diff --git a/R/1_model_parameters.R b/R/1_model_parameters.R index 000b0ceb4..fd9f20c28 100644 --- a/R/1_model_parameters.R +++ b/R/1_model_parameters.R @@ -294,7 +294,7 @@ #' (*Greenland et al. 2022; Rafi and Greenland 2020; Schweder 2018; Schweder and #' Hjort 2003; Vos 2022*). #' -#' The _parameters_ package provides several options or functions to aid +#' The **parameters** package provides several options or functions to aid #' statistical inference. These are, for example: #' - [`equivalence_test()`], to compute the (conditional) equivalence test for #' frequentist models @@ -317,7 +317,7 @@ #' Most of the above shown options or functions derive from methods originally #' implemented for Bayesian models (*Makowski et al. 2019*). However, assuming #' that model assumptions are met (which means, the model fits well to the data, -#' the correct model is chosen that reflectsa the data generating process +#' the correct model is chosen that reflects the data generating process #' (distributional model family) etc.), it seems appropriate to interpret #' results from classical frequentist models in a "Bayesian way" (more details: #' documentation in [`p_function()`]). @@ -350,7 +350,7 @@ #' Indices of Effect Existence and Significance in the Bayesian Framework. #' Frontiers in Psychology, 10, 2767. \doi{10.3389/fpsyg.2019.02767} #' -#' - Neter, J., Wasserman, W., & Kutner, M. H. (1989). Applied linear +#' - Neter, J., Wasserman, W., and Kutner, M. H. (1989). Applied linear #' regression models. #' #' - Rafi Z, Greenland S. Semantic and cognitive tools to aid statistical diff --git a/README.Rmd b/README.Rmd index f7f52adbf..efa2ee881 100644 --- a/README.Rmd +++ b/README.Rmd @@ -1,5 +1,7 @@ --- output: github_document +bibliography: paper/paper.bib +csl: paper/apa.csl --- ```{r, warning=FALSE, message=FALSE, echo = FALSE} @@ -140,10 +142,9 @@ lm(disp ~ ., data = mtcars) |> ## Statistical inference - how to quantify evidence -There is no standardized approach to drawing conclusions based on the available data and statistical models. A frequently chosen but also much criticized approach is to evaluate results based on their statistical significance (*Amrhein et al. 2017*). +There is no standardized approach to drawing conclusions based on the available data and statistical models. A frequently chosen but also much criticized approach is to evaluate results based on their statistical significance ([@amrhein_earth_2017]). -A more sophisticated way would be to test whether estimated effects exceed the "smallest effect size of interest", to avoid even the smallest effects being considered relevant simply because they are statistically significant, but clinically or practically irrelevant (*Lakens et al. 2018, Lakens 2024*). A rather unconventional approach, which is nevertheless advocated by various authors, is to interpret results from classical regression models in terms of probabilities, similar to the usual approach in Bayesian statistics (*Greenland et al. 2022; Rafi and Greenland 2020; Schweder 2018; Schweder and -Hjort 2003; Vos 2022*). +A more sophisticated way would be to test whether estimated effects exceed the "smallest effect size of interest", to avoid even the smallest effects being considered relevant simply because they are statistically significant, but clinically or practically irrelevant [@lakens2020equivalence;@lakens_improving_2022]. A rather unconventional approach, which is nevertheless advocated by various authors, is to interpret results from classical regression models in terms of probabilities, similar to the usual approach in Bayesian statistics ([@greenland_aid_2022;@rafi_semantic_2020;@schweder_confidence_2018;@schweder_frequentist_2003;@vos_frequentist_2022]). The _parameters_ package provides several options or functions to aid statistical inference. These are, for example: @@ -154,7 +155,7 @@ The _parameters_ package provides several options or functions to aid statistica - the `s_value` argument (setting `s_value = TRUE`) in `model_parameters()` replaces the p-values with their related _S_-values (*Rafi and Greenland 2020*) - finally, it is possible to generate distributions of model coefficients by generating bootstrap-samples (setting `bootstrap = TRUE`) or simulating draws from model coefficients using [`simulate_model()`](https://easystats.github.io/parameters/reference/simulate_model.html). These samples can then be treated as "posterior samples" and used in many functions from the **bayestestR** package. -Most of