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Format hypothesis test statistics

Source: R/format_stats.R
format_stats.htest.Rd

This method formats hypothesis test statistics from the class htest. Currently, this includes correlations from cor.test() and t-tests and Wilcoxon tests from t.test() and wilcox.test(). For correlations, the function detects whether the object is from a Pearson, Spearman, or Kendall correlation and reports the appropriate correlation label (r, \(\tau\), \(\rho\)). The default output is APA formatted, but this function allows control over numbers of digits, leading zeros, the presence of means and confidence intervals, italics, degrees of freedom, and mean labels, and output format of Markdown or LaTeX.

Usage

# S3 method for class 'htest'
format_stats(
  x,
  digits = NULL,
  pdigits = 3,
  pzero = FALSE,
  full = TRUE,
  italics = TRUE,
  dfs = "par",
  mean = "abbr",
  type = "md",
  ...
)

Arguments

x

An htest object

digits

Number of digits after the decimal for means, confidence intervals, and test statistics

pdigits

Number of digits after the decimal for p-values, ranging between 1-5 (also controls cutoff for small p-values)

pzero

Logical value (default = FALSE) for whether to include leading zero for p-values

full

Logical value (default = TRUE) for whether to include means and confidence intervals or just test statistic and p-value

italics

Logical value (default = TRUE) for whether p label should be italicized

dfs

Formatting for degrees of freedom ("par" = parenthetical, "sub" = subscript, "none" = do not print degrees of freedom)

mean

Formatting for mean label ("abbr" = M, "word" = Mean)

type

Type of formatting ("md" = markdown, "latex" = LaTeX)

...

Additional arguments passed to methods.

Value

A character string of statistical information formatted in Markdown or LaTeX.

See also

Other functions for printing statistical objects: format_bf(), format_corr(), format_stats(), format_stats.BFBayesFactor(), format_stats.easycorrelation(), format_ttest()

Examples

# Prepare statistical objects
test_corr <- cor.test(mtcars$mpg, mtcars$cyl)
test_corr2 <- cor.test(mtcars$mpg, mtcars$cyl, method = "kendall")
#> Warning: Cannot compute exact p-value with ties
test_ttest <- t.test(mtcars$vs, mtcars$am)
test_ttest2 <- wilcox.test(mtcars$vs, mtcars$am)
#> Warning: cannot compute exact p-value with ties

# Format correlation
format_stats(test_corr)
#> [1] "_r_ = -.85, 95% CI [-0.93, -0.72], _p_ < .001"

# Remove confidence intervals and italics
format_stats(test_corr, full = FALSE, italics = FALSE)
#> [1] "r = -.85, p < .001"

# Change digits and add leading zero to p-value
format_stats(test_corr, digits = 3, pdigits = 4, pzero = TRUE)
#> [1] "_r_ = -0.852, 95% CI [-0.926, -0.716], _p_ < 1e-04"

# Format Kendall's tau
format_stats(test_corr2)
#> [1] "_τ_ = -.80, _p_ < .001"

# Format t-test
  format_stats(test_ttest)
#> [1] "_M_ = 0.0, 95% CI [-0.2, 0.3], _t_(62) = 0.2, _p_ = .804"

# Remove mean and confidence interval
format_stats(test_ttest, full = FALSE)
#> [1] "_t_(62) = 0.2, _p_ = .804"

# Remove degrees of freedom and spell out "Mean"
format_stats(test_ttest, dfs = "none", mean = "word")
#> [1] "_Mean_ = 0.0, 95% CI [-0.2, 0.3], _t_ = 0.2, _p_ = .804"

# Format for LaTeX
format_stats(test_ttest2, type = "latex")
#> [1] "$W$ = 528.0, $p$ = .808"