Stephen and Jorge,
Perhaps a simpler solution is to use the which function
test.data <- Harman74.cor$cov #a test data set
td <- test.data * lower.tri(test.data) #this will examine only the
lower off diagonal elements
td.1 <- which(abs(td)>.6,arr.ind=TRUE) # the critical pairs
td.2 <- td[which(abs(td)>.6)] # the values
td.row <- colnames(test.data)[td.1[,1]] #get the row names
td.col <- colnames(test.data)[td.1[,2]] #and the column names
td.df <- data.frame(td.row,td.col,correl = td.2) #put it all together
Bill
At 12:57 PM -0400 9/3/08, Jorge Ivan Velez wrote:
Dear Stephen,
Perhaps
this<http://www.nabble.com/Re:-applying-cor.test-to-a-(m,-n)-matrix---SUMMARY-to17150239.html#a17150239>post
could helps. In general:
# Function
correl.stats=function(X, method = "pearson", use = "complete" , conf.level =
0.95){
require(forward)
combs=t(fwd.combn(colnames(X), 2))
temp=t(apply(combs,1, function(x){
Y=X[,as.character(x)]
res=cor.test(Y[,1],Y[,2], use = use, method = method, conf.level =
conf.level)
temp2=c(res$estimate, res$statistic, res$p.value, res$conf.int[1:2])
names(temp2)=c('rho','statistic','pvalue','lower','upper')
rm(res)
temp2
}
)
)
rownames(temp)=paste(combs[,1],combs[,2],sep="")
temp
}
# Data set
set.seed(123)
m <- matrix(rnorm(10*5), ncol=5); colnames(m)=paste("m",1:ncol(m),sep="")
# Correlations
res=correl.stats(m)
res
rho statistic pvalue lower upper
m1m2 0.57761512 2.00137666 0.08034470 -0.08173768 0.88528096
m1m3 -0.40595930 -1.25641472 0.24441138 -0.82477175 0.30046722
m1m4 0.67301956 2.57371995 0.03293644 0.07530295 0.91493950
m1m5 -0.34863673 -1.05210481 0.32348990 -0.80217657 0.36001727
m2m3 -0.56734869 -1.94869211 0.08716815 -0.88193296 0.09688755
m2m4 0.27131880 0.79731302 0.44828479 -0.43212763 0.76949303
m2m5 -0.25201740 -0.73658790 0.48241166 -0.76090564 0.44882739
m3m4 -0.43726491 -1.37521060 0.20634394 -0.83657173 0.26544083
m3m5 0.02265933 0.06410673 0.95045815 -0.61575185 0.64311043
m4m5 0.07453706 0.21141075 0.83785303 -0.58242262 0.67259801
# Filtering
res[res[,'rho']>0.6,]
rho statistic pvalue lower upper
0.67301956 2.57371995 0.03293644 0.07530295 0.91493950
HTH,
Jorge
On Wed, Sep 3, 2008 at 11:04 AM, stephen sefick <[EMAIL PROTECTED]> wrote:
I have one hundred and six independent variable that I would like to
preform a correlation analysis on. Is there anyway to only get the
values that are abolute value 0.6 or greater.
thanks
--
Stephen Sefick
Research Scientist
Southeastern Natural Sciences Academy
Let's not spend our time and resources thinking about things that are
so little or so large that all they really do for us is puff us up and
make us feel like gods. We are mammals, and have not exhausted the
annoying little problems of being mammals.
-K. Mullis
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
--
William Revelle http://personality-project.org/revelle.html
Professor http://personality-project.org/personality.html
Department of Psychology http://www.wcas.northwestern.edu/psych/
Northwestern University http://www.northwestern.edu/
Attend ISSID/ARP:2009 http://issid.org/issid.2009/
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