status (0/1).}
#' \item{URTD}{URTD complex (upper respiratory complex) (0/1).}
#' \item{Vaccinated}{vaccination status (0/1).}
#' \item{Pedigree}{pedigree (0/1).}
#' \item{Outdoor}{outdoor access (0/1).}
#' \item{Sex}{sex and castrated status (M, MN, F, FS).}
#' \item{GroupSize}{number of cats in the group (counts).}
#' \item{Age}{age in year (continuous)\.}
#' }
#' @references Berger, A., Willi, B., Meli, M. L., Boretti, F. S., Hartnack, S., Dreyfus, A., ... and Hofmann-Lehmann, R. (2015). Feline calicivirus and other respiratory pathogens in cats with Feline calicivirus-related symptoms and in clinically healthy cats in Switzerland. BMC Veterinary Research, 11(1), 282.
#'
#' @keywords datasets internal
"FCV"
#' @title Dataset related to diseases present in `finishing pigs', animals about to enter the human food chain at an abattoir.
#' @description
#' The data we consider here comprise of a randomly chosen batch of 50 pigs from
#' each of 500 randomly chosen pig producers in the UK.
#' The dataset consists of 25000 observations, 10 binary variables, and a grouping variable.
#' These are `finishing pigs', animals about to enter the human food chain at an abattoir.
#' Further description of the data set is present on the vignette.
#' @usage pigs.vienna
#' @format A data frame with a mixture of 10 discrete variables, each of which is set as a factor, and a grouping variable.
#' \describe{
#' \item{PC}{Binary.}
#' \item{PT}{Binary. }
#' \item{MS}{Binary.}
#' \item{HS}{Binary.}
#' \item{TAIL}{Binary.}
#' \item{Abscess}{Binary.}
#' \item{Pyaemia}{Binary.}
#' \item{EPcat}{Binary.}
#' \item{PDcat}{Binary.}
#' \item{plbinary}{Binary.}
#' \item{batch}{Group variable, corresponding to the 500 different pig producers}
#' }
#' @details
#' This dataset was used in an older version of the vignette.
#' See also the files provided in the package directories
#' \code{inst/bootstrapping_example} and \code{inst/old_vignette} give a
#' detailed analysis of the dataset and provide more details for a
#' bootstrapping example thereof.
#' @references Hartnack, S., et al. (2016) "Attitudes of Austrian veterinarians towards euthanasia in small animal practice: impacts of age and gender on views on euthanasia." BMC Veterinary Research 12.1: 26.
#'
#' @keywords datasets internal
"pigs.vienna"
#' @title simulated dataset from a DAG comprising of 33 variables
#' @description
#' 250 observations simulated from a DAG with 17 binary variables and 16 continuous.
#' A DAG of this data features in the vignette.
#' Note that the conditional dependence relations given are those in the population
#' and may differ in the realization of 250 observations.
#' @usage var33
#' @format A data frame with a mixture of discrete variables each of which is
#' set as a factor and continuous variables.
#' Joint distribution structure used to generate the data.
