From 4f5342dbd64cd551ed14b9b1b50d1c5ebd00aafb Mon Sep 17 00:00:00 2001 From: Daniel Date: Fri, 24 May 2024 12:15:03 +0200 Subject: [PATCH] update readme --- README.Rmd | 4 +-- README.md | 72 ++++++++++++++++++++++++++++++------------------------ 2 files changed, 42 insertions(+), 34 deletions(-) diff --git a/README.Rmd b/README.Rmd index 7bca5c21..58dc8d13 100644 --- a/README.Rmd +++ b/README.Rmd @@ -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 diff --git a/README.md b/README.md index b040f241..5faabf46 100644 --- a/README.md +++ b/README.md @@ -78,7 +78,7 @@ The package documentation can be found ## Report all the things - +All the things meme by Allie Brosh ### General Workflow @@ -262,28 +262,28 @@ report(model) # 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 @@ -296,10 +296,16 @@ model <- lm(Sepal.Length ~ Species, data = iris) 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] @@ -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) | @@ -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 . - # - 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, # . - # - 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, . 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 (2013). _Seamless R and # C++ Integration with Rcpp_. Springer, New York. doi: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 # . - # - 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, # . 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., @@ -387,11 +396,11 @@ report(sessionInfo()) # "Automated Results Reporting as a Practical Tool to Improve Reproducibility and # Methodological Best Practices Adoption." _CRAN_. # . - # - 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. # . # - 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, # . ## Credits @@ -401,7 +410,6 @@ as follows: ``` r citation("report") - To cite in publications use: Makowski, D., Lüdecke, D., Patil, I., Thériault, R., Ben-Shachar,