f code. On the other hand, the
help page for codetools::checkUsage is quite cryptic. But it's good to know at
least where to look.
Sincerely,
Leonard
From: Ivan Krylov
Sent: Wednesday, February 28, 2024 10:36 AM
To: Leo Mada via R-help
Cc: Leo Mada
Subject
В Sat, 24 Feb 2024 03:08:26 +
Leo Mada via R-help пишет:
> Are there any tools to extract the function names called by
> reverse-dependencies?
For well-behaved packages that declare their dependencies correctly,
parsing the NAMESPACE for importFrom() and import() calls should give
you the
Dear R Users,
Are there any tools to extract the function names called by
reverse-dependencies?
I would like to group these functions using clustering methods based on the
co-occurrence in the reverse-dependencies.
Utility: It may be possible to split complex packages into modules with fewer
Hello,
I am not at all sure that the following answers the question.
The code below ries to find the optimal number of clusters. One of the
changes I have made to your call to kmeans is to subset DMs not dropping
the dim attribute.
library(cluster)
max_clust <- 10
wss <- numeric(max_clust)
Hi Subhamitra,
I think the fact that you are passing a vector of values rather than a
matrix is part of the problem. As you have only one value for each
country, The points plotted will be the index on the x-axis and the
value for each country on the y-axis. Passing a value for ylim= means
that
Dear all,
I am doing a one way between subjects anova in an unbalanced data set.
Suppose we have "a" levels of the one factor. I want to merge the not so
significantly different levels into the same cluster.
Can I do a Tukey Kramer HSD and then use the following algorithm:
For i in 2 : "a"
Hello Adrian,
It all depends on what the structure of the dataset is. For instance, you said
that all your values are betweenn -1 and 1. Do the data rown sum-squared up to
1? How about the means? Are they zero. I guess all this has to depend on the
application and how the data were processed
Dear group,
pardon me for a naive question. I have data matrix (11K rows , 4K columns).
The data range is between -1 to 1. Not strictly integers, but real
numbers with at least place values in millionths.
The data distribution is peculiar (if I do plot(density(myMatrix)), I
get nice bimodal
Hi all,
I'm learning about how to do clusters of clients. Ç
I've founde this nice presentation on the subject, but the data is not
avaliable to use. I've contacted the autor, hope he'll answer soon.
https://ds4ci.files.wordpress.com/2013/09/user08_jimp_custseg_revnov08.pdf
Someone knows
Hi everybody, I have a problem with a cluster analysis.
I am trying to use hclust, method=ward.
The Ward method works with SQUARED Euclidean distances.
Hclust demands a dissimilarity structure as produced by dist.
Yet, dist does not seem to produce a table of squared euclidean distances,
Dear Marianna,
the function agnes in library cluster can compute Ward's method from a raw
data matrix (at least this is what the help page suggests).
Also, you may not be using the most recent version of hclust. The most
recent version has a note in its help page that states:
Two different
Dear R help list,
I was just wondering whether there is a way to cluster the documentation files
of data sets in the package documentation index file, so that common prefixes
such as dat... are not necessary.
Best wishes,
Alrik
Dr.
Hello,
I am new to R (and a novice at statistics). I have a list of objects,
with
(ideally) 10 different attributes measured per object. However, in
reality,
I was not able to obtain all 10 attributes for every object, so there is
some data missing (unequal number of measured attributes
Hello,
Please advice on encoding data for the following clustering problem.
I have a dataset with car usage info. Dataset has the following fields:
1. Car model (Toyoya Celica, BMW, Nissan X-Trail, Mazda Cosmo, etc.)
2. Year built
3. Country where the car runs
4. Distance run by car before
Good morning,
I am analyzing a dataset composed by 364 subjects and 13 binary variables
(0,1 = absence,presence).
I am testing possible association (co-presence) of my variables. To do
this, I was trying with cluster analysis.
My main interest is to check for the significance of the obtained
Texas AM University
College Station, TX 77843-4352
-Original Message-
From: r-help-boun...@r-project.org [mailto:r-help-bounces@r-
project.org] On Behalf Of marco milella
Sent: Thursday, December 06, 2012 12:08 PM
To: r-help@r-project.org
Subject: [R] clustering of binary data
Dear R help,
I am trying to cluster my data according to group in a data frame such as
the following:
df=data.frame(group=rep(c(a,b,c,d),10),(replicate(100,rnorm(40
I'm not sure how to tell hclust() that I want to cluster according to the
group variable. For example:
Dear R help,
I am trying to cluster my data according to group in a data frame such as
the following:
df=data.frame(group=rep(c(a,b,c,d),10),(replicate(100,rnorm(40
I'm not sure how to tell hclust() that I want to cluster according to the
group variable. For example:
Hello playeRs!