#' \describe{
#' \item{v1}{Binary, independent. }
#' \item{v2}{Gaussian, conditionally dependent upon v1. }
#' \item{v3}{Binary, independent. }
#' \item{v4}{Binary, conditionally dependent upon v3. }
#' \item{v5}{Gaussian, conditionally dependent upon v6. }
#' \item{v6}{Binary, conditionally dependent upon v4 and v7. }
#' \item{v7}{Gaussian, conditionally dependent upon v8. }
#' \item{v8}{Gaussian, conditionally dependent upon v9. }
#' \item{v9}{Binary, conditionally dependent upon v10. }
#' \item{v10}{Binary, independent. }
#' \item{v11}{Binary, conditionally dependent upon v10, v12 and v19. }
#' \item{v12}{Binary, independent.}
#' \item{v13}{Gaussian, independent.}
#' \item{v14}{Gaussian, conditionally dependent upon v13. }
#' \item{v15}{Binary, conditionally dependent upon v14 and v21. }
#' \item{v16}{Gaussian, independent. }
#' \item{v17}{Gaussian, conditionally dependent upon v16 and v20. }
#' \item{v18}{Binary, conditionally dependent upon v20. }
#' \item{v19}{Binary, conditionally dependent upon v20. }
#' \item{v20}{Binary, independent. }
#' \item{v21}{Binary, conditionally dependent upon v20. }
#' \item{v22}{Gaussian, conditionally dependent upon v21. }
#' \item{v23}{Gaussian, conditionally dependent upon v21. }
#' \item{v24}{Gaussian, conditionally dependent upon v23. }
#' \item{v25}{Gaussian, conditionally dependent upon v23 and v26. }
#' \item{v26}{Binary, conditionally dependent upon v20. }
#' \item{v27}{Binary, independent. }
#' \item{v28}{Binary, conditionally dependent upon v27, v29 and v31. }
#' \item{v29}{Gaussian, independent. }
#' \item{v30}{Gaussian, conditionally dependent upon v29. }
#' \item{v31}{Gaussian, independent. }
#' \item{v32}{Binary, conditionally dependent upon v21, v29 and v31. }
#' \item{v33}{Gaussian, conditionally dependent upon v31. }
#' }
#' @examples
#' ## Constructing the DAG of the dataset:
#' dag33 <- matrix(0, 33, 33)
#' dag33[2,1] <- 1
#' dag33[4,3] <- 1
#' dag33[6,4] <- 1; dag33[6,7] <- 1
#' dag33[5,6] <- 1
#' dag33[7,8] <- 1
#' dag33[8,9] <- 1
#' dag33[9,10] <- 1
#' dag33[11,10] <- 1; dag33[11,12] <- 1; dag33[11,19] <- 1;
#' dag33[14,13] <- 1
#' dag33[17,16] <- 1; dag33[17,20] <- 1
#' dag33[15,14] <- 1; dag33[15,21] <- 1
#' dag33[18,20] <- 1
#' dag33[19,20] <- 1
#' dag33[21,20] <- 1
#' dag33[22,21] <- 1
#' dag33[23,21] <- 1
#' dag33[24,23] <- 1
#' dag33[25,23] <- 1; dag33[25,26] <- 1
#' dag33[26,20] <- 1
#' dag33[33,31] <- 1
#' dag33[33,31] <- 1
#' dag33[32,21] <- 1; dag33[32,31] <- 1; dag33[32,29] <- 1
#' dag33[30,29] <- 1
#' dag33[28,27] <- 1; dag33[28,29] <- 1; dag33[28,31] <- 1
#'
#' @keywords datasets internal
"var33"
#' @title Toy Data Set for Examples in README
#' @description
#' 1000 observations with 5 variables: 2 continuous, 2 binary and 1 categorical.
#' @usage g2b2c_data
#' @format A data frame with five columns. Binary and categorical variables are factors.
#' \describe{
#' \item{G1}{gaussian}
#' \item{B1}{binomial}
#' \item{B2}{binomial}
#' \item{C}{categorical}
#' \item{G2}{gaussian}
#' }
#'
#' @keywords datasets internal
"g2b2c_data"
#' @title Toy Data Set for Examples in README
#' @description
#' 10000 observations with 6 variables: 2 continuous, 1 binary, 1 count, 1 categorical and 1 grouping factor.
#' @usage g2pbcgrp
#' @format A data frame with six columns. Binary and categorical variables are factors.
#' \describe{
#' \item{G1}{gaussian}
#' \item{P}{poisson}
#' \item{B}{binomial}
#' \item{C}{categorical}
#' \item{G2}{gaussian}
#' \item{group}{categorical}
#' }
#'
#' @keywords datasets internal
"g2pbcgrp"
Try the abn package in your browser
Any scripts or data that you put into this service are public.
abn documentation built on June 22, 2024, 10:23 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#' @title Dataset related to average daily growth performance and abattoir findings in pigs commercial production.
#' @usage adg
#' @description
#' The case study dataset is about growth performance and abattoir findings in pigs commercial production in a selected set of 15 Canadian farms collected in March 1987.
#' @format
#' An adapted data frame of the original dataset which consists of 341 observations of 8 variables and a grouping variable (farm).
