The "basic" interval (which you are using by default) is just the
usually normal distribution interval with the standard error
estimated from the resampling distribution. It has all the benefits
and problems of assuming a normal distribution.
If you wish to maintain the domain limits on the correlation, use the
"percentile" method, which would use the 2.5% and 97.5% quantiles of
the resampling distribution.
Because you clearly have a non-central correlation (-0.8), you would
get more accurate results with the "BCa" method, which does bias and
skew correction of the resampling distribution.
At 06:22 AM 8/16/2009, Liviu Andronic wrote:
Dear R users,
Does the results below make any sense? Can the the interval of the
correlation coefficient be between *-1.0185* and -0.8265 at 95%
confidence level?
Liviu
> library(boot)
> data(mtcars)
> with(mtcars, cor.test(mpg, wt, met="spearman"))
Spearman's rank correlation rho
data: mpg and wt
S = 10292, p-value = 1.488e-11
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
-0.88642
Warning message:
In cor.test.default(mpg, wt, met = "spearman") :
Cannot compute exact p-values with ties
> corr.fun <- function(data, indices) {
+ data.samp <- data[indices,] # allows boot to select sample
+ tmp.corr <- with(data.samp, cor.test(mpg, wt, met="spearman"))
+ return(tmp.corr$estimate)
+ }
> corr.boot <- boot(mtcars, corr.fun, R=1000)
There were 50 or more warnings (use warnings() to see the first 50)
> corr.boot
ORDINARY NONPARAMETRIC BOOTSTRAP
Call:
boot(data = mtcars, statistic = corr.fun, R = 1000)
Bootstrap Statistics :
original bias std. error
t1* -0.88642 0.012992 0.050042
> boot.ci(corr.boot, type="basic")
BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
Based on 1000 bootstrap replicates
CALL :
boot.ci(boot.out = corr.boot, type = "basic")
Intervals :
Level Basic
95% (-1.0185, -0.8265 )
Calculations and Intervals on Original Scale
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Robert A. LaBudde, PhD, PAS, Dpl. ACAFS e-mail: r...@lcfltd.com
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