compare.2.vectors {afex}R Documentation

Compare two vectors using various tests.


Compares two vectors x and y using t-test, Welch-test (also known as Satterthwaite), Wilcoxon-test, and a permutation test implemented in coin.


compare.2.vectors(x, y, paired = FALSE, na.rm = FALSE, 
     tests = c("parametric", "nonparametric"), coin = TRUE, 
     alternative = "two.sided", 
     wilcox.exact = NULL, wilcox.correct = TRUE)



a (non-empty) numeric vector of data values.


a (non-empty) numeric vector of data values.


a logical whether the data is paired. Default is FALSE.


logical. Should NA be removed? Default is FALSE.


Which tests to report, parametric or nonparamteric? The default c("parametric", "nonparametric") reports both. See details. (Arguments may be abbreviated).


logical or character. Should (permutation) tests from the coin package be reported? Default is TRUE corresponding to all implemented tests. FALSE calculates no tests from coin. A character vector may include any of the following (potentially abbreviated) implemented tests (see also Details): c("permutation", "Wilcoxon", "median")


a character, the alternative hypothesis must be one of "two.sided" (default), "greater" or "less". You can specify just the initial letter, will be passed to all functions.


distribution argument to coin, see NullDistribution or , IndependenceTest. If missing, defaults to coin::approximate(100000) indicating an approximation of the excat conditional distribution with 100.000 Monte Carlo samples. One can use "exact" for small samples and if paired = FALSE.


exact argument to wilcox.test.


correct argument to wilcox.test.


The parametric tests (currently) only contain the t-test and Welch/Statterwaithe/Smith/unequal variance t-test implemented in t.test. The latter one is only displayed if paired = FALSE.

The nonparametric tests (currently) contain the Wilcoxon test implemented in wilcox.test (stats::Wilcoxon) and (if coin = TRUE) the following tests implemented in coin:

Note that the two implementations of the Wilcoxon test probably differ. This is due to differences in the calculation of the Null distributions.


a list with up to two elements (i.e., paramteric and/or nonparamteric) each containing a data.frame with the following columns: test, test.statistic, test.value, test.df, p.


with(sleep, compare.2.vectors(extra[group == 1], extra[group == 2]))

# gives:
## $parametric
##    test test.statistic test.value test.df       p
## 1     t              t     -1.861   18.00 0.07919
## 2 Welch              t     -1.861   17.78 0.07939
## $nonparametric
##              test test.statistic test.value test.df       p
## 1 stats::Wilcoxon              W     25.500      NA 0.06933
## 2     permutation              Z     -1.751      NA 0.08154
## 3  coin::Wilcoxon              Z     -1.854      NA 0.06487
## 4          median              Z     -1.744      NA 0.17867

# compare with:
with(sleep, compare.2.vectors(extra[group == 1], extra[group == 2], 
                              alternative = "less"))

with(sleep, compare.2.vectors(extra[group == 1], extra[group == 2], 
                              alternative = "greater"))

# doesn't make much sense as the data is not paired, but whatever:
with(sleep, compare.2.vectors(extra[group == 1], extra[group == 2], 
                              paired = TRUE))

# from ?t.test:
compare.2.vectors(1:10,y=c(7:20, 200))

[Package afex version 1.3-0 Index]