Displaying 20 results from an estimated 9000 matches similar to: "convert dataframe to matrix for cmdscale"
2014 Nov 06
1
limit of cmdscale function
Hi
We have a few questions regarding the use of the "isoMDS" function.
When we run "isoMDS" function using 60,000 x 60,000 data matrix,
we get the following error message:
------------------------------------
cmdscale(d, k) : invalid value of 'n'
Calls: isoMDS -> cmdscale
------------------------------------
We checked the source code of "cmdscale" and
2013 Apr 26
1
prcomp( and cmdscale( not equivalent?
Hello,
I have a dilemma that I'm hoping the R gurus will be able to help resolve.
For background:
My data is in the form of a (dis)similarity matrix created from taking the
inverse of normalized reaction times. That is, each cell of the matrix
represents how long it took to distinguish two stimuli from one another-- a
square matrix of 45X45 where the diagonal values are all zero (since this
2016 Apr 25
1
how to create initial configuraton for isoMDS
Hi,
I'm trying to use isoMDS to project a directed graph to 2-dim vectors, but I got an error.
#here is the code to create the graph using igraph package and run isoMDS on it.
library(igraph)
library(MASS)
g<-make_graph(c(1,2, 2,3, 2,4, 3,4, 4,5, 5,6, 3,6, 1,6, 2,5),directed=TRUE)
dist<-distances(g, mode="out")
loc<-isoMDS(dist)
# below is content of the dist matrix
2005 Jan 08
0
cmdscale problem
Dear R developers,
there appears to be a small problem with function cmdscale: for
non-Euclidean distance matrices, using option add=FALSE (the default),
cmdscale misses the smallest eigenvalue. This affects GOF statistic g.1
(See Mardia, Kent + Bibby (1979): Multivariate Analysis, eq. (14.4.7).
The corresponding formula in Cox + Cox (2001): Multidimensional Scaling,
2nd ed., p 38, would
2004 Feb 26
2
Multidimensional scaling and distance matrices
Dear All,
I am in the somewhat unfortunate position of having to reproduce the
results previously obtained from (non-metric?) MDS on a "kinship" matrix
using Statistica. A kinship matrix measures affinity between groups, and
has its maximum values on the diagonal.
Apparently, starting with a nxn kinship matrix, all it was needed to do
was to feed it to Statistica flagging that the
2002 Nov 23
0
Intermittant hang in cmdscale (PR#2323)
Full_Name: Cam Webb
Version: 1.6.0 (fink X11 compile)
OS: Mac OS X (Jaguar)
Submission from: (NULL) (64.168.28.87)
This is an unpredictable, intermittant hang during cmdscale of the mva library.
Some data never cause a problem, other data always do, abut I can't track down
the difference in the structure of the data. Sometimes the function will work
for `difficult' data after it has
2011 Feb 14
1
Analyzing dissimilarity ratings with Multidimensional Scaling
Dear R-list members,
I need an help with the Multidimensional Scaling analysis (MDS).
So far I used the cmdscale() command in R, but I did not get the perceptual
map I would love to see,
and I would like to know if it is possible to get it using R, and if yes
how.
I also had a look to the functions isoMDS() and sammoc() but with no luck.
I summarize the experiment I performed, and I would ask you
2002 Feb 15
1
cmdscale k=1
In applying multidimensional scaling, it seems to me that sometimes the
underlying dimensionality of the matrix is 1. However I found a case
where cmdscale failed when I tried k=1. Here it is:
m<-matrix(
c(.5,.81,.23,.47,.61,
.19,.5,.06,.17,.28,
.77,.94,.5,.74,.85,
.53,.83,.26,.5,.64,
.39,.72,.15,.36,.5),
nrow=5)
# BTW I think cmdscale uses only the lower triangle--how to enter only
# that
2008 Feb 20
1
Stress with MDS
Hi,
I am looking for the best multidimensional configuration for my data (47*47
distance matrix).
I ve tried classical metric (cmdscale) and non metric MDS (isoMDS, nmds)
but it is now difficult to choose the best solution because of the
uncertainties in the definitions of the "stress" function.
So, same problem, several questions :
1. Statistical consideration : With
2004 Mar 26
2
Fwd: MDS problems [ajtee@ajtee.uklinux.net]
Hi all,
I'm trying to perform an MDS of some data that I have. When I use
cmdscale everything is fine and I get some interesting results however,
the tends to be low.
