You have multiple options. I will advertise my own solution - install
the package WGCNA, installation instructions at
http://labs.genetics.ucla.edu/horvath/CoexpressionNetwork/Rpackages/WGCNA/#cranInstall
then you can use the function
cp = corAndPvalue(t(genes), t(features)).
You need to transpo
The way the sample data is provided is not useful. I have re-built your data,
please find the dput() version below (and pls check whether I got it right...).
This is not my area of competence at all, but from what I see from the help
page is that the expected parameters are, among others:
x
Even reducing the command to less variables than observations I still get:
set.cor(y = (66:76), x = c(1:6), z = 65, data = dat5)
Error in solve.default(x.matrix, xy.matrix) :
Lapack routine dgesv: system is exactly singular: U[2,2] = 0
On Mon, Sep 1, 2014 at 12:25 PM, David L Carlson wrote:
1.
col 1 is perfectly correlated with col 2
col 3 is perfectly correlated with col 4
col 5 is perfectly correlated with col 6
David C
From: Patzelt, Edward [mailto:patz...@g.harvard.edu]
Sent: Tuesday, September 2, 2014 9:21 AM
To: David L Carlson
Cc: R-help@r-project.org
Subject: Re:
Thanks for including your data with dput(). I'm not familiar with set
correlation, but altogether you are working with 76 variables (columns) and
only 46 observations. Since the error message says "the system is exactly
singlular," it is likely that you have too many variables for the number of
Hello,
Instead of 'sum' use 'mean'
ok <- apply(tbl, 2, function(x) mean(!is.na(x)) >= 0.5)
cor(tbl[, ok], use="pairwise.complete.obs")
Hope this helps,
Rui Barradas
Em 29-05-2012 10:03, jeff6868 escreveu:
Hi everybody.
I'm trying to do a correlation matrix in a list of files. Each file cont
thanks a lot dear. I will keep your advice in my mind.
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Josh:
A very nice, clear, polite, concise, and reasoned alternative to "RTFM" !
Probably should be templated somehow, given the volume of queries of
this sort that this list receives. (The posting guide is too involved
to serve in its stead.)
Cheers,
Bert
On Thu, May 17, 2012 at 9:31 AM, Josh
Hi Mahdi,
Look at the documentation for cor(), by typing ?cor or help("cor").
Pearson is the default and it is trivial to select the others. I
suggest you try searching google or reading R's documentation before
posting to the list. You may not understand it all, but it shows you
tried to work o
Thanks a lot. Suppose I want to use Pearson's method, then what I have to do?
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Hi,
unless you're dealing with heteroskedastic datas, the command *cor(x)* will
be enough, where *x* is your data matrix; in this function you can easily
select the method which has to be used: Pearson's, Kendall's or Spearman's
correlation.
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I improved yesterday a bit your script (mostly according to station numbers
for the automatization). Here's the final version. thanks again!
filenames <- list.files(pattern="\\_2008_reconstruit.csv$")
Sensors <- paste("capteur_", 1:4, sep="")
Stations <-substr(filenames,1,5)
nsensors <- length
Hello Rui,
Thanks a lot for your answer.
Hou hoped that your script would help me?
I answer you: It is WON-DER-FUL!
It works very well! I had first some difficulties to adapt it to my data,
but I succeeded afterwords when I made a test between 2 stations.
It's not perfect yet (I still have to mo
Hello,
jeff6868 wrote
>
> Dear users,
>
> I'm quite a new french R-user, and I have a problem about doing a
> correlation matrix.
> I have temperature data for each weather station of my study area and for
> each year (for example, a data file for the weather station N°1 for the
> year 2009, a
Looking over the code below, I think this patched version might return
a better answer:
spec.cor <- function(dat, r, ...) {
x <- cor(dat, ...)
x[upper.tri(x, TRUE)] <- NA
i <- which(abs(x) >= r, arr.ind = TRUE)
data.frame(V1 = rownames(x)[i[,1]], V2 = colnames(x)[i[,2]], Value = x[
There have been two threads dealing with this in the last few weeks:
please search the recent archives for those threads for a good
discussion -- end result: Josh Wiley provided a useful little function
to do so that I'll copy below. RSeek.org is a good place to do your
searching.
spec.cor <- func
I think it would be better to think of this as an estimation problem rather
than a selection problem. If the correlation matrix is of interest,
estimate the entire matrix. If you want to show that you can make decisions
on the basis of the matrix, then use the bootstrap to get a confidence
interv
Alexandre,
The output from corr.test is a list of matrices. To export one of those
matrices, simply specify which one you want:
Using the example from my previous note:
> library(psych)
> examp <- corr.test(sat.act)
> mat.c.p <- lower.tri(examp$r)*examp$r + t(lower.tri(examp$p)*examp$p)
> mat
Hi,
one solution is to use sink. Check ?sink to see explanation and following
example.
sink("sink-examp.txt")
i <- 1:10
outer(i, i, "*")
sink()
Andrija
On Tue, Nov 1, 2011 at 10:43 AM, AlexC wrote:
> Hello,
>
> Thank you for your replies. I cannot run the function rcor.test even when
> having
Hello,
Thank you for your replies. I cannot run the function rcor.test even when
having loaded package ltm. Perhaps it has to do with the fact that I am
using the latest version of R and this package wasn't created under that
version
The function corr.test in package psych works fine. Is ther
Hi,
rcor.test in library(ltm) will provide a correlation matrix with p-values on
the bottom-half of the matrix.
