[R] use logical in cor.test

[pgseye]

Hi,

I've got 4 variables that I want to effectively 'stack' so that I have a
grand R variable and a grand L variable.

This works to achieve that goal:

Twin1cor<-with(twin.wide,cbind(ACDepthR.1,ACDepthL.1))
Twin2cor<-with(twin.wide,cbind(ACDepthR.2,ACDepthL.2))
Both<-rbind(Twin1cor,Twin2cor)

> str(Both)
 num [1:1858, 1:2] 3.36 NA NA NA NA NA NA 3.92 3.5 NA ...
 - attr(*, "dimnames")=List of 2
  ..$ : NULL
  ..$ : chr [1:2] "ACDepthR.1" "ACDepthL.1"


I then want to perform a pearson correlation of the two variables, but
exclude any value greater than 2.5. When I do this without excluding
anything, with:

cor.test(Both[,1],Both[,2])

I get:

	Pearson's product-moment correlation

data:  Both[, 1] and Both[, 2] 
t = 35.848, df = 854, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0 
95 percent confidence interval:
 0.7468716 0.8005199 
sample estimates:
      cor 
0.7750889 

But when I try:

cor.test(Both[,1]>2.5,Both[,2]>2.5)

I get:

	Pearson's product-moment correlation

data:  Both[, 1] > 2.5 and Both[, 2] > 2.5 
t = 13.3192, df = 854, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0 
95 percent confidence interval:
 0.3576600 0.4687099 
sample estimates:
     cor 
0.414728 

I'm not sure why. I know the correlation should improve from the plot by
excluding those under 2.5, but it decreases. Have I done something wrong
here?

Thanks,

Paul
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