Thank you all for the answers. I really need to learn a lot.
Bu I also discover cor2m(x, y, trim = TRUE, alpha = 0.05)
in ecodist package, which gives an output file with significant correlations.
Özgül
Yj>HI,
>
>#ag data was created
>bg<- as.matrix(read.table(text="
> Otu00022 Otu00029 Otu00039 Otu00042 Otu00101 Otu00105 Otu00125 Otu00131
>Otu00137 Otu00155 Otu00158 Otu00172 Otu00181 Otu00185 Otu00190 Otu00209
>Otu00218
>Gi20Jun11 0.001217 0 0.001217 0 0.000000 0 0
> 0 0.001217 0 0 0 0 0 0.001217 0
>0.001217
>Gi40Jun11 0.000000 0 0.000000 0 0.000000 0 0
> 0 0.000000 0 0 0 0 0 0.000000 0
>0.000000
>Gi425Jun11 0.000000 0 0.000000 0 0.000000 0 0
> 0 0.000000 0 0 0 0 0 0.000000 0
>0.000000
>Gi45Jun11 0.000000 0 0.000000 0 0.001513 0 0
> 0 0.000000 0 0 0 0 0 0.000000 0
>0.000000
>Gi475Jun11 0.000000 0 0.000000 0 0.000000 0 0
> 0 0.000000 0 0 0 0 0 0.000000 0
>0.000000
>Gi50Jun11 0.000000 0 0.000000 0 0.000000 0 0
> 0 0.000000 0 0 0 0 0 0.000000 0
>0.000000
>",sep="",header=TRUE,stringsAsFactors=F))
>set.seed(128)
>ag<- matrix(rnorm(30),nrow=6)
>colnames(ag)<- paste("ag",1:5,sep="")
>bg_ag<-expand.grid(colnames(bg),colnames(ag),stringsAsFactors=FALSE)
> attr(bg_ag,"out.attrs")<- NULL
> library(Hmisc)
>#correlation
>resr<-do.call(rbind,lapply(split(bg_ag,1:nrow(bg_ag)),function(x)
>{res<-rcorr(cbind(bg[,x[,1]],ag[,x[,2]]),type="spearman")$r; row.names(res)<-
>rep(paste(x[1],x[2],sep="_"),2);res}))
> head(resr)
># [,1] [,2]
>#Otu00022_ag1 1.0000000 0.1309307
>#Otu00022_ag1 0.1309307 1.0000000
>#Otu00029_ag1 1.0000000 NaN
>#Otu00029_ag1 NaN 1.0000000
>#Otu00039_ag1 1.0000000 0.1309307
>#Otu00039_ag1 0.1309307 1.0000000
>
>#p-values
>resP<-do.call(rbind,lapply(split(bg_ag,1:nrow(bg_ag)),function(x)
>{res<-rcorr(cbind(bg[,x[,1]],ag[,x[,2]]),type="spearman")$P; row.names(res)<-
>rep(paste(x[1],x[2],sep="_"),2);res}))
> head(resP)
># [,1] [,2]
>#Otu00022_ag1 NA 0.8047262
>#Otu00022_ag1 0.8047262 NA
>#Otu00029_ag1 NA NaN
>#Otu00029_ag1 NaN NA
>#Otu00039_ag1 NA 0.8047262
>#Otu00039_ag1 0.8047262 NA
>
>#If you need only the values
>indx<-row(resr)%%2!=1
> resPnew<-as.matrix(resP[indx[,1],1])
> resrnew<-as.matrix(resr[indx[,1],1])
>
>head(resPnew)
># [,1]
>#Otu00022_ag1 0.8047262
>#Otu00029_ag1 NaN
>#Otu00039_ag1 0.8047262
>#Otu00042_ag1 NaN
>#Otu00101_ag1 0.1583024
>#Otu00105_ag1 NaN
>
>head(resrnew)
># [,1]
>#Otu00022_ag1 0.1309307
>#Otu00029_ag1 NaN
>#Otu00039_ag1 0.1309307
>#Otu00042_ag1 NaN
>#Otu00101_ag1 -0.6546537
>#Otu00105_ag1 NaN
>
>A.K.
>
>
>
>
>----- Original Message -----
>From: Ozgul Inceoglu <ozgul.inceo...@ulb.ac.be>
>To: r-help@r-project.org
>Cc:
>Sent: Wednesday, February 13, 2013 4:48 AM
>Subject: [R] spearman correlation and p-value as a matrix
>
>I have two data matrices that I want to make the correlation between each
>column from data1 and each column from data 2 and also calculate the p-value
>Matrices dont have the same size and I tried such a script.
>> bg <- read.table (file.choose(), header=T, row.names)
>> bg
>> Otu00022 Otu00029 Otu00039 Otu00042 Otu00101 Otu00105 Otu00125 Otu00131
>> Otu00137 Otu00155 Otu00158 Otu00172 Otu00181 Otu00185 Otu00190 Otu00209
>> Otu00218
>> Gi20Jun11 0.001217 0 0.001217 0 0.000000 0 0 0 0.001217 0 0 0 0 0 0.001217 0
>> 0.001217
>> Gi40Jun11 0.000000 0 0.000000 0 0.000000 0 0 0 0.000000 0 0 0 0 0 0.000000 0
>> 0.000000
>> Gi425Jun11 0.000000 0 0.000000 0 0.000000 0 0 0 0.000000 0 0 0 0 0 0.000000
>> 0 0.000000
>> Gi45Jun11 0.000000 0 0.000000 0 0.001513 0 0 0 0.000000 0 0 0 0 0 0.000000 0
>> 0.000000
>> Gi475Jun11 0.000000 0 0.000000 0 0.000000 0 0 0 0.000000 0 0 0 0 0 0.000000
>> 0 0.000000
>> Gi50Jun11 0.000000 0 0.000000 0 0.000000 0 0 0 0.000000 0 0 0 0 0 0.000000 0
>> 0.000000
>ag <- read.table (file.choose(), header=T, row.names)
>
>for (i in 1:(ncol(bg)))
>for (j in 1:(ncol(ag)))
>print(c(i,j))
>final_matrix <- matrix(rep("0",ncol(bg)*ncol(ag)),ncol=ncol(bg),nrow=ncol(ag))
>
>cor <- cor.test(as.vector(as.matrix(bg[,i])),as.vector(as.matrix(ag[,j])),
>method="spearman")
>
>#but the output is not matrice with all the values but a single correlation
>value
>
>data: bg[, i] and ag[, j]
>t = 2.2992, df = 26, p-value = 0.02978
>alternative hypothesis: true correlation is not equal to 0
>95 percent confidence interval:
>0.04485289 0.67986803
>sample estimates:
> cor
>0.4110515
>
># How I can creat an outfile with all the correlations and p-values?
>
>Thank you very much!
>Özgül
>
>______________________________________________
>R-help@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.
>
>
>
Özgül Inceoglu, PhD.
Université Libre de Bruxelles
Ecology of Aquatic Systems
Campus de la plaine CP221
Boulevard du triomphe
1050 Bruxelles Belgium
______________________________________________
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and provide commented, minimal, self-contained, reproducible code.
