R/data.R

In friendly/mvinfluence: Influence Measures and Diagnostic Plots for Multivariate Linear Models

#' Fertilizer Data
#' 
#' A small data set on the use of fertilizer (x) in relation to the amount of
#' grain (y1) and straw (y2) produced.
#' 
#' The first observation is an obvious outlier and influential observation.
#' 
#' @name Fertilizer
#' @docType data
#' @format A data frame with 8 observations on the following 3 variables.
#' \describe{ 
#'     \item{grain}{amount of grain produced}
#'     \item{straw}{amount of straw produced}
#'     \item{fertilizer}{amount of fertilizer applied} }
#' @references 
#'     Hossain, A. and Naik, D. N. (1989). Detection of influential
#'     observations in multivariate regression. 
#'     \emph{Journal of Applied  Statistics}, 16 (1), 25-37.
#' @source Anderson, T. W. (1984). \emph{An Introduction to Multivariate
#'     Statistical Analysis}, New York: Wiley, p. 369.
#' @keywords datasets
#' @examples
#' 
#' data(Fertilizer)
#' 
#' # simple plots
#' plot(Fertilizer, col=c('red', rep("blue",7)), 
#'      cex=c(2,rep(1.2,7)), 
#'      pch=as.character(1:8))
#'
#' # A biplot shows the data in 2D. It gives another view of how case 1 stands out in data space
#' biplot(prcomp(Fertilizer))
#' 
#' # fit the mlm
#' mod <- lm(cbind(grain, straw) ~ fertilizer, data=Fertilizer)
#' Anova(mod)
#' 
#' # influence plots (m=1)
#' influencePlot(mod)
#' influencePlot(mod, type='LR')
#' influencePlot(mod, type='stres')
#' 
#' 
NULL