Use MASS instead of robustbase

DESCRIPTION

@@ -27,7 +27,7 @@ Imports:
     graphics,
     grDevices,
     methods,
-    robustbase,
+    MASS,
     stats,
     utils
 Suggests: 

NAMESPACE

@@ -49,6 +49,7 @@ exportMethods(transform_plr)
 exportMethods(variance)
 exportMethods(variation)
 import(arkhe)
+importFrom(MASS,cov.rob)
 importFrom(arkhe,remove_NA)
 importFrom(arkhe,remove_zero)
 importFrom(arkhe,replace_NA)
@@ -72,7 +73,6 @@ importFrom(methods,setMethod)
 importFrom(methods,slot)
 importFrom(methods,slotNames)
 importFrom(methods,validObject)
-importFrom(robustbase,covMcd)
 importFrom(stats,as.dist)
 importFrom(stats,contr.helmert)
 importFrom(stats,cov)

R/AllGenerics.R

@@ -534,8 +534,11 @@ NULL
 #' @param cov A [`numeric`] matrix giving the covariance of the
 #'  distribution. If missing, will be estimated from `x`.
 #' @param robust A [`logical`] scalar: should robust location and scatter
-#'  estimation be used (see [robustbase::covMcd()])?
-#' @param ... Extra parameters to be passed to [robustbase::covMcd()].
+#'  estimation be used?
+#' @param method A [`character`] string specifying the method to be used.
+#'  It must be one of "`mve`" (minimum volume ellipsoid) or "`mcd`" (minimum
+#'  covariance determinant). Only used if `robust` is `TRUE`.
+#' @param ... Extra parameters to be passed to [MASS::cov.rob()].
 #'  Only used if `robust` is `TRUE`.
 #' @return A [`numeric`] vector.
 #' @seealso [stats::mahalanobis()]
@@ -666,7 +669,6 @@ NULL
 #'  `quantile` is used as a cut-off value for outlier detection: observations
 #'  with larger (squared) Mahalanobis distance are considered as potential
 #'  outliers.
-#' @param ... Extra parameters to be passed to [robustbase::covMcd()].
 #' @details
 #'  An outlier can be defined as having a very large Mahalanobis distance from
 #'  all observations. In this way, a certain proportion of the observations can

R/nexus-package.R

@@ -46,7 +46,7 @@
 #' @importFrom methods as as<- callGeneric callNextMethod
 #'  .hasSlot initialize is new setClass setGeneric setMethod slot slot<-
 #'  slotNames validObject .valueClassTest
-#' @importFrom robustbase covMcd
+#' @importFrom MASS cov.rob
 #' @importFrom stats as.dist contr.helmert cov dist mahalanobis ppoints qchisq
 #'  var
 #' @importFrom utils combn

R/outliers.R

@@ -9,11 +9,12 @@ NULL
 setMethod(
   f = "outliers",
   signature = c(object = "CompositionMatrix"),
-  definition = function(object, center = NULL, cov = NULL, robust = FALSE,
-                        quantile = 0.975, ...) {
+  definition = function(object, center = NULL, cov = NULL, robust = TRUE,
+                        method = c("mve", "mcd"), quantile = 0.975, ...) {
 
     dof <- ncol(object) - 1L
-    d2 <- mahalanobis(object, center = center, cov = cov, ..., robust = robust)
+    d2 <- mahalanobis(object, center = center, cov = cov, ...,
+                      robust = robust, method = method)
     d <- sqrt(d2)
     limit <- sqrt(stats::qchisq(p = quantile, df = dof))
     out <- d > limit

R/statistics.R

@@ -136,24 +136,23 @@ setMethod("dist", "CompositionMatrix", dist.CompositionMatrix)
 
 #' @export
 #' @method mahalanobis CompositionMatrix
-mahalanobis.CompositionMatrix <- function(x, center, cov, ..., robust = TRUE) {
+mahalanobis.CompositionMatrix <- function(x, center, cov, ..., robust = TRUE,
+                                          method = c("mve", "mcd")) {
   ## Transformation
   x <- transform_ilr(x)
 
   if (missingORnull(center) | missingORnull(cov)) {
-    if (robust) {
-      ## Robust estimators
-      v <- robustbase::covMcd(x, ...)
-    } else {
-      ## Standard estimators
-      v <- list(center = colMeans(x), cov = stats::cov(x))
-    }
-
-    est <- list(center = NULL, cov = NULL)
-    est$center <- if (missingORnull(center)) v$center else center
-    est$cov <- if (missingORnull(cov)) v$cov else cov
+    if (!robust) method <- "classical" # Standard estimators
+    else method <- match.arg(method, several.ok = FALSE) # Robust estimators
+    v <- MASS::cov.rob(x, method = method, ...)
   }
 
+  est <- list(center = NULL, cov = NULL)
+  est$center <- if (missingORnull(center)) v$center else center
+  est$cov <- if (missingORnull(cov)) v$cov else cov
+
+  message(v$sing)
+
   stats::mahalanobis(x, center = est$center, cov = est$cov)
 }