CNPS (version 1.0.0)

corr_test: Correlation test

Description

Test the correlation coefficient of the sample.

Usage

corr_test(x, y, alternative = "greater", measure = "pearson",
method_p = "sampling", samplenum = 1000, conf.level.sample = 0.95)

Arguments

x

numeric vectors of data values and should have the same length

y

numeric vectors of data values and should have the same length

alternative

a character string specifying the alternative hypothesis, must be one of "two.sided", "greater"(default) or "less"

measure

the way to measure the correlation coefficient and must be one of "pearson", "spearman" or "kendall"

method_p

a string indicating what method to use for p-value. "sampling" represents sampling; "asymptotic" represents using large sample approximations

samplenum

the number of SRS samples

conf.level.sample

p-value confidence level for SRS sampling

Value

A list with following components

method

the test uesd

score

the score which is used

stat

the statistic of the data under the given scoring system

conf.int

the confidence interval for p-value(only if method_p = "sampling")

pval

p-value for the test

null.value

a character string describing the alternative hypothesis

Details

All procedures and methods of the correlation coefficient test based on the Spearman Correlation Coefficient are the same as for the Pearson Correlation Coefficient. But pay attention to that the correlation coefficient test based on Kendall Correlation Coefficient is a little different from the above two due to its definition.

References

Higgins, J. J. (2004). An introduction to modern nonparametric statistics. Pacific Grove, CA: Brooks/Cole.

Examples

# NOT RUN {
x=c(68,70,71,72)
y=c(153,155,140,180)
corr_test(x , y , measure = "kendall" , method = "asymptotic")
corr_test(x , y , measure = "kendall" , method = "sampling")
# }