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Reports additional information relevant to the report (see list of supported objects in report()).

Usage

report_info(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

An object of class report_info().

Examples

library(report)

# h-tests
report_info(t.test(iris$Sepal.Width, iris$Sepal.Length))
#> Effect sizes were labelled following Cohen's (1988) recommendations.

# ANOVAs
report_info(aov(Sepal.Length ~ Species, data = iris))
#> For one-way between subjects designs, partial eta squared is equivalent to eta squared.
#> Returning eta squared.
#> Effect sizes were labelled following Field's (2013) recommendations.
# \donttest{
# GLMs
report_info(lm(Sepal.Length ~ Petal.Length * Species, data = iris))
#> Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald t-distribution approximation.
report_info(lm(Sepal.Length ~ Petal.Length * Species, data = iris), include_effectsize = TRUE)
#> Standardized parameters were obtained by fitting the model on a standardized version of the dataset and were labelled following Cohen's (1988) recommendations. 95% Confidence Intervals (CIs) and p-values were computed using a Wald t-distribution approximation.
report_info(glm(vs ~ disp, data = mtcars, family = "binomial"))
#> Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald z-distribution approximation.

# Mixed models
if (require("lme4")) {
  model <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
  report_info(model)
}
#> Package 'merDeriv' needs to be installed to compute confidence intervals
#>   for random effect parameters.
#> Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald t-distribution approximation.

# Bayesian models
if (require("rstanarm")) {
  model <- stan_glm(Sepal.Length ~ Species, data = iris, refresh = 0, iter = 300)
  report_info(model)
}
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
#> Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT) framework, we report the median of the posterior distribution and its 95% CI (Highest Density Interval), along the probability of direction (pd), the probability of significance and the probability of being large. The thresholds beyond which the effect is considered as significant (i.e., non-negligible) and large are |0.04| and |0.25| (corresponding respectively to 0.05 and 0.30 of the outcome's SD). Convergence and stability of the Bayesian sampling has been assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017).
# }