version 0.9.0

DESCRIPTION

@@ -2,21 +2,31 @@ Package: mvinfluence
 Type: Package
 Title: Influence Measures and Diagnostic Plots for Multivariate Linear
         Models
-Version: 0.8-3
-Date: 2018-05-16
-Author: Michael Friendly
+Version: 0.9.0
+Date: 2022-09-10
+Authors@R: 
+    person(given ="Michael", 
+           family = "Friendly",
+           email = "friendly@yorku.ca", 
+           role = c("aut", "cre"),
+           comment = c(ORCID = "0000-0002-3237-0941"))
 Maintainer: Michael Friendly <friendly@yorku.ca>
 Description: Computes regression deletion diagnostics for multivariate linear models and provides some associated
 	diagnostic plots.  The diagnostic measures include hat-values (leverages), generalized Cook's distance, and
 	generalized squared 'studentized' residuals.  Several types of plots to detect influential observations are
 	provided.
 Depends: car, heplots
-Suggests:
+Suggests: knitr, rmarkdown, ggplot2, tibble, patchwork, rgl, dplyr
 LazyData: TRUE
+VignetteBuilder: knitr
+Encoding: UTF-8
 License: GPL-2
+Language: en-US
 URL: https://github.com/friendly/mvinfluence
 BugReports: https://github.com/friendly/mvinfluence/issues
-Packaged: 2018-05-17 12:28:23 UTC; Friendly
+Packaged: 2022-09-20 16:49:20 UTC; friendly
+RoxygenNote: 7.2.1
 NeedsCompilation: no
+Author: Michael Friendly [aut, cre] (<https://orcid.org/0000-0002-3237-0941>)
 Repository: CRAN
-Date/Publication: 2018-05-17 17:01:07 UTC
+Date/Publication: 2022-09-20 17:10:02 UTC