Mark
On 2011-10-26, at 7:03 AM, AlexC wrote:
> Thank you for your quick reply and helpful advice.
>
> Using this argument allows me to do what I needed to do
>
> Now the only othe
Alex,
corr.test in psych will give you a matrix of correlations, a matrix of sample
sizes, and a matrix of probabilities.
You can combine the correlations and the probabilities to form what you want:
try the following:
> library(psych)
> examp <- corr.test(sat.act)
> mat.c.p <- lower.tri(exa
Thank you for your quick reply and helpful advice.
Using this argument allows me to do what I needed to do
Now the only other thing I wanted to accomplish was to obtain the top half
of the matrix with p values
and the bottom half with the correlations, to observe the significant
correlations. I
I believe it has to do with the "complete.obs" choice and the presence
of NAs in your data. The differences should vanish with
"pairwise.complete.obs" but whether that's what you want is up to you.
Michael
On Tue, Oct 25, 2011 at 5:09 PM, AlexC wrote:
> Hi,
>
> I am currently working with a dat
Thank you all for your suggestions.
Sharad
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https://st
okay so fixed what i need to do this way
finit=0
for(ri in 1:dim(xa)[1])
{
finit=finit+1
xc[ri,1:finit]<-xa[ri,1:finit]
xc[1:finit,ri]<-xb[1:finit,ri]
}
but getting error in heatmap.2
> mycol <- colorpanel(n=40,low="red",mid="white",high="blue")
> heatmap.2(xc, breaks=pairs.breaks, col=mycol, Ro
And you might also consider packages like corrplot, corrgram etc. for
other plotting options of a correlation matrix.
They can be more informative than simply invoking image(heat)
> What a pleasant post to respond to - with self-contained code. :)
>
> heat<-matrix(0,nrow=dim(xa)[1],ncol=dim(xa)
Tena koe Sharad
If I understand you correctly, you want the lower triangle of your combined
matrix to be the lower triangle of one of the correlation matrices, and the
upper triangle to be the upper triangle from the other. If so, check
lower.tri() and upper.tri().
HTH
Peter Alspach
>
What a pleasant post to respond to - with self-contained code. :)
heat<-matrix(0,nrow=dim(xa)[1],ncol=dim(xa)[2])
heat[lower.tri(heat)]<-xa[lower.tri(xa)]
heat[upper.tri(heat)]<-xb[upper.tri(xb)]
diag(heat)<-1
heat
HTH,
Daniel
1Rnwb wrote:
>
> Hello Gurus
> I have two correlation matrices 'x
Works for me:
> x <- rnorm(10)
> y <- rnorm(10)
> cor.test(x, y, method = 'spearman')$p.value
[1] 0.166058
What are the classes of your inputs? A reproducible example would be
helpful. From the help page of cor.test():
x, y numeric vectors of data values. x and y must have the same length.
Just pointing out that Jorge wrote
cor.test
not cor.
Don't know if you saw that but it should help.
Michael Weylandt
On Aug 9, 2011, at 8:58 AM, ScottM wrote:
> Cheers Jorge,
>
> I've tried this, but keep getting error messages, relating to either:
>
> Error: unexpected '$' in "$"
>
>
Cheers Jorge,
I've tried this, but keep getting error messages, relating to either:
Error: unexpected '$' in "$"
or
Error in cor(data, method = "spearman")$p.value :
$ operator is invalid for atomic vectors
Very annoying!
S
Scott McGrane MA (Hons), MRes
SAGES Theme 1 PhD Student
Northern R
?cor.test
cor.test(x, y, method = "spearman")$p.value
HTH,
Jorge
On Tue, Aug 9, 2011 at 8:44 AM, ScottM <> wrote:
> Hello all,
>
> I've run a Spearman's Rank test to discern relationships between landscape
> characteristics and a specific aspect of river behaviour.
