如何获得R中单尾自举皮尔逊相关的置信区间？

我想计算R中单尾非参数自举皮尔逊相关检验的95%自举置信区间。然而，boot.ci只给出双尾ci。如何计算单尾引导CI

下面是我使用cor.test进行单尾自举皮尔逊相关测试的代码。它在末尾包括boot.ci，它返回双尾ci，而不是所需的单尾ci。输出作为注释包含以供比较

# Load boot package
library(boot)

# Make the results reproducible
set.seed(7612)

# Define bootstrapped Pearson correlation function and combine output into vector
bootCorTest <- function(data, i){
    d <- data[i, ]
    results <- cor.test(d$x, d$y, method = "pearson", alternative = "greater")
    c(est = results$estimate, stat = results$statistic, param = results$parameter, p.value = results$p.value, CI = results$conf.int)
}

# Define data frame (from first dataset in help("cor.test"))
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)
dat <- data.frame(x, y)

# Perform bootstrapped correlation, 1000 bootstrap replicates
b <- boot(dat, bootCorTest, R = 1000)

#Bootstrap Statistics
#        original     bias    std. error
# t1*  0.57118156 0.05237613  0.21511138
# t2*  1.84108264 1.04457361  3.14416940
# t3*  7.00000000 0.00000000  0.00000000
# t4*  0.05408653 0.01322028  0.09289083
# t5* -0.02223023 0.15123095  0.36338698
# t6*  1.00000000 0.00000000  0.00000000
b

# Original (non-bootstrap) statistics with labels
#    est.cor      stat.t    param.df     p.value         CI1         CI2 
# 0.57118156  1.84108264  7.00000000  0.05408653 -0.02223023  1.00000000
b$t0


# Two-tailed 95% Confidence intervals
# Level      Normal              Basic             
# 95%   ( 0.0972,  0.9404 )   ( 0.1867,  0.9321 ) 
# Level     Percentile            BCa          
# 95%   ( 0.2103,  0.9557 )   (-0.1535,  0.9209 ) 
boot.ci(b, type = c("norm", "basic", "perc", "bca"))

编辑：

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概述使用基本、学生化、百分位、偏差校正、偏差校正加速、测试反演和学生化测试反演方法计算单侧95%自举置信限的算法，但我不知道如何在R中应用这些参数进行相关性测试。

我意识到，通过在boot.cib中指定双尾90%置信区间，conf=0.90，并指出正相关性的期望下限或负相关性的期望上限，可以获得单尾95%自举置信下限或上限。另一个界限是负单尾相关的-1，或正单尾相关的1