MD5

@@ -1,28 +1,44 @@
-1c07be2e0c5bbe21a9bd4bf16b9b7ba7 *DESCRIPTION
-3929b04f43ef8f79555e80f65271b747 *NAMESPACE
-7fdab54a6c9b91ad720fbc9036e5b1a5 *NEWS
-49bc7ebcfa2f3f476d13b4875613cd9d *R/Jfuns.R
-06e1290da5c78cf6a91d38930b4a8252 *R/as.data.frame.inflmlm.R
-15114d05d1500e91790a4e6431f5c4f1 *R/cooks.distance.mlm.R
-c55b965d1603ed5eac3ab22180fb22f0 *R/hatvalues.mlm.R
-3faff10861c8fbf8c365567f646a9022 *R/infIndexPlot.R
-0e14b325696afceba3f1ac95aaf6b4d7 *R/influence.mlm.R
-d47fee763278b361a68f5727b7e34741 *R/influencePlot.mlm.R
-de37a9e4cbe7b1131769679115ef376d *R/lrPlot.R
-f2228772ea8fa017bd6a7ccfc8739f24 *R/mlm.influence.R
-7e8913d0afd5dc3b6b0960a6ed4e0c5f *R/mpower.R
-3f2b26d56c2b1ea50847dbb4e070e571 *R/print.inflmlm.R
-c455d858c18ba6024178ad65848253a4 *R/tr.R
-514e37d6dcfe1b6f91ef7716edca4358 *build/partial.rdb
+2012e00080b590a585c2d1becaa087b5 *DESCRIPTION
+7a5993b346c13f2525bbea88da61c0da *NAMESPACE
+2a333652ac77fe5f40b89aa36375a154 *NEWS
+491f3374662db29d33a6adae35c0670b *R/Jfuns.R
+825b78c5dd0914300a26b0bc3f17f3cb *R/as.data.frame.inflmlm.R
+48ef35f87ce844b7ba48d66747aef849 *R/cooks.distance.mlm.R
+fd1a5195ea357f514d9753302481c9b2 *R/data.R
+2ed4a0a7c6f0a96f767a0b7e6b54c482 *R/hatvalues.mlm.R
+8b7b5a431fb5d075261e0db0ce764728 *R/infIndexPlot.R
+b3a7b0ac5fb53e46f394fb3c652c49df *R/influence.mlm.R
+26cfdc9b64fb2c524f6f88bb5232813c *R/influencePlot.mlm.R
+d08b35411458e6a645cf72d56a565e15 *R/lrPlot.R
+d1e0884fa1dac0e18a1864184086b7d9 *R/mlm.influence.R
+323c725cc3de6bebddbce416845448b9 *R/mpower.R
+f987a9a9a01175c898020252355a6952 *R/mvinfluence-package.R
+92491f4656b2c51654f6b4cd682976c6 *R/print.inflmlm.R
+e7da14a110a10d16032b674d0fd846e6 *R/tr.R
+f25a8619493445f188092a97b4d02808 *build/vignette.rds
 0d7a8dcfee6858612d29312323be439e *data/Fertilizer.RData
-ed140f0678b98693e8e63848efdc86c4 *man/Fertilizer.Rd
-c502ca573146e16df22b7c6ea2f0014f *man/Jfuns.Rd
-f3aa73270289d22dc741f37ba4778e21 *man/infIndexPlot.mlm.Rd
-6a96040426010375959dec31db876cab *man/influence.measures.Rd
-6fbb1a5481d3c7d5ea03fbf858dd3fcd *man/influence.mlm.Rd
-78115c0f8e8aa84b7054e4057b440d6c *man/influencePlot.mlm.Rd
-8e95b216d551cb532ba48ea91696fc0d *man/lrPlot.Rd
-05e441e6a98f380b8c67559cb01d56a5 *man/mlm.influence.Rd
-486e141717ec3d594e5328cac11e0c10 *man/mpower.Rd
-2b03dce00dd6adb1c2cee4cb23f36eb3 *man/mvinfluence-package.Rd
-69f0a830fa15cea5d1a0083f87adc438 *man/tr.Rd
+71f07e44feed8bc45a1a3a386afddf9b *inst/WORDLIST
+5d98fbfe67d6d931331b66d0e2eb8263 *inst/doc/uni-vs-multi.R
+92a6966895ebea8922d80f08d7d88493 *inst/doc/uni-vs-multi.Rmd
+09265eece8aa02e9dc07f912d60d7b3b *inst/doc/uni-vs-multi.html
+cfa5dc385fd4c375d98f725655d60a6b *man/Fertilizer.Rd
+9fae563ac277e85b7423719dec83ee4c *man/Jtr.Rd
+bea1077751ca6af9a17aee903c8ee4a1 *man/as.data.frame.inflmlm.Rd
+3c72348269093153d113224b0187fe49 *man/cooks.distance.mlm.Rd
+62ac4898ecb84a9c7e2ca8131908fdaf *man/figures/README-indexplot-1.png
+4d366c6b0501974690eabf07a1edace7 *man/figures/README-rob-index-plot-1.png
+f6d1d5782ed20c6da51a26193206958e *man/figures/README-rohwer1-1.png
+f6d1d5782ed20c6da51a26193206958e *man/figures/README-rohwer2-1.png
+115056eed09d0e3c93b9c709389e959e *man/figures/README-rohwer3-1.png
+c982220d9e17177be79ffed6bcadbd12 *man/figures/logo.png
+18beb2d139f1a1d90426eb514d74fb65 *man/hatvalues.mlm.Rd
+6a45ee81b44bcc860972fd79530eb3f5 *man/infIndexPlot.mlm.Rd
+da7a00c22badabb2f6ecdb70c1bd7c12 *man/influence.mlm.Rd
+9b4b5058a970e8018b84927d0d067961 *man/influencePlot.mlm.Rd
+a07e2b10c3ab83d8b115410f4d28fd44 *man/lrPlot.Rd
+d241f616a341241f70cec75546654f32 *man/mlm.influence.Rd
+f357f5dff77c043d2c05305e3f55c6f4 *man/mpower.Rd
+b84a1c5beeddb19acc3f59d74fcfb774 *man/mvinfluence.Rd
+2f551bd5964304c18b4c7f8c5c4e059e *man/print.inflmlm.Rd
+6867c63755088b8b9c494ba2483fceb9 *man/tr.Rd
+92a6966895ebea8922d80f08d7d88493 *vignettes/uni-vs-multi.Rmd

