Dear John and the rest,
Finally, it seems that now I have found a solution for the problem:
options(width=200) #make window size bigger
#create a test dataset (which is a correlation matrix)
#with row and col names
#extract from this matrix only those correlations
#that fulfill a specific criterion (e.g. r>.6):
m=matrix(sample(1:100,replace=T),10,10)
dimnames(m)=list(letters[1:10],letters[11:20]) #define the matrix
w=cor(m,use="pairwise.complete.obs") #calculate correlations
dimnames(w)[[1]] <- letters[1:10] # (redundant)
ww <- which(w>0.6 & w!=1,arr.ind=TRUE) #extract row and col numbers
z <- w[w>.6 & w!=1] #extract the values of w for which cor>.6
cb=cbind(ww,z)
df=data.frame(cbind(unlist(cb), #this creates the desired data frame
rownames(m)[ww[,1]],
colnames(m)[ww[,2]]
))
names(df)=c("rownum","colnum","cor","rowname","colname")
w
df
##see the output:
w[,7:10] #I truncated some of the first columns
q r s t
a -0.309715184 -0.09883224 0.45408912 -0.07051100
b 0.404094514 0.34563176 -0.12843155 0.35146457
c 0.002622620 -0.05056305 0.41835887 0.17600379
d 0.209332532 0.41291792 -0.34179279 -0.13641805
e 0.181234863 0.05597689 -0.54389838 0.01532882
f -0.058110523 -0.31290094 -0.04454678 -0.15160867
g 1.000000000 0.32582721 -0.64595319 0.70347824
h 0.325827210 1.00000000 -0.29604489 -0.09964339
i -0.645953195 -0.29604489 1.00000000 -0.31197856
j 0.703478239 -0.09964339 -0.31197856 1.00000000
> df
rownum colnum cor rowname colname
j 10 7 0.703478238573416 j q
g 7 10 0.703478238573416 g t
Thanks to all who assisted in the solution of this problem!
All the best
Christoph
John Kane schrieb:
> I thought of that after I went home last night. So
> using Rolf's suggest, will this do what you want?
>
> Again using my simplified matrix
>
> m=matrix(sample(1:20,replace=T),4,5)
> w=data.frame(cor(m,use="pairwise.complete.obs")) ;w
> ww <- which(w>0.6 & w!=1,arr.ind=TRUE); ww
> z <- w[w>.6 & w!=1]
>
> cbind(ww,z)
>
> Exactly how you eliminate duplicate correlations is
> another question.
>
>
> --- Christoph Scherber
> <[EMAIL PROTECTED]> wrote:
>
>> Dear John,
>>
>> Thanks very much for your help; but actually I would
>> like to have the
>> colNames and rowNames for the correlations
>>
>> -such that I can say: Only (a and c ) and (d and f)
>> were correlated with
>> r>0.6:
>>
>> m=matrix(sample(1:100,replace=T),10,10)
>> dimnames(m)=list(letters[1:10],letters[11:20])
>> w=cor(m,use="pairwise.complete.obs")
>> w*(w>0.6)
>>
>> #works, but how do I get rid of those rows or
>> columns
>> #for which the colSum (or rowSum) is 1?
>>
>> Thanks very much in advance for your help!
>>
>> Best wishes
>> Christoph
>>
>>
>>
>>
>>
>> John Kane schrieb:
>>> w[w>.6] seems to work for me. I cut down the size
>> of
>>> the matrix for easier visual inspection.
>>>
>>> m=matrix(sample(1:20,replace=T),4,5)
>>> w=cor(m,use="pairwise.complete.obs")
>>> w
>>>
>>> w[w>.6]
>>>
>>> perhaps perferabely
>>> w[w>0.6 & w!=1]
>>>
>>>
>>> --- Christoph Scherber
>>> <[EMAIL PROTECTED]> wrote:
>>>
>>>> Dear R users,
>>>>
>>>> suppose I have a matrix of observations for which
>> I
>>>> calculate all
>>>> pair-wise correlations:
>>>>
>>>> m=matrix(sample(1:100,replace=T),10,10)
>>>> w=cor(m,use="pairwise.complete.obs")
>>>>
>>>> How do I extract only those correlations that are
>>>>> 0.6?
>>>> w[w>0.6] #obviously doesn´t work,
>>>>
>>>> and I can´t find a way around it.
>>>>
>>>> I would very much appreciate any help!
>>>>
>>>> Best wishes
>>>> Christoph
>>>>
>>>>
>>>> (using R 2.5.1 on Windows XP)
>>>>
>>>>
>>>>
>>>>
>>>> --
>>>> Dr. Christoph Scherber
>>>> DNPW, Agroecology
>>>> University of Goettingen
>>>> Waldweg 26
>>>> D-37073 Goettingen
>>>> Germany
>>>>
>>>> phone +49(0)551 39 8807
>>>> fax +49(0)551 39 8806
>>>> homepage www.gwdg.de/~cscherb1
>>>>
>>>> ______________________________________________
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>>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>>> PLEASE do read the posting guide
>>>> http://www.R-project.org/posting-guide.html
>>>> and provide commented, minimal, self-contained,
>>>> reproducible code.
>>>>
>>>
>>>
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