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Format statistical results

Source: R/format_stats.R
format_stats.Rd

A generic function that takes objects from various statistical methods to create formatted character strings to insert into R Markdown or Quarto documents. Currently, the generic function works with the following objects:

  1. htest objects of correlations, t-tests, and Wilcoxon tests

  2. correlations from the {correlation} package.

  3. aov objects for ANOVAs

  4. Bayes factors from the {BayesFactor} package. The function invokes specific methods that depend on the class of the first argument.

Usage

format_stats(x, ...)

Arguments

x

Statistical object.

...

Additional arguments passed to methods. For method-specific arguments, see format_stats.htest() for htest correlations, t-tests, and Wilcoxon tests, format_stats.easycorrelation() for easycorrelation correlations, format_stats.lm() for linear models, format_stats.merMod() and format_stats.lmerModLmerTest() for linear mixed models, and format_stats.BFBayesFactor() for Bayes factors from the {BayesFactor} package.

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.BFBayesFactor(), format_stats.aov(), format_stats.easycorrelation(), format_stats.htest(), format_stats.lm(), format_stats.lmerModLmerTest(), format_stats.merMod(), format_ttest()

Examples

# Format cor.test() object
format_stats(cor.test(mtcars$mpg, mtcars$cyl))
#> [1] "_r_ = -.85, 95% CI [-0.93, -0.72], _p_ < .001"

# Format correlation::correlation() object
format_stats(correlation::correlation(data = mtcars, select = "mpg", select2 = "cyl"))
#> [1] "_r_ = -.85, 95% CI [-0.93, -0.72], _p_ < .001"

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

# Format aov() object
format_stats(aov(mpg ~ cyl * hp, data = mtcars), term = "cyl")
#> [1] "_F_(1, 28) = 92.5, _p_ < .001"

# Format lm() or glm() object
format_stats(lm(mpg ~ cyl * hp, data = mtcars), term = "cyl")
#> [1] "_β_ = -4.119, SE = 0.988, _t_ = -4.168, _p_ < .001"
format_stats(glm(am ~ cyl * hp, data = mtcars, family = binomial), term = "cyl")
#> [1] "_β_ = -1.749, SE = 0.839, _z_ = -2.084, _p_ = .037"

# Format lme4::lmer() or lme4::glmer() object
format_stats(lme4::lmer(mpg ~ hp + (1 | cyl), data = mtcars), term = "hp")
#> [1] "_β_ = -0.030, SE = 0.015, _t_ = -2.088"
format_stats(lme4::glmer(am ~ hp + (1 | cyl), data = mtcars, family = binomial), term = "hp")
#> [1] "_β_ = 0.022, SE = 0.017, _z_ = 1.300, _p_ = .194"

# Format lmerTest::lmer() object
format_stats(lmerTest::lmer(mpg ~ hp + (1 | cyl), data = mtcars), term = "hp")
#> [1] "_β_ = -0.030, SE = 0.015, _t_ = -2.088, _p_ = .046"

# Format BFBayesFactor object from {BayesFactor} package
format_stats(BayesFactor::ttestBF(mtcars$vs, mtcars$am))
#> [1] "_BF_~10~ = 0.26"