NAMESPACE

@@ -1,37 +1,42 @@
+# Generated by roxygen2: do not edit by hand
 
-# imports
-importFrom("car", "showLabels", "influencePlot", "infIndexPlot", "influenceIndexPlot")
-importFrom("heplots", "trans.colors", "Mahalanobis")
-importFrom("grDevices", "palette")
-importFrom("graphics", "abline", "axis", "box", "mtext", "par", "plot",
-                       "points", "text")
-importFrom("stats", "cooks.distance", "hatvalues", "influence",
-                    "coef", "model.frame", "model.matrix",
-                    "model.response", "qbeta", "qf", "residuals", "rstudent")
-importFrom("utils", "combn")
-
-#exportPattern("^[[:alpha:]]+")
-
-export(
-  cooks.distance.mlm,
-  hatvalues.mlm,
-  influence.mlm,
-  influencePlot.mlm,
-  mlm.influence,
-  lrPlot,
-  mpower,
-  tr,
-  infIndexPlot.mlm
-)
-
-S3method(lrPlot, lm)
-
-S3method(hatvalues, mlm)
-S3method(cooks.distance, mlm)
-S3method(influence, mlm)
-S3method(influencePlot, mlm)
-S3method(infIndexPlot, mlm)
-
-S3method(print, inflmlm)
-S3method(as.data.frame, inflmlm)
-
+S3method(as.data.frame,inflmlm)
+S3method(cooks.distance,mlm)
+S3method(hatvalues,mlm)
+S3method(infIndexPlot,mlm)
+S3method(influence,mlm)
+S3method(influencePlot,mlm)
+S3method(lrPlot,lm)
+S3method(print,inflmlm)
+export("%^%")
+export(lrPlot)
+export(mlm.influence)
+export(mpower)
+export(tr)
+importFrom(car,infIndexPlot)
+importFrom(car,influenceIndexPlot)
+importFrom(car,influencePlot)
+importFrom(car,showLabels)
+importFrom(grDevices,palette)
+importFrom(graphics,abline)
+importFrom(graphics,axis)
+importFrom(graphics,box)
+importFrom(graphics,mtext)
+importFrom(graphics,par)
+importFrom(graphics,plot)
+importFrom(graphics,points)
+importFrom(graphics,text)
+importFrom(heplots,Mahalanobis)
+importFrom(heplots,trans.colors)
+importFrom(stats,coef)
+importFrom(stats,cooks.distance)
+importFrom(stats,hatvalues)
+importFrom(stats,influence)
+importFrom(stats,model.frame)
+importFrom(stats,model.matrix)
+importFrom(stats,model.response)
+importFrom(stats,qbeta)
+importFrom(stats,qf)
+importFrom(stats,residuals)
+importFrom(stats,rstudent)
+importFrom(utils,combn)

NEWS

@@ -1,3 +1,10 @@
+Version 0.9-0 (2022-09-10)
+
+o Complete overhaul, using `roxygen2` for documentation
+o Expand README, now with better examples
+o Added package `vignette("uni-vs-multi")`
+o Document the formulas used in calculations in `mvfluence-package.R`
+
 Version 0.8-3 (2018-05-16)
 
 o update mvinfluence-package.Rd using utils::promptPackage to conform to avoid hard-wired DESCRIPTION metadata

R/Jfuns.R

@@ -4,30 +4,89 @@
 # These are simply experimental, probably not to be exported
 ##########################################################
 
+#' General Classes of Influence Measures
+#' 
+#' These functions implement the general classes of influence measures for
+#' multivariate regression models defined in Barrett and Ling (1992), Eqn 2.3,
+#' 2.4, as shown in their Table 1.
+#' 
+#' There are two classes of functions, denoted \eqn{J_I^{det}} and \eqn{J_I^{tr}},
+#' with parameters \eqn{n, p, q} of the data, \eqn{m} of the subset size
+#' and \eqn{a} and \eqn{b} which define powers of terms in the formulas, typically
+#' in the set \code{-2, -1, 0}.
+#' 
+#' They are defined in terms of the submatrices for a deleted index subset
+#' \eqn{I},
+#' \deqn{H_I = X_I (X^T X)^{-1} X_I} 
+#' \deqn{Q_I = E_I (E^T E)^{-1} E_I}
+#' corresponding to the hat and residual matrices in univariate models.
+#' 
+#' For subset size \eqn{m = 1} these evaluate to scalar equivalents of hat
+#' values and studentized residuals.
+#' 
+#' For subset size \eqn{m > 1} these are \eqn{m \times m} matrices and
+#' functions in the \eqn{J^{det}} class use \eqn{|H_I|} and \eqn{|Q_I|}, while
+#' those in the \eqn{J^{tr}} class use \eqn{tr(H_I)} and \eqn{tr(Q_I)}.
+#' 
+#' The functions \code{COOKD}, \code{COVRATIO}, and \code{DFFITS} implement
+#' some of the standard influence measures in these terms for the general cases
+#' of multivariate linear models and deletion of subsets of size \code{m>1},
+#' but they have not yet been incorporated into our main functions
+#' \code{\link{mlm.influence}} and \code{\link{influence.mlm}}.
+#' 
+#' @aliases Jdet Jtr COOKD COVRATIO DFFITS
+#' @param H a scalar or \eqn{m \times m} matrix giving the hat values for subset \eqn{I}
+#' @param Q a scalar or \eqn{m \times m} matrix giving the residual values for subset \eqn{I}
+#' @param a the \eqn{a} parameter for the \eqn{J^{det}} and \eqn{J^{tr}} classes
+#' @param b the \eqn{b} parameter for the \eqn{J^{det}} and \eqn{J^{tr}} classes
+#' @param f scaling factor for the \eqn{J^{det}} and \eqn{J^{tr}} classes
+#' @return The scalar result of the computation. 
+#' @author Michael Friendly
+#' @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 array
 
