R/influence.mlm.R

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

#' Regression Deletion Diagnostics for Multivariate Linear Models
#' 
#' This collection of functions is designed to compute regression deletion
#' diagnostics for multivariate linear models following Barrett & Ling (1992)
#' that are close analogs of methods for univariate and generalized linear
#' models handled by the \code{\link[stats]{influence.measures}} in the
#' \pkg{stats} package.
#' 
#' In addition, the functions provide diagnostics for deletion of subsets of
#' observations of size \code{m>1}.
#' 
#' \code{influence.mlm} is a simple wrapper for the computational function,
#' \code{\link{mlm.influence}} designed to provide an S3 method for class
#' \code{"mlm"} objects.
#' 
#' There are still infelicities in the methods for the \code{m>1} case in the
#' current implementation. In particular, for \code{m>1}, you must call
#' \code{influence.mlm} directly, rather than using the S3 generic
#' \code{influence()}.
#' 
#' @name influence.mlm
#' @aliases influence.mlm
#' @method influence mlm
#' @param model An \code{mlm} object, as returned by \code{\link[stats]{lm}}
#' @param do.coef logical. Should the coefficients be returned in the
#' \code{inflmlm} object?
#' @param m Size of the subsets for deletion diagnostics
#' @param \dots Other arguments passed to methods
#' @return \code{influence.mlm} returns an S3 object of class \code{inflmlm}, a
#' list with the following components 
#'     \item{m}{Deletion subset size} 
#'     \item{H}{Hat values, \eqn{H_I}. If \code{m=1}, a vector of
#'              diagonal entries of the \sQuote{hat} matrix.  Otherwise, a list of 
#'              \eqn{m \times m} matrices corresponding to the \code{subsets}.} 
#'      \item{Q}{Residuals, \eqn{Q_I}.} 
#'      \item{CookD}{Cook's distance values} 
#'      \item{L}{Leverage components} 
#'      \item{R}{Residual components} 
#'      \item{subsets}{Indices of the observations in the subsets of size \code{m}} 
#'      \item{labels}{Observation labels} 
#'      \item{call}{Model call for the \code{mlm} object}
#'     \item{Beta}{Deletion regression coefficients-- included if\code{do.coef=TRUE}} 
#' @author Michael Friendly
#' @seealso \code{\link{influencePlot.mlm}}, \code{\link{mlm.influence}}
#' @references Barrett, B. E. and Ling, R. F. (1992). General Classes of
#' Influence Measures for Multivariate Regression. \emph{Journal of the
#' American Statistical Association}, \bold{87}(417), 184-191.
#' @keywords models regression
#' @examples
#' 
#' # Rohwer data
#' data(Rohwer, package="heplots")
#' Rohwer2 <- subset(Rohwer, subset=group==2)
#' rownames(Rohwer2)<- 1:nrow(Rohwer2)
#' Rohwer.mod <- lm(cbind(SAT, PPVT, Raven) ~ n+s+ns+na+ss, data=Rohwer2)
#' 
#' # m=1 diagnostics
#' influence(Rohwer.mod) |> head()
#' 
#' # try an m=2 case
#' ## res2 <- influence.mlm(Rohwer.mod, m=2, do.coef=FALSE)
#' ## res2.df <- as.data.frame(res2)
#' ## head(res2.df)
#' ## scatterplotMatrix(log(res2.df))
#' 
#' 
#' influencePlot(Rohwer.mod, id.n=4, type="cookd")
#' 
#' 
#' # Sake data
#' data(Sake, package="heplots")
#' Sake.mod <- lm(cbind(taste,smell) ~ ., data=Sake)
#' influence(Sake.mod)
#' influencePlot(Sake.mod, id.n=3, type="cookd")
#' 
#' 
#' @export
influence.mlm <-
function(model, do.coef=TRUE, m=1, ...)
	mlm.influence(model, do.coef = do.coef, m=m,  ...)