I'm working on a project for a client. She's modeling hormone levels
periodically, and trying to develop a model and fit her data to that
model, and subsequently she's trying to cluster individuals based on how
well each fits the model.
I've been looking at grofit for this,
Please read the posting guide for future questions.
I presume you mean using the vegan package? If so, then see this blog
post of mine which shows how to do something similar:
http://wp.me/pZRQ9-73
If you post more details and an example I will help further if the blog
post is not sufficient
On 30.04.2012 18:44, borinot wrote:
Hello to all,
I'm new to R so I have a lot of problems with it, but I'll only ask the main
one.
I have clustered an environmental matrix
We do not know what that is. Where is the example data? See the posting
guide.
with 2 different methods,
Hello to all,
I'm new to R so I have a lot of problems with it, but I'll only ask the main
one.
I have clustered an environmental matrix with 2 different methods, and I'd
like to plot them in a PCA and a db-RDA. I mean, I want see these clusters
in the plots like points of differents colours,
Dear R users,
I'm having trouble with calculating pvalues for my 2d dataset. First I
performed clustering and I would like to get some info about the strength
of cluster membership for each point. I've calculated (thanks to nice
people help) the multivariate normal densities (mnd) using dmvnorm
Is there a package (and for that matter a function) that I can use to
create clustered wordclouds. The current wordcloud package simply has more
frequent words as larger words, whereas what I want is the cluster centre
to be the more frequent words but, the closer a word is to another the
higher
Hello all,
I am using the clustering functions in R in order to work with large
masses of binary time series data, however the clustering functions do not
seem able to fit this size of practical problem. Library 'hclust' is good
(though it may be sub par for this size of problem, thus doubly
Try the flow cytometry clustering functions in Bioconductor.
-thomas
On Thu, Aug 11, 2011 at 7:07 AM, Ken Hutchison vicvoncas...@gmail.com wrote:
Hello all,
I am using the clustering functions in R in order to work with large
masses of binary time series data, however the clustering
On Wed, Aug 10, 2011 at 12:07 PM, Ken Hutchison vicvoncas...@gmail.com wrote:
Hello all,
I am using the clustering functions in R in order to work with large
masses of binary time series data, however the clustering functions do not
seem able to fit this size of practical problem. Library
There is a number of methods in the literature to decide the number of
clusters for k-means. Probably the most popular one is the Calinski and
Harabasz index, implemented as calinhara in package fpc. A distance
based version (and several other indexes to do this) is in function
cluster.stats
PS to my previous posting: Also have a look at kmeansruns in fpc. This
runs kmeans for several numbers of clusters and decides the number of
clusters by either CalinskiHarabasz or Average Silhouette Width.
Christian
On Wed, 10 Aug 2011, Ken Hutchison wrote:
Hello all,
I am using the
-project.org
[mailto:r-help-boun...@r-project.org] On Behalf Of pguilha
Sent: 04 July 2011 19:22
To: r-help@r-project.org
Subject: [R] clustering based on most significant pvalues
does not separate the groups!
Hi all,
I have some microarray data on 40 samples that fall into two
groups. I
Yes absolutely, your explanation makes sense. Thanks very much.
rgds
Paul
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Hi all,
I have some microarray data on 40 samples that fall into two groups. I have
a value for 480k probes for each of those samples. I performed a t test
(rowttests) on each row(giving the indices of the columns for each group)
then used p.adjust() to adjust the pvalues for the number of tests
Dear Experts,
I am using the below script to generate the heat map of gene expression
data. I am using Hierarchical Clustering (hclust) for clustering. Now I want
to compare different clustering parameters such as *K-means* clustering, Model
Based Clustering,
I have two queries:
1. How to
Hi Guys
I want to apply a clustering algo to my dataset in order to find the
regions points(X,Y) which have similar values(percent_GC and
mean_phred_quality). Details below.
I have sampled 1% of points from my main data set of 85 million
points. The result is still somewhat large 800K points
Hi,
I have a gene expression experiment with 20 samples and 25000 genes each.
I'd like to perform clustering on these. It turned out to become much faster
when I transform the underlying matrix with t(matrix). Unfortunately then
I'm not anymore able to use cutree to access individual clusters. In
Don't you expect it to be a lot faster if you cluster 20 items instead of 25000?