#' \describe{
#' \item{AR}{presence of atrophic rhinitis.}
#' \item{pneumS}{presence of moderate to severe pneumonia.}
#' \item{female}{sex of the pig (1=female, 0=castrated). }
#' \item{livdam}{presence of liver damage (parasite-induced white spots).}
#' \item{eggs}{presence of fecal/gastrointestinal nematode eggs at time of slaughter.}
#' \item{wormCount}{count of nematodes in small intestine at time of slaughter.}
#' \item{age}{days elapsed from birth to slaughter (days).}
#' \item{adg}{average daily weight gain (grams).}
#' \item{farm}{farm ID.}
#' }
#' @details
#' When using the data to fit an additive Bayesian network,
#' the variables \code{AR}, \code{pneumS}, \code{female}, \code{livdam},
#' \code{eggs} are considered binomial, \code{wormcount} Poisson,
#' \code{age} and \code{adg} Gaussian.
#' The variable \code{farm} can be used to adjust for grouping.
#'
#' @references
#' Kratzer, G., Lewis, F.I., Comin, A., Pittavino, M. and Furrer, R. (2019). "Additive Bayesian Network Modelling with the R Package abn". arXiv preprint arXiv:1911.09006.
#' Dohoo, Ian Robert, Wayne Martin, and Henrik Stryhn. Veterinary epidemiologic research. No. V413 DOHv. Charlottetown, Canada: AVC Incorporated, 2003.
#' @keywords datasets internal
"adg"
#' @title 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).
#'
#' \describe{
#' \item{b1}{binary, logit()=1 }
#' \item{b2}{binary, logit()=1 }
#' \item{b3}{binary, logit()=1 }
#' \item{b4}{binary, logit()=1 }
#' \item{b5}{binary, logit()=1 }
#' \item{b6}{binary, logit()=1 }
#' \item{b7}{binary, logit()=1 }
#' \item{b8}{binary, logit()=1 }
#' \item{b9}{binary, logit()=1 }
#' \item{b10}{binary, logit()=1 }
#' \item{g1}{gaussian, identity()=1 }
#' \item{g2}{gaussian, identity()=1 }
#' \item{g3}{gaussian, identity()=1 }
#' \item{g4}{gaussian, identity()=1 }
#' \item{g5}{gaussian, identity()=1 }
#' \item{g6}{gaussian, identity()=1 }
#' \item{g7}{gaussian, identity()=1 }
#' \item{g8}{gaussian, identity()=1 }
#' \item{g9}{gaussian, identity()=1 }
#' \item{g10}{gaussian, identity()=1 }
#' \item{p1}{poisson, log()=1 }
#' \item{p2}{poisson, log()=1 }
#' \item{p3}{poisson, log()=1 }
#' \item{p4}{poisson, log()=1 }
#' \item{p5}{poisson, log()=1 }
#' \item{p6}{poisson, log()=1 }
#' \item{p7}{poisson, log()=1 }
#' \item{p8}{poisson, log()=1 }
#' \item{p9}{poisson, log()=1 }
#' \item{p10}{poisson, log()=1 }
#'}
#' @examples
#' \dontrun{
#' ## 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))
#' }
#' @keywords datasets internal
"ex0.dag.data"
#' @title 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).
#' \describe{
#' \item{b1}{binary, logit()=1 }
#' \item{p1}{poisson, log()=1 }
#' \item{g1}{gaussian, identity()=1 }
#' \item{b2}{binary, logit()=1}
#' \item{p2}{poisson, log()=1+b1+p1 }
#' \item{b3}{binary, logit()=1+b1+g1+b2 }
#' \item{g2}{gaussian, identify()=1+p1+g1+b2 }
#' \item{b4}{binary, logit()=1+g1+p2}
#' \item{b5}{binary, logit()=1+g1+g2 }
#' \item{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")
#' @keywords datasets internal
"ex1.dag.data"
#' @title 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).
