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4 changes: 2 additions & 2 deletions README.Rmd
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Expand Up @@ -262,11 +262,11 @@ paste(
Report can also help you create a sample description table (also referred to as **Table 1**).

```{r, eval=FALSE}
report_sample(iris, group_by = "Species")
report_sample(iris, by = "Species")
```

```{r, echo=FALSE}
knitr::kable(report_sample(iris, group_by = "Species"))
knitr::kable(report_sample(iris, by = "Species"))
```

### Report system and packages
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72 changes: 40 additions & 32 deletions README.md
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Expand Up @@ -78,7 +78,7 @@ The package documentation can be found

## Report all the things

<a href=https://easystats.github.io/report/><img src="man/figures/allthethings.jpg" height="60"></a>
<a href=https://easystats.github.io/report/><img src="man/figures/allthethings.jpg" height="60" alt="All the things meme by Allie Brosh" ></a>

### General Workflow

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# of 1000 iterations and a warmup of 500) to predict mpg with qsec and wt
# (formula: mpg ~ qsec + wt). Priors over parameters were all set as normal (mean
# = 0.00, SD = 8.43; mean = 0.00, SD = 15.40) distributions. The model's
# explanatory power is substantial (R2 = 0.81, 95% CI [0.69, 0.89], adj. R2 =
# 0.79). The model's intercept, corresponding to qsec = 0 and wt = 0, is at 19.56
# (95% CI [9.60, 30.59]). Within this model:
# explanatory power is substantial (R2 = 0.81, 95% CI [0.70, 0.90], adj. R2 =
# 0.79). The model's intercept, corresponding to qsec = 0 and wt = 0, is at 19.80
# (95% CI [8.93, 29.80]). Within this model:
#
# - The effect of qsec (Median = 0.94, 95% CI [0.38, 1.45]) has a 99.90%
# probability of being positive (> 0), 98.80% of being significant (> 0.30), and
# 0.05% of being large (> 1.81). The estimation successfully converged (Rhat =
# 1.001) and the indices are reliable (ESS = 1921)
# - The effect of wt (Median = -5.05, 95% CI [-6.01, -4.05]) has a 100.00%
# - The effect of qsec (Median = 0.93, 95% CI [0.40, 1.49]) has a 100.00%
# probability of being positive (> 0), 99.05% of being significant (> 0.30), and
# 0.25% of being large (> 1.81). The estimation successfully converged (Rhat =
# 1.000) and the indices are reliable (ESS = 1864)
# - The effect of wt (Median = -5.04, 95% CI [-5.99, -4.08]) has a 100.00%
# probability of being negative (< 0), 100.00% of being significant (< -0.30),
# and 100.00% of being large (< -1.81). The estimation successfully converged
# (Rhat = 1.000) and the indices are reliable (ESS = 2020)
# (Rhat = 0.999) and the indices are reliable (ESS = 2424)
#
# 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 |0.30| and |1.81|. 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).
# large are |0.30| and |1.81| (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).

## Other types of reports

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report_model(model)
# linear model (estimated using OLS) to predict Sepal.Length with Species (formula: Sepal.Length ~ Species)
```

``` r

report_performance(model)
# The model explains a statistically significant and substantial proportion of
# variance (R2 = 0.62, F(2, 147) = 119.26, p < .001, adj. R2 = 0.61)
```

``` r

report_statistics(model)
# beta = 5.01, 95% CI [4.86, 5.15], t(147) = 68.76, p < .001; Std. beta = -1.01, 95% CI [-1.18, -0.84]
Expand Down Expand Up @@ -334,7 +340,7 @@ Report can also help you create a sample description table (also
referred to as **Table 1**).

``` r
report_sample(iris, group_by = "Species")
report_sample(iris, by = "Species")
```

