Extract Label Information from Statistical Tests

Extracts label information from statistical tests. Useful for labelling plots with test outputs.

Usage

get_pwc_label(stat.test, type = c("expression", "text"))

get_test_label(
  stat.test,
  description = NULL,
  p.col = "p",
  type = c("expression", "text"),
  correction = c("auto", "GG", "HF", "none"),
  row = NULL,
  detailed = FALSE
)

create_test_label(
  statistic.text,
  statistic,
  p,
  parameter = NA,
  description = NULL,
  n = NA,
  effect.size = NA,
  effect.size.text = NA,
  type = c("expression", "text"),
  detailed = FALSE
)

get_n(stat.test)

get_description(stat.test)

Arguments

stat.test: statistical test results returned by rstatix functions.
type: the label type. Can be one of "text" and "expression". Partial match allowed. If you want to add the label onto a ggplot, it might be useful to specify type = "expresion".
description: the test description used as the prefix of the label. Examples of description are "ANOVA", "Two Way ANOVA". To remove the default description, specify description = NULL. If missing, we'll try to guess the statistical test default description.
p.col: character specifying the column containing the p-value. Default is "p", can be "p.adj".
correction: character, considered only in the case of ANOVA test. Which sphericity correction of the degrees of freedom should be reported for the within-subject factors (repeated measures). The default is set to "GG" corresponding to the Greenhouse-Geisser correction. Possible values are "GG", "HF" (i.e., Hyunh-Feldt correction), "none" (i.e., no correction) and "auto" (apply automatically GG correction if the sphericity assumption is not for within-subject design.
row: numeric, the row index to be considered. If NULL, the last row is automatically considered for ANOVA test.
detailed: logical value. If TRUE, returns detailed label.
statistic.text: character specifying the test statistic. For example statistic.text = "F" (for ANOVA test ); statistic.text = "t" (for t-test ).
statistic: the numeric value of a statistic.
p: the p-value of the test.
parameter: string containing the degree of freedom (if exists). Default is NA to accommodate non-parametric tests. For example parameter = "1,9" (for ANOVA test. Two parameters exist: DFn and DFd); sparameter = "9" (for t-test ).
n: sample count, example: n = 10.
effect.size: the effect size value
effect.size.text: a character specifying the relevant effect size. For example, for Cohens d statistic, effect.size.text = "d". You can also use plotmath expression as follow quote(italic("d")).

Value

a text label or an expression to pass to a plotting function.

Functions

get_pwc_label(): Extract label from pairwise comparisons.
get_test_label(): Extract labels for statistical tests.
create_test_label(): Create labels from user specified test results.
get_n(): Extracts sample counts (n) from an rstatix test outputs. Returns a numeric vector.
get_description(): Extracts the description of an rstatix test outputs. Returns a character vector.

Examples

# Load data
#:::::::::::::::::::::::::::::::::::::::
data("ToothGrowth")
df <- ToothGrowth

# One-way ANOVA test
#:::::::::::::::::::::::::::::::::::::::::
anov <- df %>% anova_test(len ~ dose)
get_test_label(anov, detailed = TRUE, type = "text")
#> [1] "Anova, F(1,58) = 105.06, p = <0.0001, eta2[g] = 0.644, n = 60"

# Two-way ANOVA test
#:::::::::::::::::::::::::::::::::::::::::
anov <- df %>% anova_test(len ~ supp*dose)
get_test_label(anov, detailed = TRUE, type = "text",
   description = "Two Way ANOVA")
#> [1] "Two Way ANOVA, F(1,56) = 5.33, p = 0.025, eta2[g] = 0.087, n = 60"


# Kruskal-Wallis test
#:::::::::::::::::::::::::::::::::::::::::
kruskal<- df %>% kruskal_test(len ~ dose)
get_test_label(kruskal, detailed = TRUE, type = "text")
#> [1] "Kruskal-Wallis, X2(2) = 40.67, p = <0.0001, n = 60"

# Wilcoxon test
#:::::::::::::::::::::::::::::::::::::::::
# Unpaired test
wilcox <- df %>% wilcox_test(len ~ supp)
get_test_label(wilcox, detailed = TRUE, type = "text")
#> [1] "Wilcoxon test, W = 575.5, p = 0.064, n = 60"
# Paired test
wilcox <- df %>% wilcox_test(len ~ supp, paired = TRUE)
get_test_label(wilcox, detailed = TRUE, type = "text")
#> [1] "Wilcoxon test, V = 369, p = 0.0038, n = 30"

# T test
#:::::::::::::::::::::::::::::::::::::::::
ttest <- df %>% t_test(len ~ dose)
get_test_label(ttest, detailed = TRUE, type = "text")
#> [[1]]
#> [1] "T test, t(37.99) = -6.48, p = <0.0001, n = 40"
#> 
#> [[2]]
#> [1] "T test, t(36.88) = -11.8, p = <0.0001, n = 40"
#> 
#> [[3]]
#> [1] "T test, t(37.1) = -4.9, p = <0.0001, n = 40"
#> 


# Pairwise comparisons labels
#:::::::::::::::::::::::::::::::::::::::::
get_pwc_label(ttest, type = "text")
#> [1] "pwc: T test; p.adjust: Holm"


# Create test labels
#:::::::::::::::::::::::::::::::::::::::::
create_test_label(
  statistic.text = "F", statistic = 71.82,
  parameter = "4, 294",
  p = "<0.0001",
  description = "ANOVA",
  type = "text"
)
#> [1] "ANOVA, p = <0.0001"


# Extract infos
#:::::::::::::::::::::::::::::::::::::::::
stat.test <- df %>% t_test(len ~ dose)
get_n(stat.test)
#> [1] 40 40 40
get_description(stat.test)
#> [1] "T test"