the above shown options or functions derive from methods originally implemented for Bayesian models (*Makowski et al. 2019*). However, assuming that model assumptions are met (which means, the model fits well to the data, the correct model is chosen that reflectsa the data generating process (distributional model family) etc.), it seems appropriate to interpret results from classical frequentist models in a "Bayesian way" (more details: documentation in [`p_function()`]). +Most of the above shown options or functions derive from methods originally implemented for Bayesian models ([@makowski2019bayetestR]). However, assuming that model assumptions are met (which means, the model fits well to the data, the correct model is chosen that reflects the data generating process (distributional model family) etc.), it seems appropriate to interpret results from classical frequentist models in a "Bayesian way" (more details: documentation in [`p_function()`](https://easystats.github.io/parameters/reference/p_function.html)). ## Citation @@ -167,3 +168,5 @@ citation("parameters") ## Code of Conduct Please note that the parameters project is released with a [Contributor Code of Conduct](https://www.contributor-covenant.org/version/2/1/code_of_conduct/). By contributing to this project, you agree to abide by its terms. + +## References diff --git a/README.md b/README.md index 73c9daf0f..96aec0f2c 100644 --- a/README.md +++ b/README.md @@ -235,18 +235,18 @@ lm(disp ~ ., data = mtcars) |> There is no standardized approach to drawing conclusions based on the available data and statistical models. A frequently chosen but also much criticized approach is to evaluate results based on their statistical -significance (*Amrhein et al. 2017*). +significance ((Amrhein, Korner-Nievergelt, & Roth, 2017)). A more sophisticated way would be to test whether estimated effects exceed the “smallest effect size of interest”, to avoid even the smallest effects being considered relevant simply because they are statistically significant, but clinically or practically irrelevant -(*Lakens et al. 2018, Lakens 2024*). A rather unconventional approach, -which is nevertheless advocated by various authors, is to interpret -results from classical regression models in terms of probabilities, -similar to the usual approach in Bayesian statistics (*Greenland et -al. 2022; Rafi and Greenland 2020; Schweder 2018; Schweder and Hjort -2003; Vos 2022*). +(Lakens, 2024; Lakens, Scheel, & Isager, 2018). A rather unconventional +approach, which is nevertheless advocated by various authors, is to +interpret results from classical regression models in terms of +probabilities, similar to the usual approach in Bayesian statistics +((Greenland, Rafi, Matthews, & Higgs, 2022; Rafi & Greenland, 2020; +Schweder, 2018; Schweder & Hjort, 2003; Vos & Holbert, 2022)). The *parameters* package provides several options or functions to aid statistical inference. These are, for example: @@ -275,12 +275,13 @@ statistical inference. These are, for example: many functions from the **bayestestR** package. Most of the above shown options or functions derive from methods -originally implemented for Bayesian models (*Makowski et al. 2019*). -However, assuming that model assumptions are met (which means, the model -fits well to the data, the correct model is chosen that reflectsa the -data generating process (distributional model family) etc.), it seems -appropriate to interpret results from classical frequentist models in a -“Bayesian way” (more details: documentation in \[`p_function()`\]). +originally implemented for Bayesian models ((Makowski, Ben-Shachar, & +Lüdecke, 2019)). However, assuming that model assumptions are met (which +means, the model fits well to the data, the correct model is chosen that +reflects the data generating process (distributional model family) +etc.), it seems appropriate to interpret results from classical +frequentist models in a “Bayesian way” (more details: documentation in +[`p_function()`](https://easystats.github.io/parameters/reference/p_function.html)). ## Citation @@ -315,3 +316,86 @@ Please note that the parameters project is released with a [Contributor Code of Conduct](https://www.contributor-covenant.org/version/2/1/code_of_conduct/). By contributing to this project, you agree to abide by its terms. + +## References + +
+ +
+ +Amrhein, V., Korner-Nievergelt, F., & Roth, T. (2017). The earth is flat +( *p* \> 0.05): Significance thresholds and the crisis of unreplicable +research. *PeerJ*, *5*, e3544. + +