What I wnat to do is compare this with the Non-Metric MDS using isoMDS
or sammon. However, when I try using these I get the following message.
Error in isoMDS(x.dist) : zero or negative distance between objects 2
and 4
2001 Oct 12
1
MASS: isoMDS and sammon
If tbl is an object of class 'dist', you can do this:
a <- sammon(tbl, k=3)
But you can't do this:
b <- isoMDS(tbl, k=3)
Wouldn't it be sensible to have identical interfaces to sammon()
and isoMDS() ?
I think all that would be needed is to change this:
isoMDS <- function(d, y=cmdscale(d, 2), maxit=50, trace=TRUE)
{
...into this:
isoMDS <-
2007 Jul 23
2
cmdscale question
Hi.
I know matrices that use distances between places works fine when using
cmdscale. However, what about matricies such as:
A B C D E
A 0 1 23 12 9
B 1 0 10 12 3
C 23 10 0 23 4
D 12 12 23 0 21
E 9 3 4 21 0
i.e. matrices which do not represent physical distances between places (as
they would not make sense for real distances such as the one above)
1997 Oct 24
0
R-beta: Problem with cmdscale on R for W95
I'm using rseptbeta for w95,
I tried to do:
> library(mva)
> data(eurodist)
> cmdscale(eurodist)
Error in .C("dblcen", x, as.integer(n)) : C/Fortran function not in load table
>
how can I solve this problem?
Andrea Rossetti
_______________________________________________________
Statistica & Informatica per la Gestione delle Imprese |
Universit? degli Studi di
2004 Sep 08
8
isoMDS
Dear List:
I have a question regarding an MDS procedure that I am accustomed to
using. I have searched around the archives a bit and the help doc and
still need a little assistance. The package isoMDS is what I need to
perform the non-metric scaling, but I am working with similarity
matrices, not dissimilarities. The question may end up being resolved
simply.
Here is a bit of substantive
2000 Jan 28
2
Memory woes
Hello all-
I'm having some problems with memory consumption under R. I've tried
increasing the appropriate memory values, but it keeps asking for more;
I've even upped the heap size to 600M, significantly eating into swap
(256M real, 500+M swap). So, performance slows to a crawl.
What I'm trying to do is run isoMDS on a 4000x4000 matrix.
My first question is, how much memory
2001 Dec 18
0
cmdscale: labels missing (PR#1220)
The function cmdscale tries to copy names from the source to the
result. This only works if the source is a matrix.
If m is a matrix with labels (rownames) and d is an object of
class "dist" with labels, this works:
cmdscale(m)
...but with this, there are no labels in the results:
cmdscale(d)
However, this works:
cmdscale(as.matrix(d))
My suggestion is to change, in
2006 Apr 19
3
isoMDS and 0 distances
Hi,
I'm trying to do a non-metric multidimensional scaling using isoMDS.
However, I have some '0' distances in my data, and I'm not sure how to
deal with them. I'd rather not drop rows from the original data, as I am
comparing several datasets (morphology and molecular data) for the same
individuals, and it's interesting to see how much morphological
variation can be
2007 Jun 14
2
Difference between prcomp and cmdscale
I'm looking for someone to explain the difference between these
procedures. The function prcomp() does principal components anaylsis,
and the function cmdscale() does classical multi-dimensional scaling
(also called principal coordinates analysis).
My confusion stems from the fact that they give very similar results:
my.d <- matrix(rnorm(50), ncol=5)
rownames(my.d) <-
2007 Feb 13
4
isoMDS vs. other non-metric non-R routines
Dear useRs,
last week I asked you about a problem related to isoMDS. It turned
out that in my case isoMDS was trapped. Nonetheless, I still have
some problems with other data sets. Therefore I would like to know if
anyone here has experience with how well isoMDS performs in
comparison to other non-metric MDS routines, like Minissa.
I have the feeling that for large data sets with a high
2011 Apr 02
3
Plotting MDS (multidimensional scaling)
Hi,
I just encountered what I thought was strange behavior in MDS. However, it
turned out that the mistake was mine. The lesson learned from my mistake is
that one should plot on a square pane when plotting results of an MDS. Not
doing so can be very misleading. Follow the example of an equilateral
triangle below to see what I mean. I hope this helps others to avoid this
kind of headache.