Merge remote-tracking branch 'origin/devel'

DESCRIPTION

@@ -17,9 +17,10 @@ Authors@R: c(
     )
   )
 Description: 
-  Landmark Multi-Dimensional Scaling ('LMDS') is an extension of classical 'Torgerson MDS'.
-  'LMDS' aims to produce the same dimensionality reduction as `cmdscale()`, but scale
-  linearly with respect to the number of samples.
+  A fast dimensionality reduction method scaleable to large numbers of samples.
+  Landmark Multi-Dimensional Scaling (LMDS) is an extension of classical Torgerson MDS,
+  but rather than calculating a complete distance matrix between all pairs of samples,
+  only the distances between a set of landmarks and the samples are calculated.
 License: GPL-3
 Encoding: UTF-8
 Imports:
@@ -29,7 +30,7 @@ Imports:
 Suggests: 
   Matrix,
   testthat
-RoxygenNote: 6.1.1
+RoxygenNote: 7.1.0
 Roxygen: list(markdown = TRUE)
 URL: http://github.com/dynverse/lmds
 BugReports: https://github.com/dynverse/lmds/issues

NEWS.md

@@ -2,6 +2,7 @@
 
 Initial release of lmds.
 
-* Landmark Multi-Dimensional Scaling (LMDS) is an extension of classical Torgerson MDS.
-  LMDS aims to produce the same dimensionality reduction as `cmdscale()`, but scale
-  linearly with respect to the number of samples.
+* A fast dimensionality reduction method scaleable to large numbers of samples.
+  Landmark Multi-Dimensional Scaling (LMDS) is an extension of classical Torgerson MDS,
+  but rather than calculating a complete distance matrix between all pairs of samples,
+  only the distances between a set of landmarks and the samples are calculated.

R/cmdscale_landmarks.R

@@ -8,6 +8,8 @@
 #' @importFrom irlba partial_eigen
 #' @importFrom dynutils scale_uniform
 #'
+#' @return The dimensionality reduction in the form of a `ncol(dist_2lm)` by `ndim` matrix.
+#'
 #' @export
 #'
 #' @examples
@@ -65,5 +67,7 @@ cmdscale_landmarks <- function(dist_2lm, ndim = 3, rescale = TRUE, ...) {
     dimred <- dynutils::scale_uniform(dimred)
   }
 
+  colnames(dimred) <- paste0("comp_", seq_len(ndim))
+
   dimred
 }

R/lmds.R

@@ -1,8 +1,9 @@
 #' Landmark MDS
 #'
-#' Landmark Multi-Dimensional Scaling (LMDS) is an extension of classical 'Torgerson' MDS.
-#' LMDS scales to significantly larger data sets since it only computes the distances between
-#' a set of landmarks and all samples. Has built-in support for sparse matrices.
+#' A fast dimensionality reduction method scaleable to large numbers of samples.
+#' Landmark Multi-Dimensional Scaling (LMDS) is an extension of classical 'Torgerson MDS',
+#' but rather than calculating a complete distance matrix between all pairs of samples,
+#' only the distances between a set of landmarks and the samples are calculated.
 #'
 #' @inheritParams select_landmarks
 #' @inheritParams cmdscale_landmarks
@@ -11,6 +12,8 @@
 #'
 #' @include cmdscale_landmarks.R select_landmarks.R
 #'
+#' @return The dimensionality reduction in the form of a `nrow(x)` by `ndim` matrix.
+#'
 #' @examples
 #' library(Matrix)
 #' x <- Matrix::rsparsematrix(1000, 1000, .01)
@@ -40,13 +43,8 @@ lmds <- dynutils::inherit_default_params(
     )
 
     rownames(dimred) <- rownames(x)
-    colnames(dimred) <- paste0("comp_", seq_len(ndim))
+
 