>
> I've executed a correlati
On Apr 7, 2011, at 3:09 PM, Dmitry Berman wrote:
Listers,
I have a question regarding correlation matrices. It is fairly
straight
forward to build a correlation matrix of an entire data frame. I
simply use
the command cor(MyDataFrame). However, what I would like to do is
construct
a smal
I am sorry for the noise, but
with(MyDataFrame[, 1:3])
should have been
with(cor(MyDataFrame[, 1:3]))
Best,
Jorge
On Thu, Apr 7, 2011 at 3:21 PM, Jorge Ivan Velez <> wrote:
> Hi Dmitry,
>
> You might try
>
> with(MyDataFrame[, 1:3])
>
> is variable1, variable2 and variable3 correspond to th
Hi Dmitry,
You might try
with(MyDataFrame[, 1:3])
is variable1, variable2 and variable3 correspond to the first three columns
of your data, or
with( MyDataFrame( cor( cbind( variable1, variable2, variable3) ) ) )
otherwise.
HTH,
Jorge
On Thu, Apr 7, 2011 at 3:09 PM, Dmitry Berman <> wrote:
On 7 April 2011 12:09, Dmitry Berman wrote:
> Listers,
>
> I have a question regarding correlation matrices. It is fairly straight
> forward to build a correlation matrix of an entire data frame. I simply use
> the command cor(MyDataFrame). However, what I would like to do is construct
> a smaller
Hi ,
you can try using cov2cor(vcov(lm(calorie~height))) to get the correlation
matrix of estimated coefficients
boltonboy999 wrote:
>
> Hi everyone,
>
> This is pretty urgent so if anyone can help that would be great.
>
> I have a table of information. The categories are weight, height
On the topic of visualizing correlation, see also
Murdoch, D.J. and Chow, E.D. (1996). A graphical display of large
correlation matrices.
The American Statistician 50, 178-180.
with examples here:
# install.packages('ellipse')
example(plotcorr, package='ellipse')
On Sat, Mar 8, 2008 at 3:01 A
If your purpose is simply to represent a correlation matrix it in a more
compact way see ?symnum, the corrgram package and an example in the
book Multivariate Data Visualization (regarding which gives a lattice
implementation).
On Fri, Mar 7, 2008 at 2:15 PM, Martin Kaffanke
wrote:
> Thank you, t
On 3/5/08, Martin Kaffanke <[EMAIL PROTECTED]> wrote:
> Now I'd like to have it one sided, means only the left bottom side to be
> printed (the others are the same) and I'd like to have * where the
> p-value is lower than 0.05 and ** lower than 0.01.
Look here [1], at "Visualizing Correlations"
Thank you, thats really good and gives me very good information.
Thanks,
Martin
Am Donnerstag, den 06.03.2008, 14:35 -0500 schrieb Chuck Cleland:
> On 3/6/2008 2:07 PM, Martin Kaffanke wrote:
> > Am Mittwoch, den 05.03.2008, 14:38 -0300 schrieb Henrique Dallazuanna:
> >> Try this:
> >>
> >> On 05
On 3/6/2008 2:07 PM, Martin Kaffanke wrote:
> Am Mittwoch, den 05.03.2008, 14:38 -0300 schrieb Henrique Dallazuanna:
>> Try this:
>>
>> On 05/03/2008, Martin Kaffanke <[EMAIL PROTECTED]> wrote:
>>> Hi there!
>>>
>>> In my case,
>>>
>>> cor(d[1:20])
>>>
>>> makes me a good correlation matrix.
>>>
Please provide a example of what you want
On 06/03/2008, Martin Kaffanke <[EMAIL PROTECTED]> wrote:
>
> Am Mittwoch, den 05.03.2008, 14:38 -0300 schrieb Henrique Dallazuanna:
>
> > Try this:
> >
> > On 05/03/2008, Martin Kaffanke <[EMAIL PROTECTED]> wrote:
> > > Hi there!
> > >
> > > In my
Am Mittwoch, den 05.03.2008, 14:38 -0300 schrieb Henrique Dallazuanna:
> Try this:
>
> On 05/03/2008, Martin Kaffanke <[EMAIL PROTECTED]> wrote:
> > Hi there!
> >
> > In my case,
> >
> > cor(d[1:20])
> >
> > makes me a good correlation matrix.
> >
> > Now I'd like to have it one sided, means
Try this:
On 05/03/2008, Martin Kaffanke <[EMAIL PROTECTED]> wrote:
> Hi there!
>
> In my case,
>
> cor(d[1:20])
>
> makes me a good correlation matrix.
>
> Now I'd like to have it one sided, means only the left bottom side to be
> printed (the others are the same) and I'd like to have * wher
If I understand your question, you can try something like this:
cor(data.frame(lapply(split(x, x$state), "[", 3)))
On 29/01/2008, Serguei Kaniovski <[EMAIL PROTECTED]> wrote:
> Hello,
>
> I cannot figure out how to use "tapply" to compute the correlation matrix
> in the variable "x" between the s
On Jan 8, 2008 12:34 AM, suman Duvvuru <[EMAIL PROTECTED]> wrote:
> Hello,
> I have a dataset with 20,000 variables.and I would like to compute a pearson
> correlation matrix which will be 2*2. The cor() function doesnt work
> in this case due to memory problem. If you have any ideas regar
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