-Original Message-
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
Behalf Of Maxim
Sent: Wednesday, March 02, 2011 4:08 PM
To: r-help@r-project.org
Subject: [R] clustering problem
-project.org
Subject: [R] clustering problem
Hi,
I have a gene expression experiment with 20 samples and 25000 genes each.
I'd like to perform clustering on these. It turned out to become much
faster
when I transform the underlying matrix with t(matrix). Unfortunately then
I'm not anymore able
After ordering the table of membership degrees , i must get the difference
between the first and second coloumns , between the first and second largest
membership degree of object i. This for K=2,K=3,to K.max=6.
This difference is multiplyed by the Crisp silhouette index vector (si). Too
it
After ordering the table of membership degrees , i must get the difference
between the first and second coloumns , between the first and second largest
membership degree of object i. This for K=2,K=3,to K.max=6.
This difference is multiplyed by the Crisp silhouette index vector (si). Too
it
Dear R-help,
I am doing clustering via finite mixture model. Please suggest some packages in
R to find clusters via finite mixture model with continuous variables. And
also I wish to verify the distributional properties of the mixture
distributions
by fitting the model with lognormal, gamma,
There are quite a few packages that work with finite mixtures, as
evidenced by the descriptions here:
http://cran.r-project.org/web/packages/index.html
These might be useful:
http://cran.r-project.org/web/packages/flexmix/index.html
http://cran.r-project.org/web/packages/mclust/index.html
After ordering the table of membership degrees , i must get the difference
between the first and second coloumns , between the first and second largest
membership degree of object i. This for K=2,K=3,to K.max=6.
This difference is multiplyed by the Crisp silhouette index vector (si). Too
it
I must get an index (fuzzy silhouette), a weighted average. A average the
crisp silhouette for every row (i) s and the weight of each term is
determined by the difference between the membership degrees of corrisponding
object to its first and second best matching fuzzy clusters.
i need the
hello,
i'm pete ,how can i order rows of matrix by max to min value?
I have a matrix of membership degrees, with 82 (i) rows and K coloumns, K
are clusters.
I need first and second largest elements of the i-th row.
for example
1 0.66 0.04 0.01 0.30
2 0.02 0.89 0.09 0.00
3 0.06 0.92 0.01 0.01
use 'apply':
head(x.m)
V2 V3 V4 V5
[1,] 0.66 0.04 0.01 0.30
[2,] 0.02 0.89 0.09 0.00
[3,] 0.06 0.92 0.01 0.01
[4,] 0.07 0.71 0.21 0.01
[5,] 0.10 0.85 0.04 0.01
[6,] 0.91 0.04 0.02 0.02
x.m.sort - apply(x.m, 1, sort, decreasing = TRUE)
head(t(x.m.sort))
[,1] [,2] [,3] [,4]
thank you ,you have been very kind
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__
R-help@r-project.org mailing list
Jüri,
How did you create the output?
An example to cluster transactions with arules can be found in:
Michael Hahsler and Kurt Hornik. Building on the arules infrastructure
for analyzing transaction data with R. In R. Decker and H.-J. Lenz,
editors, /Advances in Data Analysis, Proceedings of
Hello.
I have a general question regarding to clustering of association rules.
According to http://cran.r-project.org/web/packages/arules/vignettes/arules.pdf
4.7 Distance based clustering transactions and associations there is
possibility for creating clusters of association rules.
I do not
David Winsemius wrote:
On Oct 29, 2010, at 12:08 PM, David Winsemius wrote:
On Oct 29, 2010, at 11:37 AM, dpender wrote:
Apologies for being vague,
The structure of the output is as follows:
Still no code?
I am using the Clusters function from the evd package
$ cluster1 :
On Oct 30, 2010, at 7:49 AM, dpender wrote:
David Winsemius wrote:
On Oct 29, 2010, at 12:08 PM, David Winsemius wrote:
On Oct 29, 2010, at 11:37 AM, dpender wrote:
Apologies for being vague,
The structure of the output is as follows:
Still no code?
I am using the Clusters
That's helpful but the reason I'm using clusters in evd is that I need to
specify a time condition to ensure independence.
I therefore have an output in the form Cluster[[i]][j-k] where i is the
cluster number and j-k is the range of values above the threshold taking
account of the time
On Oct 29, 2010, at 5:14 AM, dpender wrote:
That's helpful but the reason I'm using clusters in evd is that I
need to
specify a time condition to ensure independence.
