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

System and packages (

`sessionInfo`

)Correlations and t-tests (

`htest`

)ANOVAs (

`aov, anova, aovlist, ...`

)Regression models (

`glm, lm, ...`

)Mixed models (

`glmer, lmer, glmmTMB, ...`

)Bayesian models (

`stanreg, brms...`

)Bayes factors (from

`bayestestR`

)Structural Equation Models (SEM) (from

`lavaan`

)Model comparison (from

`performance()`

)

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()`

).

`report(x, ...)`

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

... | Arguments passed to or from other methods. |

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.

`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`

).

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:

: Detailed text.`as.report_text(r)`

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

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

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

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

Specific components of reports (especially for stats models):

Other types of reports:

Methods:

Template file for supporting new models:

```
library(report)
model <- t.test(mpg ~ am, data = mtcars)
r <- report(model)
# 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)
#> Parameter | Group | Mean_Group1 | Mean_Group2 | Difference | 95% CI | t(18.33) | p | Method | Alternative | d | d CI
#> ----------------------------------------------------------------------------------------------------------------------------------------------------------------
#> mpg | am | 17.15 | 24.39 | -7.24 | [-11.28, -3.21] | -3.77 | 0.001 | Welch Two Sample t-test | two.sided | -1.76 | [-2.82, -0.67]
summary(as.data.frame(r))
#> Difference | 95% CI | t(18.33) | p | Alternative | d | d CI
#> --------------------------------------------------------------------------------------
#> -7.24 | [-11.28, -3.21] | -3.77 | 0.001 | two.sided | -1.76 | [-2.82, -0.67]
```