+ +
+ +Greenland, S., Rafi, Z., Matthews, R., & Higgs, M. (2022). *To Aid +Scientific Inference, Emphasize Unconditional Compatibility Descriptions +of Statistics*. Retrieved from + +
+ +
+ +Lakens, D. (2024). *Improving Your Statistical Inferences*. + + +
+ +
+ +Lakens, D., Scheel, A. M., & Isager, P. M. (2018). Equivalence testing +for psychological research: A tutorial. *Advances in Methods and +Practices in Psychological Science*, *1*(2), 259–269. + + +
+ +
+ +Makowski, D., Ben-Shachar, M., & Lüdecke, D. (2019). +bayestestR: Describing effects and their +uncertainty, existence and significance within the bayesian framework. +*Journal of Open Source Software*, *4*(40), 1541. + + +
+ +
+ +Rafi, Z., & Greenland, S. (2020). Semantic and cognitive tools to aid +statistical science: Replace confidence and significance by +compatibility and surprise. *BMC Medical Research Methodology*, *20*(1), +244. + +
+ +
+ +Schweder, T. (2018). Confidence is epistemic probability for empirical +science. *Journal of Statistical Planning and Inference*, *195*, +116–125. + +
+ +
+ +Schweder, T., & Hjort, N. L. (2003). Frequentist Analogues of Priors and +Posteriors. In B. Stigum (Ed.), *Econometrics and the Philosophy of +Economics: Theory-Data Confrontations in Economics* (pp. 285–217). +Retrieved from + +
+ +
+ +Vos, P., & Holbert, D. (2022). Frequentist statistical inference without +repeated sampling. *Synthese*, *200*(2), 89. + + +
+ +
diff --git a/man/equivalence_test.lm.Rd b/man/equivalence_test.lm.Rd index 349631599..86a303406 100644 --- a/man/equivalence_test.lm.Rd +++ b/man/equivalence_test.lm.Rd @@ -182,7 +182,7 @@ probabilities, similar to the usual approach in Bayesian statistics (\emph{Greenland et al. 2022; Rafi and Greenland 2020; Schweder 2018; Schweder and Hjort 2003; Vos 2022}). -The \emph{parameters} package provides several options or functions to aid +The \strong{parameters} package provides several options or functions to aid statistical inference. These are, for example: \itemize{ \item \code{\link[=equivalence_test]{equivalence_test()}}, to compute the (conditional) equivalence test for @@ -207,7 +207,7 @@ the \strong{bayestestR} package. Most of the above shown options or functions derive from methods originally implemented for Bayesian models (\emph{Makowski et al. 2019}). However, assuming that model assumptions are met (which means, the model fits well to the data, -the correct model is chosen that reflectsa the data generating process +the correct model is chosen that reflects the data generating process (distributional model family) etc.), it seems appropriate to interpret results from classical frequentist models in a "Bayesian way" (more details: documentation in \code{\link[=p_function]{p_function()}}). diff --git a/man/model_parameters.Rd b/man/model_parameters.Rd index 98bc06042..97ff22702 100644 --- a/man/model_parameters.Rd +++ b/man/model_parameters.Rd @@ -339,7 +339,7 @@ probabilities, similar to the usual approach in Bayesian statistics (\emph{Greenland et al. 2022; Rafi and Greenland 2020; Schweder 2018; Schweder and Hjort 2003; Vos 2022}). -The \emph{parameters} package provides several options or functions to aid +The \strong{parameters} package provides several options or functions to aid statistical inference. These are, for example: \itemize{ \item \code{\link[=equivalence_test]{equivalence_test()}}, to compute the (conditional) equivalence test for @@ -364,7 +364,7 @@ the \strong{bayestestR} package. Most of the above shown options or functions derive from methods originally implemented for Bayesian models (\emph{Makowski et al. 2019}). However, assuming that model assumptions are met (which means, the model fits well to the data, -the correct model is chosen that reflectsa the data generating process +the correct model is chosen that reflects the data generating process (distributional model family) etc.), it seems appropriate to interpret results from classical frequentist models in a "Bayesian way" (more details: documentation in \code{\link[=p_function]{p_function()}}). @@ -451,7 +451,7 @@ in Psychological Science, 1(2), 259–269. \doi{10.1177/2515245918770963} \item Makowski, D., Ben-Shachar, M. S., Chen, S. H. A., and Lüdecke, D. (2019). Indices of Effect Existence and Significance in the Bayesian Framework. Frontiers in Psychology, 10, 2767. \doi{10.3389/fpsyg.2019.02767} -\item Neter, J., Wasserman, W., & Kutner, M. H. (1989). Applied linear +\item Neter, J., Wasserman, W., and