R/mpcmp-package.R

In thomas-fung/mpcmp: Mean-Parametrized Conway-Maxwell Poisson (COM-Poisson) Regression

#' Mean-parametrized Conway-Maxwell Poisson Regression
#'
#' @name mpcmp-package
#' @aliases mpcmp
#' @docType package
#' @title Mean-parametrized Conway-Maxwell Poisson Regression
#' @keywords package
#' @references
#' Fung, T., Alwan, A., Wishart, J. and Huang, A. (2019). \code{mpcmp}:
#' Mean-parametrized Conway-Maxwell Poisson Regression. R package version 0.2.0.
#'
#' Huang, A. (2017). Mean-parametrized Conway-Maxwell-Poisson regression models for
#' dispersed counts. \emph{Statistical Modelling} \bold{17}, 359--380.
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#' Attendance data set
#'
#' This data set gives the number of days absent
#' from high school and the gender, maths score (standardized score out of 100) and
#' academic programme ('General', 'Academic' and 'Vocational') of 314 students
#' sampled from two urban high schools. The attendance data frame has 314 observations
#' on 5 variables.
#'
#' @name attendance
#' @format A data frame with 314 observations on 5 variables.
#' \describe{
#' \item{id}{Identifier}
#' \item{gender}{gender}
#' \item{math}{standardized math score out of 100}
#' \item{daysabs}{number of days absent from high school}
#' \item{prog}{academic programme ('General', 'Academic' and 'Vocational')}
#' }
#'
#' @docType data
#' @keywords datasets
#' @usage
#' data(attendance)
#' @source \url{https://stats.oarc.ucla.edu/stat/stata/dae/nb_data.dta}
#' @examples
#' ## For examples see example(glm.cmp)
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#' Takeover Bids data set
#'
#' This data set gives the number of bids received by 126 US firms that were successful
#' targets of tender offers during the period 1978--1985, along with some explanatory
#' variables on the defensive actions taken by management of target firm, firm-specific
#' characteristics and intervention taken by federal regulators. The \code{takeoverbids}
#' data frame has 126 observations on 14 variables. The descriptions below are taken from
#'  Sáez-Castillo and Conde-Sánchez (2013).
#'
#' @name takeoverbids
#' @format A data frame with 126 observations on 14 variables.
#' \describe{
#' \item{bidprem}{bid price divided by price 14 working days before bid}
#' \item{docno}{doc no}
#' \item{finrest}{indicator variable for proposed change in ownership structure}
#' \item{insthold}{percentage of stock held by institutions}
#' \item{leglrest}{indicator variable for legal defence by lawsuit}
#' \item{numbids}{number of bids received after the initial bid}
#' \item{obs}{Identifier}
#' \item{rearest}{indicator variable for proposed changes in asset structure}
#' \item{regulatn}{indicator variable for Department of Justice intervention}
#' \item{size}{total book value of assets in billions of dollars}
#' \item{takeover}{Indicator. 1 if the company was being taken over}
#' \item{weeks}{time in weeks between the initial and final offers}
#' \item{whtknght}{indicator variable for management invitation
#' for friendly third-party bid}
#' \item{sizesq}{book value squared}
#' }
#' @docType data
#' @keywords datasets
#' @usage
#' data(takeoverbids)
#' @references
#' Cameron, A.C. and Johansson, P. (1997). Count Data Regression Models using Series
#' Expansions: with Applications. \emph{Journal of Applied Econometrics} \bold{12} 203--223.
#'
#' Cameron, A.C. and Trivedi P.K. (1998). Regression analysis of count data, Cambridge University Press, \url{http://cameron.econ.ucdavis.edu/racd/racddata.html} chapter 5.
#'
#' Croissant Y (2011) Ecdat: Datasets for econometrics, R Package, version 0.1-6.1.
#'
#' Jaggia, S. and Thosar, S. (1993). Multiple Bids as a Consequence of Target Management
#' Resistance \emph{Review of Quantitative Finance and Accounting} \bold{3}, 447--457.
#'
#' @source
#' Journal of Applied Econometrics data archive: \url{http://qed.econ.queensu.ca/jae/}.
#' @examples
#'
#' ### Huang (2017) Page 371--372: Underdispersed Takeover Bids data
#' data(takeoverbids)
#' M.bids <- glm.cmp(numbids ~ leglrest + rearest + finrest + whtknght
#'   + bidprem + insthold + size + sizesq + regulatn, data = takeoverbids)
#' M.bids
#' summary(M.bids)
#' plot(M.bids) # or autoplot(M.bids)
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#' Cotton Bolls data set
#'
#' This data set gives the observed number of bolls produced by the cotton
#' plants at five growth stages: vegetative, flower-bud, blossom, fig and cotton boll;
#' to examine the effect of five defoliation levels (0\%, 25\%, 50\%, 75\% and 100\%).
#'
#' @name cottonbolls
#' @format A data frame with 125 observations on 4 variables.
#' \describe{
#' \item{nc}{number of bolls produced by two cotton plants at harvest}
#' \item{stages}{growth stage}
#' \item{def}{artificial defoliation level}
#' \item{def2}{square of def}
#' }
#' @docType data
#' @keywords datasets
#' @usage data(cottonbolls)
#' @references
#' Zeviani, W.M., Riberio P.J. Jr., Bonat, W.H., Shimakura S.E. and Muniz J.A. (2014).
#' The Gamma-count distribution in the analysis of experimental underdispersed data.
#' \emph{Journal of Applied Statistics} \bold{41}, 2616--26.
#'
#' @source
#' Supplementary Content of Zeviani et al. (2014):
#' \url{http://www.leg.ufpr.br/doku.php/publications:papercompanions:zeviani-jas2014}
#'
#' @examples
#' ### Huang (2017) Page 373--375: Underdispersed Cotton bolls data
#' ### Model fitting for predictor V
#'
#' data(cottonbolls)
#' M.bolls <- glm.cmp(nc ~ 1 + stages:def + stages:def2, data = cottonbolls)
#' M.bolls
#' summary(M.bolls)
NULL

