Nonparametric location tests for one and two samples

Install required packages

coin, DescTools

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

One-sample

Sign-test

Two-sided test

set.seed(123)
medH0 <- 30
DV    <- sample(0:100, 20, replace=TRUE)

library(DescTools)
SignTest(DV, mu=medH0)

    One-sample Sign-Test

data:  DV
S = 15, number of differences = 20, p-value = 0.04139
alternative hypothesis: true median is not equal to 30
95.9 percent confidence interval:
 33 89
sample estimates:
median of the differences 
                       54 

Wilcoxon signed rank test

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

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

    Wilcoxon signed rank test

data:  IQ
V = 48, p-value = 0.01855
alternative hypothesis: true location is greater than 110
95 percent confidence interval:
 113.5   Inf
sample estimates:
(pseudo)median 
           121 

Two independent samples

Sign-test

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]))

Looks at the number of cases in each group which are below or above the median of the combined data.

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

    Exact Median Test

data:  DV by IV (A, B)
Z = 1.1431, p-value = 0.3868
alternative hypothesis: true mu is not equal to 0

Wilcoxon rank-sum test (\(=\) Mann-Whitney \(U\)-test)

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

    Wilcoxon rank sum test

data:  DV by IV
W = 202, p-value = 0.02647
alternative hypothesis: true location shift is less than 0
95 percent confidence interval:
      -Inf -1.770728
sample estimates:
difference in location 
             -9.761436 

library(coin)
wilcox_test(DV ~ IV, alternative="less", conf.int=TRUE,
            distribution="exact", data=wIndDf)

    Exact Wilcoxon Mann-Whitney Rank Sum Test

data:  DV by IV (A, B)
Z = -1.9407, p-value = 0.02647
alternative hypothesis: true mu is less than 0
95 percent confidence interval:
      -Inf -1.770728
sample estimates:
difference in location 
             -9.761436 

Two dependent samples

Sign-test

N      <- 20
DVpre  <- 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")))

Two-sided test

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

library(DescTools)
SignTest(DVdiff$DV, mu=medH0)

    One-sample Sign-Test

data:  DVdiff$DV
S = 13, number of differences = 20, p-value = 0.2632
alternative hypothesis: true median is not equal to 0
95.9 percent confidence interval:
 -1.255058 27.268025
sample estimates:
median of the differences 
                  8.47604 

Wilcoxon signed rank test

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

    Exact Wilcoxon-Signed-Rank Test

data:  y by x (neg, pos) 
     stratified by block
Z = 2.128, p-value = 0.01638
alternative hypothesis: true mu is greater than 0

Detach (automatically) loaded packages (if possible)

try(detach(package:DescTools))
try(detach(package:coin))
try(detach(package:survival))
try(detach(package:splines))

Get the article source from GitHub

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