diff --git a/R/MDS-class.R b/R/MDS-class.R
index 1fad442..9be510d 100644
--- a/R/MDS-class.R
+++ b/R/MDS-class.R
@@ -45,12 +45,18 @@
 #' 
 #' @slot GoF `numeric`, vector of goodness of fit indicators,
 #' as a function of number of dimensions.
-#' `GoF[nDim]` is the global goodness of fit.
+#' `GoF[nDim]` is the global goodness of fit.   
+#' 
+#' Note pseudo R square and goodness of fit indicators are essentially the 
+#' same indicator, only the definition of total sum of squares differ:
+#' - for pseudo RSq: TSS is calculated using the mean pairwise distance 
+#' as minimum
+#' - for goodness of fit: TSS is calculated using 0 as minimum
 #' 
 #' @slot smacofRes an object of class 'smacofB' containing the algorithmic 
 #' optimization results, for example stress and stress per point, 
 #' as returned by `smacof::smacofSym()` method.
-#'
+#' 
 #' @exportClass MDS
 #' 
 #' @return nothing
@@ -168,7 +174,7 @@ nDim <- function(x) {
 #' @export
 nPoints <- function(x) {
     stopifnot(inherits(x, "MDS"))
-    return(dim(x@pwDist)[1])
+    return(attr(x@pwDist, "Size"))
 }
 
 #' @rdname MDS
diff --git a/R/stats.R b/R/stats.R
index 7609d49..96fa2c3 100644
--- a/R/stats.R
+++ b/R/stats.R
@@ -1487,25 +1487,7 @@ channelSummaryStats <- function(
 #' @param maxDim in case `nDim` is found iteratively, 
 #' maximum number of dimensions the search procedure is allowed to explore
 #' @param ... additional parameters passed to SMACOF algorithm
-#'
-#' @return a list with six elements:
-#' - `$pwDist` the initial pair-wise distance (same as input)
-#' - `$proj` the final configuration, i.e. the projected data matrix 
-#' (`nSamples` rows, `nDim` columns) in `nDim` dimensions
-#' - `$projDist` the distance matrix of projected data
-#' - `stress` the global stress loss function final value 
-#' obtained from the SMACOF algorithm
-#' - `spp` the stress per point obtained from the SMACOF algorithm, i.e.
-#' the contribution of each point to the stress loss function
-#' - `$RSq` R squares, for each d, from 1 to `nDim`:
-#' the (pseudo) R square when taking all dims from 1 to d.
-#' - `$GoF` Goodness of fit, for each d, from 1 to `nDim`:
-#' the goodness of fit indicator (b/w 0 and 1) when taking all dims from 1 to d.
-#' Note pseudo R square and goodness of fit indicators are essentially the 
-#' same indicator, only the definition of total sum of squares differ:
-#' - for pseudo RSq: TSS is calculated using the mean pairwise distance 
-#' as minimum
-#' - for goodness of fit: TSS is calculated using 0 as minimum
+#' @return an object of S4 class `MDS` 
 #' 
 #' @importFrom stats as.dist dist
 #' @export
diff --git a/man/MDS.Rd b/man/MDS.Rd
index 3120299..bc894cd 100644
--- a/man/MDS.Rd
+++ b/man/MDS.Rd
@@ -93,7 +93,15 @@ as a function of number of dimensions.
 
 \item{\code{GoF}}{\code{numeric}, vector of goodness of fit indicators,
 as a function of number of dimensions.
-\code{GoF[nDim]} is the global goodness of fit.}
+\code{GoF[nDim]} is the global goodness of fit.
+
+Note pseudo R square and goodness of fit indicators are essentially the
+same indicator, only the definition of total sum of squares differ:
+\itemize{
+\item for pseudo RSq: TSS is calculated using the mean pairwise distance
+as minimum
+\item for goodness of fit: TSS is calculated using 0 as minimum
+}}
 
 \item{\code{smacofRes}}{an object of class 'smacofB' containing the algorithmic
 optimization results, for example stress and stress per point,
diff --git a/man/computeMetricMDS.Rd b/man/computeMetricMDS.Rd
index caed754..71be403 100644
--- a/man/computeMetricMDS.Rd
+++ b/man/computeMetricMDS.Rd
@@ -33,26 +33,7 @@ maximum number of dimensions the search procedure is allowed to explore}
 \item{...}{additional parameters passed to SMACOF algorithm}
 }
 \value{
-a list with six elements:
-\itemize{
-\item \verb{$pwDist} the initial pair-wise distance (same as input)
-\item \verb{$proj} the final configuration, i.e. the projected data matrix
-(\code{nSamples} rows, \code{nDim} columns) in \code{nDim} dimensions
-\item \verb{$projDist} the distance matrix of projected data
-\item \code{stress} the global stress loss function final value
-obtained from the SMACOF algorithm
-\item \code{spp} the stress per point obtained from the SMACOF algorithm, i.e.
-the contribution of each point to the stress loss function
-\item \verb{$RSq} R squares, for each d, from 1 to \code{nDim}:
-the (pseudo) R square when taking all dims from 1 to d.
-\item \verb{$GoF} Goodness of fit, for each d, from 1 to \code{nDim}:
-the goodness of fit indicator (b/w 0 and 1) when taking all dims from 1 to d.
-Note pseudo R square and goodness of fit indicators are essentially the
-same indicator, only the definition of total sum of squares differ:
-\item for pseudo RSq: TSS is calculated using the mean pairwise distance
-as minimum
-\item for goodness of fit: TSS is calculated using 0 as minimum
-}
+an object of S4 class \code{MDS}
 }
 \description{
 Multi-dimensional scaling projection of samples,
diff --git a/tests/testthat/test-MDS-class.R b/tests/testthat/test-MDS-class.R
index 78551d1..7b7caad 100644
--- a/tests/testthat/test-MDS-class.R
+++ b/tests/testthat/test-MDS-class.R
@@ -76,3 +76,12 @@ test_that("basic MDS class works", {
     smacofRes <- smacofRes(mdsObj)
     expect_equal(class(smacofRes), c("smacofB", "smacof"))
 })
+
+test_that("MDS object export and reimport works", {
+    outputFile <- base::tempfile()
+    saveRDS(object = mdsObj, file = outputFile)
+    mdsObj2 <- readRDS(file = outputFile)
+    ret <- validObject(mdsObj2)
+    expect_true(ret)
+})
+    
\ No newline at end of file