Create reports for htest objects (t.test(), cor.test(),
etc.).
Usage
# S3 method for htest
report(x, ...)
# S3 method for htest
report_effectsize(x, ...)
# S3 method for htest
report_table(x, ...)
# S3 method for htest
report_statistics(x, table = NULL, ...)
# S3 method for htest
report_parameters(x, table = NULL, ...)
# S3 method for htest
report_model(x, table = NULL, ...)
# S3 method for htest
report_info(x, effectsize = NULL, ...)
# S3 method for htest
report_text(x, table = NULL, ...)Arguments
- x
Object of class
htest.- ...
Arguments passed to or from other methods.
- table
Provide the output of
report_table()to avoid its re-computation.- effectsize
Provide the output of
report_effectsize()to avoid its re-computation.
Value
An object of class report().
See also
Specific components of reports (especially for stats models):
Other types of reports:
Methods:
Template file for supporting new models:
Examples
# t-tests
report(t.test(iris$Sepal.Width, iris$Sepal.Length))
#> Effect sizes were labelled following Cohen's (1988) recommendations.
#>
#> The Welch Two Sample t-test testing the difference between iris$Sepal.Width and
#> iris$Sepal.Length (mean of x = 3.06, mean of y = 5.84) suggests that the effect
#> is negative, statistically significant, and large (difference = -2.79, 95% CI
#> [-2.94, -2.64], t(225.68) = -36.46, p < .001; Cohen's d = -4.21, 95% CI [-4.66,
#> -3.76])
report(t.test(iris$Sepal.Width, iris$Sepal.Length, var.equal = TRUE))
#> Effect sizes were labelled following Cohen's (1988) recommendations.
#>
#> The Two Sample t-test testing the difference between iris$Sepal.Width and
#> iris$Sepal.Length (mean of x = 3.06, mean of y = 5.84) suggests that the effect
#> is negative, statistically significant, and large (difference = -2.79, 95% CI
#> [-2.94, -2.64], t(298) = -36.46, p < .001; Cohen's d = -4.21, 95% CI [-4.61,
#> -3.80])
report(t.test(mtcars$mpg ~ mtcars$vs))
#> Effect sizes were labelled following Cohen's (1988) recommendations.
#>
#> The Welch Two Sample t-test testing the difference of mtcars$mpg by mtcars$vs
#> (mean in group 0 = 16.62, mean in group 1 = 24.56) suggests that the effect is
#> negative, statistically significant, and large (difference = -7.94, 95% CI
#> [-11.46, -4.42], t(22.72) = -4.67, p < .001; Cohen's d = -1.70, 95% CI [-2.55,
#> -0.82])
report(t.test(mtcars$mpg, mtcars$vs, paired = TRUE), verbose = FALSE)
#> Effect sizes were labelled following Cohen's (1988) recommendations.
#>
#> The Paired t-test testing the difference between mtcars$mpg and mtcars$vs (mean
#> difference = 19.65) suggests that the effect is positive, statistically
#> significant, and large (difference = 19.65, 95% CI [17.60, 21.71], t(31) =
#> 19.49, p < .001; Cohen's d = 3.45, 95% CI [2.52, 4.36])
report(t.test(iris$Sepal.Width, mu = 1))
#> Effect sizes were labelled following Cohen's (1988) recommendations.
#>
#> The One Sample t-test testing the difference between iris$Sepal.Width (mean =
#> 3.06) and mu = 1 suggests that the effect is positive, statistically
#> significant, and large (difference = 2.06, 95% CI [2.99, 3.13], t(149) = 57.81,
#> p < .001; Cohen's d = 4.72, 95% CI [4.15, 5.27])
# Correlations
report(cor.test(iris$Sepal.Width, iris$Sepal.Length))
#> Effect sizes were labelled following Funder's (2019) recommendations.
#>
#> The Pearson's product-moment correlation between iris$Sepal.Width and
#> iris$Sepal.Length is negative, statistically not significant, and small (r =
#> -0.12, 95% CI [-0.27, 0.04], t(148) = -1.44, p = 0.152)