+#' J trace function
 Jtr <- function (H, Q, a, b, f) {
 		I <- diag(nrow(H))
 		res <- H %*% Q %*% mpower(I-H-Q, a) %*% mpower(I-H, b)
 		f * tr(res)
 	}
+
+#' J det function
+#' 
+#' @rdname Jtr
 Jdet <- function (H, Q, a, b, f) {
 		I <- diag(nrow(H))
 		res <- H %*% Q %*% mpower(I-H-Q, a) %*% mpower(I-H, b)
 		f * det(res)
 	}
 
-	# Cook D, in terms of Jtr()
+#' Cook D, in terms of Jtr()
+#'
+#' @param n sample size
+#' @param p number of predictor variables
+#' @param r number of response variables
+#' @param m deletion subset size
+#' @rdname Jtr
+
 COOKD <- function(H, Q, n, p, r, m) {
  		f <- (n-p)/p
  		Jtr(H, Q, 0, -2, f)
  }
 
-	# DFFITS^2, in terms of Jtr()
+#' DFFITS^2, in terms of Jtr()
+#' 
+#' @rdname Jtr
 DFFITS <- function(H, Q, n, p, r, m) {
  		f <- (n-p-m)/p
  		Jtr(H, Q, -1, 0, f)
  }
 
+#' COVRATIO, in terms of Jdet()
+#' @rdname Jtr
+
 COVRATIO <- function(H, Q, n, p, r, m) {
  		f <- ((n-p)/(n-p-m))^r*p
  		Jdet(H, Q, p, -(r+p), f)

R/as.data.frame.inflmlm.R

@@ -1,3 +1,20 @@
+#' Convert an inflmlm object to a data frame
+#' 
+#' This function is used internally in the package to convert the result of \code{mlm.influence()} to a data frame.
+#' It is not normally called by the user.
+#'
+#' @param x    An \code{inflmlm} object, as returned by \code{mlm.influence}
+#' @param ...  ignored
+#' @param FUN  in the case where the subset size, \code{m>1}, the function used on the \code{H, Q, L, R} to calculate
+#'             a single statistic. The default is \code{det}. An alternative is \code{tr}, for matrix trace.
+#' @param funnames logical. Should the \code{FUN} name be prepended to the statistics when creating a data frame?
+#'
+#' @return     A data frame containing the influence statistics
+#' @export
+#'
+#' @examples
+#' # none
+#' 
 as.data.frame.inflmlm <-
 function(x, ..., FUN=det, funnames=TRUE) {
 	m <- x$m

R/cooks.distance.mlm.R

@@ -1,3 +1,36 @@
+#' Cook's distance for a MLM
+#'
+#' The functions \code{cooks.distance.mlm} and \code{hatvalues.mlm} are
+#' designed as extractor functions for regression deletion diagnostics for
+#' multivariate linear models following Barrett & Ling (1992). These are close
+#' analogs of methods for univariate and generalized linear models handled by
+#' the \code{\link[stats]{influence.measures}} in the \code{stats} package.
+#' 
+#' In addition, the functions provide diagnostics for deletion of subsets of
+#' observations of size \code{m>1}.
+#' 
+#' @param model A \code{mlm} object, fit by \code{lm()}
+#' @param infl  A \code{inflmlm} object. The default simply runs \code{mlm.influence()} on the model, suppressing coefficients.
+#' @param ...   Ignored
+#'
+#' @importFrom stats cooks.distance hatvalues influence coef model.frame model.matrix
+#' @return      A vector of Cook's distances
+#' @export
+#' @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
+#' 
+#' 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)
+#' 
+#' hatvalues(Rohwer.mod)
+#' cooks.distance(Rohwer.mod)
+#' 
 cooks.distance.mlm <-
 function (model, infl = mlm.influence(model, do.coef = FALSE), ...) 
 {

R/data.R

@@ -0,0 +1,46 @@
+#' 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
+
+