     dimred
   }
 )
-
-
-
-
-

R/package.R

@@ -1,8 +1,9 @@
 #' Landmark Multi-Dimensional Scalng
 #'
-#' Landmark Multi-Dimensional Scaling (LMDS) is an extension of classical Torgerson MDS.
-#' LMDS aims to produce the same dimensionality reduction as `cmdscale()`, but scale
-#' linearly with respect to the number of samples.
+#' A fast dimensionality reduction method scaleable to large numbers of samples.
+#' Landmark Multi-Dimensional Scaling (LMDS) is an extension of classical Torgerson MDS´,
+#' but rather than calculating a complete distance matrix between all pairs of samples,
+#' only the distances between a set of landmarks and the samples are calculated.
 #'
 #' @importFrom assertthat assert_that
 #' @importFrom dynutils calculate_distance is_sparse

R/select_landmarks.R

@@ -18,7 +18,7 @@
 #' select_landmarks(x)
 select_landmarks <- function(
   x,
-  distance_method = c("euclidean", "pearson", "spearman", "cosine", "manhattan"),
+  distance_method,
   landmark_method = c("sample"),
   num_landmarks = 500
 ) {
@@ -46,3 +46,4 @@ select_landmarks <- function(
 
   dist_2lm
 }
+formals(select_landmarks)$distance_method <- unique(c("euclidean", dynutils::list_distance_methods())) # prioritise euclidean

README.Rmd

@@ -21,9 +21,10 @@ library(tidyverse)
 
 # `lmds`:  Landmark Multi-Dimensional Scaling
 
-Landmark Multi-Dimensional Scaling (LMDS) is an extension of classical Torgerson MDS.
-LMDS aims to produce the same dimensionality reduction as `cmdscale()`, but scale
-linearly with respect to the number of samples.
+A fast dimensionality reduction method scaleable to large numbers of samples.
+Landmark Multi-Dimensional Scaling (LMDS) is an extension of classical Torgerson MDS,
+but rather than calculating a complete distance matrix between all pairs of samples,
+only the distances between a set of landmarks and the samples are calculated.
 
 ```{r compare}
 library(lmds)

README.md

@@ -6,9 +6,11 @@ Status](https://travis-ci.org/dynverse/lmds.svg?branch=master)](https://travis-c
 
 # `lmds`: Landmark Multi-Dimensional Scaling
 
-Landmark Multi-Dimensional Scaling (LMDS) is an extension of classical
-Torgerson MDS. LMDS aims to produce the same dimensionality reduction as
-`cmdscale()`, but scale linearly with respect to the number of samples.
+A fast dimensionality reduction method scaleable to large numbers of
+samples. Landmark Multi-Dimensional Scaling (LMDS) is an extension of
+classical Torgerson MDS, but rather than calculating a complete distance
+matrix between all pairs of samples, only the distances between a set of
+landmarks and the samples are calculated.
 
 ``` r
 library(lmds)
@@ -44,7 +46,8 @@ changes.
 
 Initial release of lmds.
 
-  - Landmark Multi-Dimensional Scaling (LMDS) is an extension of
-    classical Torgerson MDS. LMDS aims to produce the same
-    dimensionality reduction as `cmdscale()`, but scale linearly with
-    respect to the number of samples.
+  - A fast dimensionality reduction method scaleable to large numbers of
+    samples. Landmark Multi-Dimensional Scaling (LMDS) is an extension
+    of classical Torgerson MDS, but rather than calculating a complete
+    distance matrix between all pairs of samples, only the distances
+    between a set of landmarks and the samples are calculated.

cran-comments.md

@@ -5,6 +5,12 @@ Initial release
 * Landmark Multi-Dimensional Scaling (LMDS) is an extension of classical Torgerson MDS.
   LMDS aims to produce the same dimensionality reduction as `cmdscale()`, but scale
   linearly with respect to the number of samples.
+
+Resubmission:
+* I extended the description field in the DESCRIPTION to better reflect the purpose of
+  the package. I did not add a reference, as there is no corresponding manuscript. 
+
+* I added `\value{}` fields to each of the functions.
 
 # Test environments