Create reports of different objects. See the documentation for your object's class:

Most of the time, the object created by the report() function can be further transformed, for instance summarized (using summary()), or converted to a table (using as.data.frame()).

## Usage

report(x, ...)

## Arguments

x

The R object that you want to report (see list of of supported objects above).

...

Arguments passed to or from other methods.

## Value

A list-object of class report, which contains further list-objects with a short and long description of the model summary, as well as a short and long table of parameters and fit indices.

## Details

### Organization

report_table and report_text are the two distal representations of a report, and are the two provided in report(). However, intermediate steps are accessible (depending on the object) via specific functions (e.g., report_parameters).

### Output

The report() function generates a report-object that contain in itself different representations (e.g., text, tables, plots). These different representations can be accessed via several functions, such as:

• as.report_text(r): Detailed text.

• as.report_text(r, summary=TRUE): Minimal text giving the minimal information.

• as.report_table(r): Comprehensive table including most available indices.

• as.report_table(r, summary=TRUE): Minimal table.

Note that for some report objects, some of these representations might be identical.

Specific components of reports (especially for stats models):

• report_table()

• report_parameters()

• report_statistics()

• report_effectsize()

• report_model()

• report_priors()

• report_random()

• report_performance()

• report_info()

• report_text()

Other types of reports:

• report_system()

• report_packages()

• report_participants()

• report_sample()

• report_date()

Methods:

• as.report()

Template file for supporting new models:

• report.default()

## Examples


library(report)

model <- t.test(mpg ~ am, data = mtcars)
r <- report(model)
#> Warning: Unable to retrieve data from htest object. Returning an approximate
#>   effect size using t_to_d().

# Text
r
#> Effect sizes were labelled following Cohen's (1988) recommendations.
#>
#> The Welch Two Sample t-test testing the difference of mpg by am (mean in group
#> 0 = 17.15, mean in group 1 = 24.39) suggests that the effect is negative,
#> statistically significant, and large (difference = -7.24, 95% CI [-11.28,
#> -3.21], t(18.33) = -3.77, p = 0.001; Cohen's d = -1.76, 95% CI [-2.82, -0.67])
summary(r)
#> The Welch Two Sample t-test testing the difference of mpg by am (mean in group
#> 0 = 17.15, mean in group 1 = 24.39) suggests that the effect is negative,
#> statistically significant, and large (difference = -7.24, 95% CI [-11.28,
#> -3.21], t(18.33) = -3.77, p = 0.001, Cohen's d = -1.76)

# Tables
as.data.frame(r)
#> Welch Two Sample t-test
#>
#> Parameter | Group | Mean_Group1 | Mean_Group2 | Difference |          95% CI | t(18.33) |     p |     d |          d  CI
#> ------------------------------------------------------------------------------------------------------------------------
#> mpg       |    am |       17.15 |       24.39 |      -7.24 | [-11.28, -3.21] |    -3.77 | 0.001 | -1.76 | [-2.82, -0.67]
#>
#> Alternative hypothesis: two.sided
summary(as.data.frame(r))
#> Difference |          95% CI | t(18.33) |     p |     d
#> -------------------------------------------------------
#> -7.24      | [-11.28, -3.21] |    -3.77 | 0.001 | -1.76
#>
#> Alternative hypothesis: two.sided