R/schoolsites.R

In heplots: Visualizing Hypothesis Tests in Multivariate Linear Models

#' @name schoolsites
#' @aliases schoolsites
#' @docType data
#' @title
#' Schooldata Sites
#'
#' @description
#' Descriptors for the sites of the \code{\link{schooldata}} dataset, from Charnes et al. (1981).
#' The study was designed to compare schools using Program Follow Through (PFT)
#' management methods of taking actions to achieve goals with those of
#' Non Follow Through (NFT). Observations \code{1:49} came from PFT sites
#' and \code{50:70} from NFT sites.
#' This dataset gives other descriptors for the sites, from their Exhibit C.
#'
#' @usage data("schoolsites")
#' @format
#'  A data frame with 70 observations on the following 7 variables.
#'  \describe{
#'    \item{\code{site}}{site number, a numeric vector}
#'    \item{\code{type}}{program type, a factor with levels \code{PFT} ("Program Follow Through") and \code{NFT} ("Non Follow Through")}
#'    \item{\code{model}}{education style model, a factor with levels \code{BA}, \code{Bank Street}, 
#'         \code{California Process}, \code{Cognitive Curriculum}, \code{DIM}, \code{EDC}, \code{Home-School},
#'          \code{ILM}, \code{Parent Education}, \code{Responsive Education}, \code{SEDL}, \code{TEEM}}
#'    \item{\code{site_name}}{location of site, a character vector}
#'    \item{\code{region}}{US region, a factor with levels \code{NC}, \code{NE}, \code{S}, \code{W}}
#'    \item{\code{city_size}}{city size, an ordered factor with levels \code{Rural} < \code{Small} < \code{Medium} < \code{Large}}
#'    \item{\code{student_pop}}{size of the student population, a numeric vector}
#'  }
#'
#' % @details 
#' %%  ~~ If necessary, more details than the __description__ above ~~
#' @source 
#' A. Charnes, W.W. Cooper and E. Rhodes (1981). Evaluating Program and
#' Managerial Efficiency: An Application of Data Envelopment Analysis to
#' Program Follow Through. \emph{Management Science}, \bold{27}, 668-697, Exhibit C.
#'
#' @seealso \code{\link{schooldata}}
#' @concept MANOVA
#' @examples
#' data(schoolsites)
#' str(schoolsites)
#' schools <- cbind(schooldata, schoolsites)
#' schools.mod <- lm(cbind(reading, mathematics, selfesteem) ~
#'                     education + occupation + visit + counseling + teacher +
#'                     type + region, data = schools)
#' car::Anova(schools.mod)
#'
#' @keywords datasets
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