Create reports of different objects. See the documentation for your object's class:
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
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()).
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.
See also
Specific components of reports (especially for stats models):
Other types of reports:
Methods:
Template file for supporting new models:
Examples
library(report)
model <- t.test(mtcars$mpg ~ mtcars$am)
r <- report(model)
# Text
r
#> Effect sizes were labelled following Cohen's (1988) recommendations.
#>
#> The Welch Two Sample t-test testing the difference of mtcars$mpg by mtcars$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 = -1.41, 95% CI [-2.26,
#> -0.53])
summary(r)
#> The Welch Two Sample t-test testing the difference of mtcars$mpg by mtcars$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 = -1.41)
# Tables
as.data.frame(r)
#> Welch Two Sample t-test
#>
#> Parameter | Group | Mean_Group1 | Mean_Group2 | Difference | 95% CI | t(18.33) | p | Cohen's d | Cohens_d CI
#> ---------------------------------------------------------------------------------------------------------------------------------
#> mtcars$mpg | mtcars$am | 17.15 | 24.39 | -7.24 | [-11.28, -3.21] | -3.77 | 0.001 | -1.41 | [-2.26, -0.53]
#>
#> Alternative hypothesis: two.sided
summary(as.data.frame(r))
#> Difference | 95% CI | t(18.33) | p | Cohen's d | Cohens_d CI
#> ----------------------------------------------------------------------------
#> -7.24 | [-11.28, -3.21] | -3.77 | 0.001 | -1.41 | [-2.26, -0.53]
#>
#> Alternative hypothesis: two.sided