#' \describe{
#' \item{b1}{binary,logit()=1+g1+b2+b3+p3+b4+g4+b5}
#' \item{g1}{gaussian,identity()=1}
#' \item{p1}{poisson,log()=1+g6}
#' \item{b2}{binary,logit()=1+p3+b4+p6}
#' \item{g2}{gaussian,identify()=1+b2}
#' \item{p2}{poisson,log()=1+b2}
#' \item{b3}{binary,logit()=1+g1+g2+p2+g3+p3+g4}
#' \item{g3}{gaussian,identify()=1+g1+p3+b4}
#' \item{p3}{poisson,log()=1}
#' \item{b4}{binary,logit()=1+g1+p3+p5}
#' \item{g4}{gaussian,identify()=1+b4;}
#' \item{p4}{poisson,log()=1+g1+b2+g2+b5}
#' \item{b5}{binary,logit()=1+b2+g2+b3+p3+g4}
#' \item{g5}{gaussian,identify()=1}
#' \item{p5}{poisson,log()=1+g1+g5+b6+g6}
#' \item{b6}{binary,logit()=1}
#' \item{g6}{gaussian,identify()=1}
#' \item{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")
#' @keywords datasets internal
"ex2.dag.data"
#' @title Validation data set for use with abn library examples
#' @description
#' 1000 observations across with 13 binary variables and one grouping variable. Real (anonymised) data of unknown structure.
#' @usage ex3.dag.data
#' @format A data frame with 14 columns, where
#' \code{b1,b2,\dots,b13} are binary variables encoded as factors and
#' \code{group} is a factor with 100 factors defining the sampling
#' groups (10 observations each).
#'
#' @keywords datasets internal
"ex3.dag.data"
#' @title Valdiation data set for use with abn library examples
#' @description
#' 2000 observations across with 10 binary variables and one grouping variable. Real (anonymised) data of unknown structure.
#' @usage ex4.dag.data
#' @format A data frame with eleven columns:
#' \code{group} factor with 85 levels defining sampling groups;
#' \code{b1,\dots,b10} binary variables encoded as factors.
#'
#' @keywords datasets internal
"ex4.dag.data"
#' @title Valdiation data set for use with abn library examples
#' @description
#' 434 observations across with 18 variables, 6 binary and 12 continuous, and one grouping variable. Real (anonymised) data of unknown structure.
#' @usage ex5.dag.data
#' @format A data frame with 19 columns: \code{b1,\dots,b6} binary
#' variables, encoded as factors;
#' \code{g1,\dots,g12} continuous variables. Finally, the column
#' \code{group} defines sampling groups (encoded as a factor as well).
#'
#' @keywords datasets internal
"ex5.dag.data"
#' @title Valdiation data set for use with abn library examples
#' @description
#' 800 observations across with 8 variables, 1 count, 2 binary and 4 continuous, and 1 grouping variable. Real (anonymised) data of unknown structure.
#' @usage ex6.dag.data
#' @format A data frame with eight columns. Binary variables are factors
#' \describe{
#' \item{p1}{count}
#' \item{g1}{continuous}
#' \item{g2}{continuous}
#' \item{b1}{binary}
#' \item{b2}{binary}
#' \item{g3}{continuous}
#' \item{g4}{continuous}
#' \item{group}{factor,defines sampling groups}
#' }
#'
#' @keywords datasets internal
"ex6.dag.data"
#' @title Valdiation data set for use with abn library examples
#' @description
#' 10648 observations across with 3 variables, 2 binary and 1 grouping variable.
#' Real (anonymised) data of unknown structure.
#' @usage ex7.dag.data
#' @format A data frame, binary variables are factors
#' \describe{
#' \item{b1}{binary}
#' \item{b2}{binary}
#' \item{group}{factor, defines sampling groups}
#' }
#'
#' @keywords datasets internal
"ex7.dag.data"
#' @title Dataset related to Feline calicivirus infection among cats in Switzerland.
#' @description
#' The dataset is about the Feline calicivirus (FCV) infection among cats in Switzerland.
#' FCV is a virus that occurs worldwide in domestic cats but also in exotic felids.
#' FCV is a highly contagious virus that is the major cause of upper respiratory
#' disease or cat flue that affects felids. This is a complex disease caused by
#' different viral and bacterial pathogens, i.e., FCV, FHV-1, \emph{Mycoplasma felis},
#' \emph{Chlamydia felis} and \emph{Bordetella bronchiseptica}.
#' It can be aggravated by retrovirus infections such as FeLV and FIV.
#' This composite dynamic makes it very interesting for a BN modeling approach.
#' The data were collected between September 2012 and April 2013.
#' @usage FCV
#' @format An adapted data frame of the original dataset, which consists of 300 observations of 15 variables.
