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 fromcor.test()
,t.test()
, orwilcox.test()
.- 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.
See also
Other functions for printing statistical objects:
format_bf()
,
format_corr()
,
format_stats()
,
format_stats.BFBayesFactor()
,
format_stats.aov()
,
format_stats.easycorrelation()
,
format_stats.lm()
,
format_stats.lmerModLmerTest()
,
format_stats.merMod()
,
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"