Hi Jorge,
That was great! Thank you very much for the suggestion.
I just had to make a couple of minor changes (e.g., pearson vs. spearman)
and it worked perfectly.
Thanks,
Joao Pedro
Hi,
I'm sure it could be better but try this:
# F statistics based on lm
FSTAT=function(y,x) summary(lm(y~x))$f[1]
# Correlation and p-value
CORR=function(y,x){
tc=cor.test(x,y,method="spearman",alternative="two.sided")
temp=matrix(c(tc$estimate,tc$p.value),ncol=2)
colnames(temp)=c('rho','pvalue')
temp
}
# 1000 variables and 100 samples
set.seed(124)
X=matrix(rnorm(1000*100),ncol=100)
# Correlation coefficient, p-value and F statistics
corr=t(apply(X[-1,],1,CORR,x=X[1,])) # Your reference is X[1,]
fs=apply(X[-1,],1,FSTAT,x=X[1,]) # Your reference is X[1,]
# Report
temp=data.frame(fstats=fs,rho=corr[,1],pvalue=corr[,2])
rownames(temp)=paste("X",2:nrow(X),sep="")
dim(temp)
[1] 999 3
temp[1:10,]
fstats rho pvalue
X2 1.421307790 -0.05038104 0.61807912
X3 0.051423768 -0.04614461 0.64795111
X4 0.128000634 0.01795380 0.85902211
X5 0.990235820 -0.06540654 0.51730942
X6 5.569006085 0.24232823 0.01532172
X7 0.001862766 -0.01436544 0.88703532
X8 1.025363077 -0.10628263 0.29206908
X9 0.679794149 0.06509451 0.51927479
X10 1.296034903 0.09492949 0.34686211
X11 0.126636867 0.05137714 0.61110106
HTH,
Jorge
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