#' \describe{
#' \item{FCV}{Feline Calici Virus status (0/1).}
#' \item{FHV_1}{Feline Herpes Virus 1 status (0/1). }
#' \item{C_felis}{C-felis and Chlamydia felis status (0/1).}
#' \item{M_felis}{Mycoplasma felis status (0/1).}
#' \item{B_bronchiseptica}{B-bronchiseptica & Bordetella bronchispetica status (0/1).}
#' \item{FeLV}{feline leukosis virus status (0/1).}
#' \item{FIV}{feline immunodeficiency virus status (0/1).}
#' \item{Gingivostomatitis}{gingivostomatitis complex status (0/1).}
#' \item{URTD}{URTD complex (upper respiratory complex) (0/1).}
#' \item{Vaccinated}{vaccination status (0/1).}
#' \item{Pedigree}{pedigree (0/1).}
#' \item{Outdoor}{outdoor access (0/1).}
#' \item{Sex}{sex and castrated status (M, MN, F, FS).}
#' \item{GroupSize}{number of cats in the group (counts).}
#' \item{Age}{age in year (continuous)\.}
#' }
#' @references Berger, A., Willi, B., Meli, M. L., Boretti, F. S., Hartnack, S., Dreyfus, A., ... and Hofmann-Lehmann, R. (2015). Feline calicivirus and other respiratory pathogens in cats with Feline calicivirus-related symptoms and in clinically healthy cats in Switzerland. BMC Veterinary Research, 11(1), 282.
#'
#' @keywords datasets internal
"FCV"
#' @title Dataset related to diseases present in `finishing pigs', animals about to enter the human food chain at an abattoir.
#' @description
#' The data we consider here comprise of a randomly chosen batch of 50 pigs from
#' each of 500 randomly chosen pig producers in the UK.
#' The dataset consists of 25000 observations, 10 binary variables, and a grouping variable.
#' These are `finishing pigs', animals about to enter the human food chain at an abattoir.
#' Further description of the data set is present on the vignette.
#' @usage pigs.vienna
#' @format A data frame with a mixture of 10 discrete variables, each of which is set as a factor, and a grouping variable.
#' \describe{
#' \item{PC}{Binary.}
#' \item{PT}{Binary. }
#' \item{MS}{Binary.}
#' \item{HS}{Binary.}
#' \item{TAIL}{Binary.}
#' \item{Abscess}{Binary.}
#' \item{Pyaemia}{Binary.}
#' \item{EPcat}{Binary.}
#' \item{PDcat}{Binary.}
#' \item{plbinary}{Binary.}
#' \item{batch}{Group variable, corresponding to the 500 different pig producers}
#' }
#' @details
#' This dataset was used in an older version of the vignette.
#' See also the files provided in the package directories
#' \code{inst/bootstrapping_example} and \code{inst/old_vignette} give a
#' detailed analysis of the dataset and provide more details for a
#' bootstrapping example thereof.
#' @references Hartnack, S., et al. (2016) "Attitudes of Austrian veterinarians towards euthanasia in small animal practice: impacts of age and gender on views on euthanasia." BMC Veterinary Research 12.1: 26.
#'
#' @keywords datasets internal
"pigs.vienna"
#' @title simulated dataset from a DAG comprising of 33 variables
#' @description
#' 250 observations simulated from a DAG with 17 binary variables and 16 continuous.
#' A DAG of this data features in the vignette.
#' Note that the conditional dependence relations given are those in the population
#' and may differ in the realization of 250 observations.
#' @usage var33
#' @format A data frame with a mixture of discrete variables each of which is
#' set as a factor and continuous variables.
#' Joint distribution structure used to generate the data.