I believe this is the first we heard about any particular function or
package.
I therefore have an output
We
Apologies for being vague,
The structure of the output is as follows:
$ cluster1 : Named num [1:131] 3.05 2.71 3.26 2.91 2.88 3.11 3.21 -1 2.97
3.39 ...
..- attr(*, names)= chr [1:131] 6667 6668 6669 6670 ...
With 613 clusters. What I require is abstracting the first and last value
of
On Oct 29, 2010, at 11:37 AM, dpender wrote:
Apologies for being vague,
The structure of the output is as follows:
Still no code?
$ cluster1 : Named num [1:131] 3.05 2.71 3.26 2.91 2.88 3.11 3.21
-1 2.97
3.39 ...
..- attr(*, names)= chr [1:131] 6667 6668 6669 6670 ...
With 613
On Oct 29, 2010, at 12:08 PM, David Winsemius wrote:
On Oct 29, 2010, at 11:37 AM, dpender wrote:
Apologies for being vague,
The structure of the output is as follows:
Still no code?
$ cluster1 : Named num [1:131] 3.05 2.71 3.26 2.91 2.88 3.11 3.21
-1 2.97
3.39 ...
..- attr(*,
I am looking to use R in order to determine the number of extreme events for
a high frequency (20 minutes) dataset of wave heights that spans 25 years
(657,432) data points.
I require the number, spacing and duration of the extreme events as an
output.
I have briefly used the clusters function
I have worked with seismic data measured at 100hz, and had no trouble
locating events in long records (several times the size of your
dataset). 20 minutes is high frequency? what kind of waves are
these? what is the wavelength? some details would help.
albyn
On Thu, Oct 28, 2010 at 05:00:10AM
On Oct 28, 2010, at 8:00 AM, dpender wrote:
I am looking to use R in order to determine the number of extreme
events for
a high frequency (20 minutes) dataset of wave heights that spans 25
years
(657,432) data points.
I require the number, spacing and duration of the extreme events as
Hi, just a general question: when we do hierarchical clustering, should we
compute the dissimilarity matrix based on scaled dataset or non-scaled dataset?
daisy() in cluster package allow standardizing the variables before calculating
dissimilarity matrix; but dist() doesn't have that option at
John,
Hi, just a general question: when we do hierarchical clustering, should we
compute the dissimilarity matrix based on scaled dataset or non-scaled dataset?
daisy() in cluster package allow standardizing the variables before calculating
dissimilarity matrix;
I'd say that should
Hello
I've been asked to help evaluate a vegetation data set, specifically to
examine it for community similarity. The initial problem I see is that the
data is ordinal. At best this only captures a relative ranking of
abundance and ordinal ranks are assigned after data collection.I've
Steve -
Take a look at daisy() in the cluster package.
- Phil Spector
Statistical Computing Facility
Department of Statistics
UC
Re: [R] Clustering with ordinal
data
Hello Steve,
I've been asked to help evaluate a vegetation data set, specifically to
examine it for community similarity. The initial problem I see is that the
data is ordinal. At best this only captures a relative ranking of
abundance and ordinal ranks are assigned after data collection.
Dear All
Do you know how to make a heatmap and use cosine correlation for
clustering? This is what my colleague can do in gene-math and I want to
do in R but I don't know how to.
Thanks a lot
Leila
__
R-help@r-project.org mailing list
Hi,
is there a way in R to identify those cluster methods / distance measures
which best reflect predefined cluster groups.
Given 10 observations O1...O10. Optimally, these 10 observations cluster as
follows:
cluster1: O1, O2, O3, O4
cluster2: O5, O6
cluster3: O7, O8, O9, O10.
What I want is a
Hi,
I use the following clustering methods and get the
corresponding dendrograms for single, complete, average, ward and
kmeans clustering.
This gives the dendrograms, but doesn't show the calculation-way.
My question: is there a possibility to show this calculation steps
(cluster steps) in
Hi Ralph,
In case of hclust, the dendrogram does show the steps (they are the
heights presented in the graph).
You can present them also in a matrix using cutree, for example:
dat - (USArrests)
n - (dim(dat)[1])
hc - hclust(dist(USArrests))
cutree(hc, k=1:n)
You might then visualize the
Thank you Etienne, this seems to work like a charm. Also thanks to the rest
of you for your help.
