many correlations
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 <ssefick at gmail.com> 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
______________________________________________ R-help at r-project.org mailing list 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.
[[alternative HTML version deleted]]
______________________________________________ R-help at r-project.org mailing list 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.
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/