R/Russett.R

In Tenenhaus/RGCCA: Regularized and Sparse Generalized Canonical Correlation Analysis for Multiblock Data

#' Russett data
#'
#' @format A data frame with 47 rows and 12 variables.
#' @docType data
#'
#' @description
#' The Russett data set (Russett, 1964) is studied in Gifi (1990). Three
#' blocks of variables have been defined for 47 countries. The first block
#' is related to "Agricultural Inequality", the second to
#' "Industrial Development", and the last one describes the
#' "Political Instability". Russett collected this data to study
#' relationships between Agricultural Inequality, Industrial Development and
#' Political Instability. Russett's hypotheses can be formulated as follows:
#' It is difficult for a country to escape dictatorship when its agricultural
#' inequality is above-average and its industrial development below-average.
#'
#' \describe{
#'
#' \item{X1}{Agricultural Inequality \itemize{
#'      \item GINI: Inequality of land distribution,
#'      \item FARM: Percentage of farmers that own half of the land,
#'      \item RENT: Percentage of farmers that rent all their land.
#' }}
#'
#' \item{X2}{Industrial Development \itemize{
#'      \item GNPR: Gross national product per capita ($1955),
#'      \item LABO: Percentage of labor forced employed in agriculture.
#' }}
#'
#' \item{X3}{Political Instability \itemize{
#'      \item INST: Instability of executive (45-61),
#'      \item ECKS: Number of violent internal war incidents (46-61),
#'      \item DEAT: Number of people killed as a result of civic group
#' violence (50-62),
#'      \item DEMOSTAB: Stable democracy,
#'      \item DEMOINST: Unstable democracy,
#'      \item DICTATOR: Dictatorship.
#' }}
#' }
#'
#' @references Russett B.M. (1964), Inequality and Instability: The Relation of
#' Land Tenure to Politics, World Politics 16:3, 442-454.
#' @references Gifi, A. (1990), Nonlinear multivariate analysis,
#' Chichester: Wiley.
#' @usage data(Russett)
#' @examples
#' #Loading of the Russett dataset
#' data(Russett)
#' #Russett is partitioned into three blocks (X_agric, X_ind, X_polit)
#' X_agric <- Russett[, c("gini", "farm", "rent")]
#' X_ind <- Russett[, c("gnpr", "labo")]
#' X_polit <- Russett[, c("inst", "ecks", "death", "demostab",
#'                        "demoinst", "dictator")]
#' A <- list(X_agric, X_ind, X_polit)
#' @keywords datasets
"Russett"