Henrik
On 11 June 2010 13:51, Cuvelier Etienne ecuscim...@gmail.com wrote:
Le 11/06/2010 12:45, Henrik Aldberg a écrit :
I have a directed graph which is represented as a matrix on the form
Henrik,
the methods you use are NOT applicable to directed graphs, in the
contrary even. They will split up what you want to put together. In
your data, an author never cites himself. Hence, A and B are far more
different than B and D according to the techniques you use.
Please check out
Dave,
I used daisy with the default settings (daisy(M) where M is the matrix).
Henrik
On 11 June 2010 21:57, Dave Roberts dvr...@ecology.msu.montana.edu wrote:
Henrik,
The clustering algorithms you refer to (and almost all others) expect
the matrix to be symmetric. They do not seek a
Henrik,
Given your initial matrix, that should tell you which authors are
similar/dissimilar to which other authors in terms of which authors they
cite. In this case authors 1 and 3 are most similar because they both
cite authors 2 and 4. Authors 2 and 3 are most different because they
I have a directed graph which is represented as a matrix on the form
0 4 0 1
6 0 0 0
0 1 0 5
0 0 4 0
Each row correspond to an author (A, B, C, D) and the values says how many
times this author have cited the other authors. Hence the first row says
that author A have cited author B four
Le 11/06/2010 12:45, Henrik Aldberg a écrit :
I have a directed graph which is represented as a matrix on the form
0 4 0 1
6 0 0 0
0 1 0 5
0 0 4 0
Each row correspond to an author (A, B, C, D) and the values says how many
times this author have cited the other authors. Hence the first
Henrik,
The clustering algorithms you refer to (and almost all others)
expect the matrix to be symmetric. They do not seek a graph-theoretic
solution, but rather proximity in geometric or topological space.
How did you convert y9oru matrix to a dissimilarity?
Dave Roberts
Henrik
Hi Ayesha,
hclust is a way to go (much better then trying to invent the wheel here).
Please add what you used to create:
distA
And create a sample data set to show us what you did, using
dput
Best,
Tal
Contact
Details:---
As Tal said.
Next to that, I read that column1 (and column2?) are supposed to be seen as
factors, not as numerical variables. Did you take that into account somehow?
It's easy to reproduce the error code :
n - NULL
if(n2)print(This is OK)
Error in if (n 2) print(This is OK) : argument is of
Thanks Tal Joris!
I created my distance matrix distA by using the dist() function in R
manipulating my output in order to get a matrix.
distA =as.matrix(dist(t(x2))) # x2 being my original dataset
as according to the documentaion on dist()
For the default method, a dist object, or a matrix (of
Hi Ayesha,
I wish to help you, but without a simple self contained example that shows
your issue, I will not be able to help.
Try using the ?dput command to create some simple data, and let us see what
you are doing.
Best,
Tal
Contact
errr, forget about the output of dput(q), but keep it in mind for next time.
f = dist(t(q))
hclust(f,method=single)
it's as simple as that.
Cheers
Joris
On Fri, May 28, 2010 at 10:39 PM, Ayesha Khan
ayesha.diamond...@gmail.comwrote:
v - dput(x,sampledata.txt)
dim(v)
q - v[1:10,1:10]
f
Yes Joris. I did try that and it does produce the results. I am now
wondering why I wanted a matrix like structure in the first place. However,
I do want 'f' to contain values less than 2 only. but when i try to get rid
of values greater than 2 by doing N - (f[f2], f strcuture disrupts and
hclust
v - dput(x,sampledata.txt)
dim(v)
q - v[1:10,1:10]
f =as.matrix(dist(t(q)))
distB=NULL
for(k in 1:(nrow(f)-1)) for( m in (k+1):ncol(f)) {
if(f[k,m] 2) distB=rbind(distB,c(k,m,f[k,m]))
}
#now distB looks like this
distB
[,1] [,2] [,3]
[1,]12 1.6275568
[2,]13
I assume my matrix should look something like this?..
round(distance, 4)
P00A P00B M02A M02B P04A P04B M06A M06B P08A
P08B M10A
P00B 0.9678
M02A 1.0054 1.0349
M02B 1.0258 1.0052 1.2106
P04A 1.0247 0.9928 1.0145 0.9260
P04B 0.9898 0.9769 0.9875 0.9855 0.6075
M06A 1.0159
I can't run your code.
Please, just give me whatever comes on your screen when you run:
dput(q)
On Fri, May 28, 2010 at 10:57 PM, Ayesha Khan
ayesha.diamond...@gmail.comwrote:
I assume my matrix should look something like this?..
round(distance, 4)
P00A P00B M02A M02B P04A
Ah OK, I didn't get your question then.
a dist-object is actually a vector of numbers with a couple of attributes.
