wilcoxon: Wilcoxon test

In matrixTests: Fast Statistical Hypothesis Tests on Rows and Columns of Matrices

Wilcoxon test

Description

Performs a Wilcoxon test on each row/column of the input matrix.

Usage

row_wilcoxon_twosample(
  x,
  y,
  null = 0,
  alternative = "two.sided",
  exact = NA,
  correct = TRUE
)

col_wilcoxon_twosample(
  x,
  y,
  null = 0,
  alternative = "two.sided",
  exact = NA,
  correct = TRUE
)

row_wilcoxon_onesample(
  x,
  null = 0,
  alternative = "two.sided",
  exact = NA,
  correct = TRUE
)

col_wilcoxon_onesample(
  x,
  null = 0,
  alternative = "two.sided",
  exact = NA,
  correct = TRUE
)

row_wilcoxon_paired(
  x,
  y,
  null = 0,
  alternative = "two.sided",
  exact = NA,
  correct = TRUE
)

col_wilcoxon_paired(
  x,
  y,
  null = 0,
  alternative = "two.sided",
  exact = NA,
  correct = TRUE
)

Arguments

x	numeric matrix.
y	numeric matrix for the second group of observations.
null	true values of the location shift for the null hypothesis. A single number or numeric vector with values for each observation.
alternative	alternative hypothesis to use for each row/column of x. A single string or a vector with values for each observation. Values must be one of "two.sided" (default), "greater" or "less".
exact	logical or NA (default) indicator whether an exact p-value should be computed (see Details). A single value or a logical vector with values for each observation.
correct	logical indicator whether continuity correction should be applied in the cases where p-values are obtained using normal approximation. A single value or logical vector with values for each observation.

Details

Functions to perform one sample and two sample Wilcoxon tests on rows/columns of matrices. Main arguments and results were intentionally matched to the wilcox.test() function from default stats package. Other arguments were split into separate functions:

row_wilcoxon_onesample(x) - one sample Wilcoxon test on rows. col_wilcoxon_onesample(x) - one sample Wilcoxon test on columns.

Results should be the same as running wilcox.test(x) on every row (or column) of x.

row_wilcoxon_twosample(x, y) - two sample Wilcoxon test on rows. col_wilcoxon_twosample(x, y) - two sample Wilcoxon test on columns.

Results should be the same as running wilcox.test(x, y) on every row (or column) of x and y.

row_wilcoxon_paired(x, y) - two sample paired Wilcoxon test on rows. col_wilcoxon_paired(x, y) - two sample paired Wilcoxon test on columns.

Results should be the same as running wilcox.test(x, y, paired=TRUE) on every row (or column) of x and y.

By default if 'exact' argument is set to 'NA', exact p-values are computed only if both 'x' and 'y' contain less than 50 values and there are no ties. Single sample and paired tests have additional requirement of not having zeroe values (values equal to null hypothesis location argument 'mu'). Otherwise, a normal approximation is used. Be wary of using 'exact=TRUE' on large sample sizes as computations can take a very long time.

'correct' argument controls the continuity correction of p-values but only when exact p-values cannot be computed and normal approximation is used. For cases where exact p-values are returned 'correct' is switched to FALSE.

Value

a data.frame where each row contains the results of a wilcoxon test performed on the corresponding row/column of x. The columns will vary depending on the type of test performed.

They will contain a subset of the following information:
1. obs.x - number of x observations
2. obs.y - number of y observations
3. obs.tot - total number of observations
4. obs.paired - number of paired observations (present in x and y)
5. statistic - Wilcoxon test statistic
6. pvalue - p-value
7. location.null - location shift of the null hypothesis
8. alternative - chosen alternative hypothesis
9. exact - indicates if exact p-value was computed
10. correct - indicates if continuity correction was performed

Note

Confidence interval and pseudo-median calculations are not implemented.

Author(s)

Karolis Koncevičius

See Also

wilcox.test()

Examples

X <- iris[iris$Species=="setosa",1:4]
Y <- iris[iris$Species=="virginica",1:4]
col_wilcoxon_twosample(X, Y)

# same row using different alternative hypotheses
col_wilcoxon_twosample(X[,c(1,1,1)], Y[,c(1,1,1)], alternative=c("t", "g", "l"))

matrixTests documentation built on Oct. 6, 2023, 1:07 a.m.