#' Fish data set
#'
#' This data set gives the the number of fish species in lakes of the world; to examine the
#' effect of the surface area of the lakes. The latitude of the lakes are also recorded.
#'
#' This data set is also used to illustrate that the fitting algorithm can handle some
#' larger count data.
#'
#' @name fish
#' @format A data frame with 70 observations on 4 variables.
#' \describe{
#' \item{lake}{name of the lakes}
#' \item{species}{number of fish species in lakes}
#' \item{area}{surface area (km squared) }
#' \item{latitude}{latitude of the lakes}
#' }
#' @docType data
#' @keywords datasets
#' @usage
#' data(fish)
#' @references
#' Barbour, C. D. and Brown, J. H. (1974). Fish species diversity in lakes. \emph{The
#' American Naturalist}, \bold{108}, 473--488.
#'
#' @examples
#' ### Barbour & Brown (1974): Overdispersed Fish data
#' \donttest{
#' data(fish)
#' M.fish <- glm.cmp(species ~ 1 + log(area), data = fish)
#' M.fish
#' summary(M.fish)
#' }
NULL

#' Sitophilus data set
#'
#' Ribeiro et al. (2013) carried out an experiment to assess the bioactivity of extracts from different parts (seeds, leaves and branches) of Annona mucosa (Annonaceae) to control Sitophilus zeamais (Coleoptera: Curculionidae), a major pest of stored maize/corn in Brazil.
#'
#' 10g of corn and 20 animals adults were placed in each Petri dish. Extracts prepared with different parts of mucosa or just water (control) were completely randomized with 10 replicates.
#'
#' The numbers of emerged insects (progeny) after 60 days and their corresponding treatments were recorded in this dataset.
#'
#' This dataset was obtained from the `cmpreg``package of Ribeiro Jr,
#' Zeviani & Demétrio (2019), which is based on the work of Ribeiro Junior et al. (2019).
#'
#' This data set is also used to illustrate the syntax for regression on the dispersion.
#' @name sitophilus
#' @format A data frame with 40 observations on 2 variables.
#' \describe{
#' \item{extract}{the treatment used}
#' \item{ninsect}{number emerged insects (progeny)}
#' }
#' @docType data
#' @keywords datasets
#' @usage
#' data(sitophilus)
#' @references
#' Ribeiro Junior, E.E., Zeviani, W.M., Bonat, W.H., Demétrio, C.G., & Hinde, J. (2019). Reparametrization of COM–Poisson regression models with applications in the analysis of experimental data. Statistical Modelling. https://doi.org/10.1177%2F1471082X19838651.
#'
#' Ribeiro, L.P., Vendramim, J.D., Bicalho, K.U., Andrade, M.S.,  Fernandes, J.B., Moral, R.A., & Demétrio C.G.B. (2013). Annona mucosa Jacq. (Annonaceae): A promising source of bioactive compounds against Sitophilus zeamais Mots. (Coleoptera: Curculionidae). \emph{Journal of Stored Products Research}, \bold{55}, 6-14.
#'
#' @examples
#' ## For examples see example(glm.cmp)
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## usethis namespace: start
#' @useDynLib mpcmp, .registration = TRUE
#' @importFrom Rcpp sourceCpp
## usethis namespace: end
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