You can't just cut out values like that. The hclust function needs a perfect
distance matrix to use the calculations.
shortcut is easy : just do f - f/2*max(f), and all values are
i have a matrix with the following dimensions
136 3
and it looks something like
[,1] [,2] [,3]
[1,] 402 675 1.802758
[2,] 402 696 1.938902
[3,] 402 699 1.994253
[4,] 402 945 1.898619
[5,] 424 470 1.812857
[6,] 424 905 1.816345
[7,] 470 905 1.871252
Hello everyone
I am trying to use CLARA method for finding clusters in my spatial surface
temperature data and noticed one problem. My data are in the form
lat,lon,temperature. I extract lat,lon and cluster number for each point in
the dataset. When I plotted a map of cluster numbers I found
Dear Paco,
as far as I know, there is no such problem with clara, but I may be wrong.
However, in order to help you (though I'm not sure whether I'll be able to
do that), we'd need to understand precisely what you were doing in R and
how your data looks like (code and data; you can show us a
On Wednesday 14 October 2009, Paul Evans wrote:
Hi,
I just wanted to check whether there is a clustering package available for
ordinal data. My data looks something like: #1 #2 #3 #4.
A B C D...
D B C A...
D C A A...
where each column represents a sample, and each row some ordinal
Hi,
I just wanted to check whether there is a clustering package available for
ordinal data. My data looks something like:
#1 #2 #3 #4.
A B C D...
D B C A...
D C A A...
where each column represents a sample, and each row some ordinal values. I
would like to cluster such that similar samples
I checked the R procedure HCLUST (hierarchical clustering) but it
looks like it requires a full triangular n x n similarity matrix as
input, where n = number of observations. The number of variables is
200.
My data set has n = 50,000 observations (keywords), and I use ad-hoc
similarity measures,
I checked the R procedure HCLUST (hierarchical clustering) but it
looks like it requires a full triangular n x n similarity matrix as
input, where n = number of observations. The number of variables is
200.
My data set has n = 50,000 observations (keywords), and I use ad-hoc
similarity measures,
How can I cluster and order within part of a previous clustering result?
For example, I am clustering and ordering results as follows:
rows - 30
cols - 3
x - matrix(sample(-1:1,rows*cols,replace=T), nrow=rows,
ncol=cols,dimnames=list(c(paste(R,1:rows,sep=)),c(paste(C,1:cols,sep=
x
Hi there,
I'm travelling right now so I can't really check this but it seems that
the problem is that cluster.stats needs a partition as input. hclust
doesn't give you a partition but you can generate one from it using
cutree.
BTW, rather use - than =.
Best wishes,
Christian
On Wed, 15
Hello,
I am using the dunn metric, but something is wrong and I dont understand
what or what that this error mean. Please can you help me with this?
The instructions are:
#Indice de Dunn
disbupa=dist(bupa[,1:6])
a=hclust(disbupa)
cluster.stats(disbupa,a,bupa[,7])$dunn
And the error is:
I don't have any experience with your particular problem, but the thing I
notice is that mahalanobis is that by default you specify a covariance
matrix, and it uses solve to calculate its inverse. If you could supply the
inverse covariance matrix (and specify inverted=TRUE to mahalanobis), that
Dear R ExpeRts,
I'm having memory difficulties using mahalanobis distance to trying to cluster
in R. I was wondering if anyone has done it with a matrix of 6525x17 (or
something similar to that size). I have a matrix of 6525 genes and 17 samples.
I have my R memory increased to the max and
I am new to R and have written a function that clusters on subsets of a big
data data set with 60,000 points. I am not sure why, but I keep getting a
run-time error. Any suggestions would be greatly appreciated.
Here is the code:
library(cba)
d-read.csv(data.csv, header=TRUE)
It would help a lot if you told us what the error message was, and provided
some data to work with. As it is, we can't even run the function to find
out what goes wrong.
And also, OS, version of R - all that stuff that the posting guide requests.
Sarah
On Sat, Nov 8, 2008 at 10:31 AM, Bryan
Hi all.
I have alrge microarray dat set that i would like to analyze using
hierarchical clustering. The problem is when i use the command below,
hc- hclust(dist(array), ave)
i get get this feedback...
Error in as.vector(x, mode) :
cannot coerce type 'closure' to vector of type 'any'
Can some
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