#' \describe{
#' \item{v1}{Binary, independent. }
#' \item{v2}{Gaussian, conditionally dependent upon v1. }
#' \item{v3}{Binary, independent. }
#' \item{v4}{Binary, conditionally dependent upon v3. }
#' \item{v5}{Gaussian, conditionally dependent upon v6. }
#' \item{v6}{Binary, conditionally dependent upon v4 and v7. }
#' \item{v7}{Gaussian, conditionally dependent upon v8. }
#' \item{v8}{Gaussian, conditionally dependent upon v9. }
#' \item{v9}{Binary, conditionally dependent upon v10. }
#' \item{v10}{Binary, independent. }
#' \item{v11}{Binary, conditionally dependent upon v10, v12 and v19. }
#' \item{v12}{Binary, independent.}
#' \item{v13}{Gaussian, independent.}
#' \item{v14}{Gaussian, conditionally dependent upon v13. }
#' \item{v15}{Binary, conditionally dependent upon v14 and v21. }
#' \item{v16}{Gaussian, independent. }
#' \item{v17}{Gaussian, conditionally dependent upon v16 and v20. }
#' \item{v18}{Binary, conditionally dependent upon v20. }
#' \item{v19}{Binary, conditionally dependent upon v20. }
#' \item{v20}{Binary, independent. }
#' \item{v21}{Binary, conditionally dependent upon v20. }
#' \item{v22}{Gaussian, conditionally dependent upon v21. }
#' \item{v23}{Gaussian, conditionally dependent upon v21. }
#' \item{v24}{Gaussian, conditionally dependent upon v23. }
#' \item{v25}{Gaussian, conditionally dependent upon v23 and v26. }
#' \item{v26}{Binary, conditionally dependent upon v20. }
#' \item{v27}{Binary, independent. }
#' \item{v28}{Binary, conditionally dependent upon v27, v29 and v31. }
#' \item{v29}{Gaussian, independent. }
#' \item{v30}{Gaussian, conditionally dependent upon v29. }
#' \item{v31}{Gaussian, independent. }
#' \item{v32}{Binary, conditionally dependent upon v21, v29 and v31. }
#' \item{v33}{Gaussian, conditionally dependent upon v31. }
#' }
#' @examples
#' ## Constructing the DAG of the dataset:
#' dag33 <- matrix(0, 33, 33)
#' dag33[2,1] <- 1
#' dag33[4,3] <- 1
#' dag33[6,4] <- 1; dag33[6,7] <- 1
#' dag33[5,6] <- 1
#' dag33[7,8] <- 1
#' dag33[8,9] <- 1
#' dag33[9,10] <- 1
#' dag33[11,10] <- 1; dag33[11,12] <- 1; dag33[11,19] <- 1;
#' dag33[14,13] <- 1
#' dag33[17,16] <- 1; dag33[17,20] <- 1
#' dag33[15,14] <- 1; dag33[15,21] <- 1
#' dag33[18,20] <- 1
#' dag33[19,20] <- 1
#' dag33[21,20] <- 1
#' dag33[22,21] <- 1
#' dag33[23,21] <- 1
#' dag33[24,23] <- 1
#' dag33[25,23] <- 1; dag33[25,26] <- 1
#' dag33[26,20] <- 1
#' dag33[33,31] <- 1
#' dag33[33,31] <- 1
#' dag33[32,21] <- 1; dag33[32,31] <- 1; dag33[32,29] <- 1
#' dag33[30,29] <- 1
#' dag33[28,27] <- 1; dag33[28,29] <- 1; dag33[28,31] <- 1
#'
#' @keywords datasets internal
"var33"
#' @title Toy Data Set for Examples in README
#' @description
#' 1000 observations with 5 variables: 2 continuous, 2 binary and 1 categorical.
#' @usage g2b2c_data
#' @format A data frame with five columns. Binary and categorical variables are factors.
#' \describe{
#' \item{G1}{gaussian}
#' \item{B1}{binomial}
#' \item{B2}{binomial}
#' \item{C}{categorical}
#' \item{G2}{gaussian}
#' }
#'
#' @keywords datasets internal
"g2b2c_data"
#' @title Toy Data Set for Examples in README
#' @description
#' 10000 observations with 6 variables: 2 continuous, 1 binary, 1 count, 1 categorical and 1 grouping factor.
#' @usage g2pbcgrp
#' @format A data frame with six columns. Binary and categorical variables are factors.
#' \describe{
#' \item{G1}{gaussian}
#' \item{P}{poisson}
#' \item{B}{binomial}
#' \item{C}{categorical}
#' \item{G2}{gaussian}
#' \item{group}{categorical}
#' }
#'
#' @keywords datasets internal
"g2pbcgrp"
Try the abn package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.