I tried 2 methods to estimate C.I. of correlation coefficient of variables x and y:
> x <- c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1)
> y <- c( 2.6, 3.1, 2.5, 5.0, 3.6, 4.0, 5.2, 2.8, 3.8)
#METHOD 1: Pearson's
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> cor.test(x, y, method = "pearson", conf.level = 0.95)
Pearson's product-moment correlation
data: x and y
t = 1.8411, df = 7, p-value = 0.1082
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.1497426 0.8955795
sample estimates:
cor
0.5711816
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#METHOD 2: bootstrap
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> boot(cbind(x,y), cor, 200)
ORDINARY NONPARAMETRIC BOOTSTRAP
Call:
boot(data = cbind(x, y), statistic = cor, R = 200)
Bootstrap Statistics :
original bias std. error
t1* 0.5429120 -0.5232893 0.3799117
t2* 0.3832856 -0.3779026 0.3876666
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'cor.test' gave the value of cor as 0.5711816. Why did 'boot' give two original cors:
t1 and t2. And why does none of t1 and t2 equal to what 'cor.test' gave.
Thank you very much
Xiao
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