ksample_test {CNPS} R Documentation

## 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)


[Package CNPS version 1.0.0 Index]