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()
).
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.
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(mpg ~ am, data = mtcars)
r <- report(model)
#> Warning: Unable to retrieve data from htest object. Using t_to_d() approximation.
# 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 | d CI
#> ------------------------------------------------------------------------
#> -7.24 | [-11.28, -3.21] | -3.77 | 0.001 | -1.76 | [-2.82, -0.67]
#>
#> Alternative hypothesis: two.sided