Performs one-sample and two-sample sign tests.

sign_test(data, formula, comparisons = NULL, ref.group = NULL,
  p.adjust.method = "holm", alternative = "two.sided", mu = 0,
  conf.level = 0.95, detailed = FALSE)

pairwise_sign_test(data, formula, comparisons = NULL, ref.group = NULL,
  p.adjust.method = "holm", detailed = FALSE, ...)

Arguments

data

a data.frame containing the variables in the formula.

formula

a formula of the form x ~ group where x is a numeric variable giving the data values and group is a factor with one or multiple levels giving the corresponding groups. For example, formula = TP53 ~ treatment.

comparisons

A list of length-2 vectors specifying the groups of interest to be compared. For example to compare groups "A" vs "B" and "B" vs "C", the argument is as follow: comparisons = list(c("A", "B"), c("B", "C"))

ref.group

a character string specifying the reference group. If specified, for a given grouping variable, each of the group levels will be compared to the reference group (i.e. control group).

p.adjust.method

method to adjust p values for multiple comparisons. Used when pairwise comparisons are performed. Allowed values include "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". If you don't want to adjust the p value (not recommended), use p.adjust.method = "none".

alternative

a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less". You can specify just the initial letter.

mu

a single number representing the value of the population median specified by the null hypothesis.

conf.level

confidence level of the interval.

detailed

logical value. Default is FALSE. If TRUE, a detailed result is shown.

...

other arguments passed to the function sign_test()

Value

return a data frame with some the following columns:

  • .y.: the y variable used in the test.

  • group1,group2: the compared groups in the pairwise tests.

  • n,n1,n2: Sample counts.

  • statistic: Test statistic used to compute the p-value. That is the S-statistic (the number of positive differences between the data and the hypothesized median), with names attribute "S".

  • df, parameter: degrees of freedom. Here, the total number of valid differences.

  • p: p-value.

  • method: the statistical test used to compare groups.

  • p.signif, p.adj.signif: the significance level of p-values and adjusted p-values, respectively.

  • estimate: estimate of the effect size. It corresponds to the median of the differences.

  • alternative: a character string describing the alternative hypothesis.

  • conf.low,conf.high: Lower and upper bound on a confidence interval of the estimate.

The returned object has an attribute called args, which is a list holding the test arguments.

Functions

  • sign_test: Sign test

  • pairwise_sign_test: performs pairwise two sample Wilcoxon test.

Note

This function is a reimplementation of the function SignTest() from the DescTools package.

Examples

# Load data #::::::::::::::::::::::::::::::::::::::: data("ToothGrowth") df <- ToothGrowth # One-sample test #::::::::::::::::::::::::::::::::::::::::: df %>% sign_test(len ~ 1, mu = 0)
#> # A tibble: 1 x 7 #> .y. group1 group2 n statistic df p #> * <chr> <chr> <chr> <int> <dbl> <dbl> <dbl> #> 1 len 1 null model 60 60 60 1.73e-18
# Two-samples paired test #::::::::::::::::::::::::::::::::::::::::: df %>% sign_test(len ~ supp)
#> # A tibble: 1 x 8 #> .y. group1 group2 n1 n2 statistic df p #> * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> #> 1 len OJ VC 30 30 19 29 0.136
# Compare supp levels after grouping the data by "dose" #:::::::::::::::::::::::::::::::::::::::: df %>% group_by(dose) %>% sign_test(data =., len ~ supp) %>% adjust_pvalue(method = "bonferroni") %>% add_significance("p.adj")
#> # A tibble: 3 x 11 #> dose .y. group1 group2 n1 n2 statistic df p p.adj p.adj.signif #> <dbl> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <chr> #> 1 0.5 len OJ VC 10 10 7 9 0.18 0.54 ns #> 2 1 len OJ VC 10 10 8 10 0.109 0.327 ns #> 3 2 len OJ VC 10 10 4 10 0.754 1 ns
# pairwise comparisons #:::::::::::::::::::::::::::::::::::::::: # As dose contains more than two levels ==> # pairwise test is automatically performed. df %>% sign_test(len ~ dose)
#> # A tibble: 3 x 10 #> .y. group1 group2 n1 n2 statistic df p p.adj p.adj.signif #> * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <chr> #> 1 len 0.5 1 20 20 1 20 4.01e-5 8.02e-5 **** #> 2 len 0.5 2 20 20 0 20 1.91e-6 5.73e-6 **** #> 3 len 1 2 20 20 3 20 3.00e-3 3.00e-3 **
# Comparison against reference group #:::::::::::::::::::::::::::::::::::::::: # each level is compared to the ref group df %>% sign_test(len ~ dose, ref.group = "0.5")
#> # A tibble: 2 x 10 #> .y. group1 group2 n1 n2 statistic df p p.adj p.adj.signif #> * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <chr> #> 1 len 0.5 1 20 20 1 20 4.01e-5 4.01e-5 **** #> 2 len 0.5 2 20 20 0 20 1.91e-6 3.82e-6 ****