### R wilcoxon rank test

`wilcox.test()`

function performs wilcoxon rank test, which assumes that the means of two unnormally distributed datasets are equal.

wilcox.test(x, ...) wilcox.test(x, y, alternative = c("two.sided", "less", "greater"), mu = 0, paired = FALSE, exact = NULL, correct = TRUE, conf.int=FALSE, conf.level = 0.95, ...)

`x,y`

: Unnormally distributed data sets`ratio`

: Hypothesized ratio of x/y, default is 1`alternative`

: alternative hypothesis, including "two.sided","greater","less"`conf.level`

: confidence level`...`

- c(1,5,9,24,56,21,3,7,21,4) > y <- c(12,15,5,9,9,14,56,22,3,7,32,5) > wilcox.test(x,y)

Wilcoxon rank sum test with continuity correction data: x and y W = 51.5, p-value = 0.5966 alternative hypothesis: true location shift is not equal to 0 Warning message: In wilcox.test.default(x, y) : cannot compute exact p-value with ties

Since the p-value = 0.5966 is much higher than 0.05, the hypothesis that the two means are equal is accepted.

> y <- c(1233,4356,987,39999,1111,200000) > wilcox.test(x,y)

Wilcoxon rank sum test with continuity correction data: x and y W = 0, p-value = 0.001364 alternative hypothesis: true location shift is not equal to 0 Warning message: In wilcox.test.default(x, y) : cannot compute exact p-value with ties

p-value = 0.001363 which is much lower than 0.05, rejects the hypothesis.