ksample_test {CNPS}

Multiple sample permutation test

Description

Test whether there is a difference among k treatments.

Usage

ksample_test(x, group, score = "kruskal", method_p = "sampling", type = "normal",
samplenum = 1000, conf.level.sample = 0.95)

Arguments

x

numeric vector of data values

group

factor that determines the grouping of elements in x

score

a discrete value indicating the type of score. There are "original", "Wilcoxon", "van" and "exp" to be selected

method_p

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

type

"normal" refers to ordinary test, "JT" refers to ordered alternative hypothesis

samplenum

the number of SRS samples

conf.level.sample

p-value confidence level for SRS sampling

Details

Use a permutation sample based on the F-statistic or use a large sample approximation to determine if there is a difference between the populations (treatments). If the sample is ordered, you can also use the JT test. The argument "score" allows you to choose different scoring system to do the test. If you want to use a special scoring system defined by yourself, just transform the data first and then choose score="original" in the function.

Value

A list with following components

method

the test uesd

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

alternative

a character string describing the alternative hypothesis

Author(s)

Jiasheng Zhang, Feng Yu, Yangyang Zhang, Siwei Deng. Tutored by YuKun Liu and Dongdong Xiang.

References

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

Examples

x1=c( 13.0, 24.1, 11.7, 16.3, 15.5, 24.5)
x2=c( 42.0, 18.0, 14.0, 36.0, 11.6, 19.0)
x3=c( 15.6, 23.8, 24.4, 24.0, 21.0, 21.1)
x4=c( 35.3, 22.5, 16.9, 25.0, 23.1, 26.0)
x <- c(x1, x2, x3, x4)
ind=c(rep(1,length(x1)), rep(2, length(x2)), rep(3, length(x3)), rep(4, length(x4)))
group=as.factor(ind)
ksample_test(x , group , type = "JT" , samplenum = 4000)