| Variable | setosa (n=50) | versicolor (n=50) | virginica (n=50) | Total (n=150) |
Expand All @@ -353,32 +359,35 @@ analysis paragraph about the tools used.
report(sessionInfo())
```

# Analyses were conducted using the R Statistical language (version 4.2.2; R Core
# Team, 2022) on macOS Ventura 13.1, using the packages lme4 (version 1.1.32;
# Bates D et al., 2015), Matrix (version 1.5.3; Bates D et al., 2022), Rcpp
# (version 1.0.10; Eddelbuettel D, François R, 2011), rstanarm (version 2.21.3;
# Goodrich B et al., 2022), report (version 0.5.7; Makowski D et al., 2023) and
# dplyr (version 1.1.0; Wickham H et al., 2023).
# Analyses were conducted using the R Statistical language (version 4.4.0; R Core
# Team, 2024) on Windows 11 x64 (build 22631), using the packages lme4 (version
# 1.1.35.3; Bates D et al., 2015), Matrix (version 1.7.0; Bates D et al., 2024),
# Rcpp (version 1.0.12; Eddelbuettel D et al., 2024), rstanarm (version 2.32.1;
# Goodrich B et al., 2024), report (version 0.5.8.3; Makowski D et al., 2023) and
# dplyr (version 1.1.4; Wickham H et al., 2023).
#
# References
# ----------
# - Bates D, Mächler M, Bolker B, Walker S (2015). "Fitting Linear Mixed-Effects
# Models Using lme4." _Journal of Statistical Software_, *67*(1), 1-48.
# doi:10.18637/jss.v067.i01 <https://doi.org/10.18637/jss.v067.i01>.
# - Bates D, Maechler M, Jagan M (2022). _Matrix: Sparse and Dense Matrix Classes
# and Methods_. R package version 1.5-3,
# - Bates D, Maechler M, Jagan M (2024). _Matrix: Sparse and Dense Matrix Classes
# and Methods_. R package version 1.7-0,
# <https://CRAN.R-project.org/package=Matrix>.
# - Eddelbuettel D, François R (2011). "Rcpp: Seamless R and C++ Integration."
# _Journal of Statistical Software_, *40*(8), 1-18. doi:10.18637/jss.v040.i08
# - Eddelbuettel D, Francois R, Allaire J, Ushey K, Kou Q, Russell N, Ucar I,
# Bates D, Chambers J (2024). _Rcpp: Seamless R and C++ Integration_. R package
# version 1.0.12, <https://CRAN.R-project.org/package=Rcpp>. Eddelbuettel D,
# François R (2011). "Rcpp: Seamless R and C++ Integration." _Journal of
# Statistical Software_, *40*(8), 1-18. doi:10.18637/jss.v040.i08
# <https://doi.org/10.18637/jss.v040.i08>. Eddelbuettel D (2013). _Seamless R and
# C++ Integration with Rcpp_. Springer, New York. doi:10.1007/978-1-4614-6868-4
# <https://doi.org/10.1007/978-1-4614-6868-4>, ISBN 978-1-4614-6867-7.
# Eddelbuettel D, Balamuta JJ (2018). "Extending extitR with extitC++: A Brief
# Introduction to extitRcpp." _The American Statistician_, *72*(1), 28-36.
# Eddelbuettel D, Balamuta J (2018). "Extending R with C++: A Brief Introduction
# to Rcpp." _The American Statistician_, *72*(1), 28-36.
# doi:10.1080/00031305.2017.1375990
# <https://doi.org/10.1080/00031305.2017.1375990>.
# - Goodrich B, Gabry J, Ali I, Brilleman S (2022). "rstanarm: Bayesian applied
# regression modeling via Stan." R package version 2.21.3,
# - Goodrich B, Gabry J, Ali I, Brilleman S (2024). "rstanarm: Bayesian applied
# regression modeling via Stan." R package version 2.32.1,
# <https://mc-stan.org/rstanarm/>. Brilleman S, Crowther M, Moreno-Betancur M,
# Buros Novik J, Wolfe R (2018). "Joint longitudinal and time-to-event models via
# Stan." StanCon 2018. 10-12 Jan 2018. Pacific Grove, CA, USA.,
Expand All @@ -387,11 +396,11 @@ report(sessionInfo())
# "Automated Results Reporting as a Practical Tool to Improve Reproducibility and
# Methodological Best Practices Adoption." _CRAN_.
# <https://easystats.github.io/report/>.
# - R Core Team (2022). _R: A Language and Environment for Statistical
# - R Core Team (2024). _R: A Language and Environment for Statistical
# Computing_. R Foundation for Statistical Computing, Vienna, Austria.
# <https://www.R-project.org/>.
# - Wickham H, François R, Henry L, Müller K, Vaughan D (2023). _dplyr: A Grammar
# of Data Manipulation_. R package version 1.1.0,
# of Data Manipulation_. R package version 1.1.4,
# <https://CRAN.R-project.org/package=dplyr>.

## Credits
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``` r
citation("report")

To cite in publications use:

Makowski, D., Lüdecke, D., Patil, I., Thériault, R., Ben-Shachar,
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