How to perform two sets of independent sample rank and test in R language

Install the required packages

wants <- c("coin")has   <- wants %in% rownames(installed.packages())if(any(!has)) install.packages(wants[!has])>

A sample test

set.seed(123)medH0 <- 30DV    <- sample(0:100, 20, replace=TRUE)DV    <- DV[DV != medH0]N     <- length(DV)(obs  <- sum(DV > medH0))

[1] 15

(pGreater <- 1-pbinom(obs-1, N, 0.5))

[1] 0.02069

(pTwoSided <- 2 * pGreater)

[1] 0.04139

Wilcoxon Row Inspection

IQ    <- c(99, 131, 118, 112, 128, 136, 120, 107, 134, 122)medH0 <- 110

wilcox.test(IQ, alternative="greater", mu=medH0, conf.int=TRUE)

2. Wilcoxon signed Rank test
4. Data:iq
5. V = , p-value = 0.01855
6. Alternative hypothesis: true location is greater than
7. percent Confidence interval:
8. 113.5 Inf
9. Sample estimates:
10. (pseudo) Median
11. 121

Two independent sample tests

Nj  <- c(20, 30)DVa <- rnorm(Nj[1], mean= 95, sd=15)DVb <- rnorm(Nj[2], mean=100, sd=15)wIndDf <- data.frame(DV=c(DVa, DVb),                     IV=factor(rep(1:2, Nj), labels=LETTERS[1:2]))

View the number of cases in each group that are below or above the median of the combined data.

library(coin)median_test(DV ~ IV, distribution="exact", data=wIndDf)

2. Exact Median Test
4. Data: DV by IV (A, B)
5. Z = 1.143, p-value = 0.3868
6. Alternative hypothesis: true mu isn't equal to 0

Wilcoxon rank and test (Mann-Whitney Quarantine)

wilcox.test(DV ~ IV, alternative="less", conf.int=TRUE, data=wIndDf)

1.   
2.  wilcoxon rank sum test
3.   
4.  data : DV by IV
5.  w = 202, P-value = 0.02647
6.  alternative hypothesis : true location shift is less than 0 /span>
7.  95 percent confidence interval:
8.  -inf -1.771
9.  sample estimates:
10.  difference in location
11.  -9.761

1.  library (coin)
2.  wilcox_test (DV ~ IV, alternative= "less", Conf.int=true,
3.  distribution= "exact", DATA=WINDDF)

2. Exact Wilcoxon Mann-whitney Rank Sum Test
4. Data: DV by IV (A, B)
5. Z = -1.941, p-value = 0.02647
6. Alternative hypothesis: true mu is less than 0
7. percent Confidence interval:
8. -inf -1.771
9. Sample estimates:
10. Difference in location
11. -9.761

Two dependent sample tests

N      <- 20DVpre  <- rnorm(N, mean= 95, sd=15)DVpost <- rnorm(N, mean=100, sd=15)wDepDf <- data.frame(id=factor(rep(1:N, times=2)),                     DV=c(DVpre, DVpost),                     IV=factor(rep(0:1, each=N), labels=c("pre", "post")))

medH0  <- 0DVdiff <- aggregate(DV ~ id, FUN=diff, data=wDepDf)(obs   <- sum(DVdiff$DV < medH0))

[1] 7

(pLess <- pbinom(obs, N, 0.5))

[1] 0.1316

ranking WilcoxonInspection

wilcoxsign_test(DV ~ IV | id, alternative="greater",                distribution="exact", data=wDepDf)

2. Exact Wilcoxon-signed-rank Test
4. Data: y by x (neg, pos)
5. stratified by block
6. Z = 2.128, p-value = 0.01638
7. Alternative hypothesis: true mu is greater than 0

Detach (Auto) load the package

try(detach(package:coin))try(detach(package:modeltools))try(detach(package:survival))try(detach(package:mvtnorm))try(detach(package:splines))try(detach(package:stats4))

How to perform two sets of independent sample rank and test in R language