Kutner, M. H. (1989). Applied linear regression models. \item Rafi Z, Greenland S. Semantic and cognitive tools to aid statistical science: replace confidence and significance by compatibility and surprise. diff --git a/man/p_significance.lm.Rd b/man/p_significance.lm.Rd index 3e69052e2..5d8426a6c 100644 --- a/man/p_significance.lm.Rd +++ b/man/p_significance.lm.Rd @@ -75,7 +75,7 @@ probabilities, similar to the usual approach in Bayesian statistics (\emph{Greenland et al. 2022; Rafi and Greenland 2020; Schweder 2018; Schweder and Hjort 2003; Vos 2022}). -The \emph{parameters} package provides several options or functions to aid +The \strong{parameters} package provides several options or functions to aid statistical inference. These are, for example: \itemize{ \item \code{\link[=equivalence_test]{equivalence_test()}}, to compute the (conditional) equivalence test for @@ -100,7 +100,7 @@ the \strong{bayestestR} package. Most of the above shown options or functions derive from methods originally implemented for Bayesian models (\emph{Makowski et al. 2019}). However, assuming that model assumptions are met (which means, the model fits well to the data, -the correct model is chosen that reflectsa the data generating process +the correct model is chosen that reflects the data generating process (distributional model family) etc.), it seems appropriate to interpret results from classical frequentist models in a "Bayesian way" (more details: documentation in \code{\link[=p_function]{p_function()}}). diff --git a/paper/paper.bib b/paper/paper.bib index e279f20b9..c8f61d639 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -155,3 +155,107 @@ @Article{benshachar2020effecsize doi = {10.5281/zenodo.3952214}, url = {https://easystats.github.io/effectsize}, } + +@misc{lakens_improving_2022, + title = {Improving {Your} {Statistical} {Inferences}}, + copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International, Open Access}, + url = {https://zenodo.org/record/6409077}, + urldate = {2024-09-03}, + publisher = {Zenodo}, + author = {Lakens, Daniël}, + month = apr, + year = {2024}, + doi = {10.5281/ZENODO.6409077} +} + +@article{amrhein_earth_2017, + title = {The earth is flat ( \textit{p} {\textgreater} 0.05): significance thresholds and the crisis of unreplicable research}, + volume = {5}, + copyright = {http://creativecommons.org/licenses/by/4.0/}, + issn = {2167-8359}, + shorttitle = {The earth is flat ( \textit{p} {\textgreater} 0.05)}, + url = {https://peerj.com/articles/3544}, + doi = {10.7717/peerj.3544}, + language = {en}, + urldate = {2024-09-03}, + journal = {PeerJ}, + author = {Amrhein, Valentin and Korner-Nievergelt, Fränzi and Roth, Tobias}, + month = jul, + year = {2017}, + pages = {e3544} +} + +@misc{greenland_aid_2022, + title = {To {Aid} {Scientific} {Inference}, {Emphasize} {Unconditional} {Compatibility} {Descriptions} of {Statistics}}, + url = {http://arxiv.org/abs/1909.08583}, + abstract = {All scientific interpretations of statistical outputs depend on background (auxiliary) assumptions that are rarely delineated or explicitly interrogated. These include not only the usual modeling assumptions, but also deeper assumptions about the data-generating mechanism that are implicit in conventional statistical interpretations yet are unrealistic in most health, medical and social research. We provide arguments and methods for reinterpreting statistics such as P-values and interval estimates in unconditional terms, which describe compatibility of observations with an entire set of underlying assumptions, rather than with a narrow target hypothesis conditional on the assumptions. Emphasizing unconditional interpretations helps avoid overconfident and misleading inferences in light of uncertainties about the assumptions used to arrive at the statistical results. These include not only mathematical assumptions, but also those about absence of systematic errors, protocol violations, and data corruption. Unconditional descriptions introduce assumption uncertainty directly into the primary statistical interpretations of results, rather than leaving it for the discussion of limitations after presentation of conditional interpretations. The unconditional approach does not entail different methods or calculations, only different interpretation of the usual results. We view use of unconditional description as a vital component of effective statistical training and presentation. By interpreting statistical outputs in unconditional terms, researchers can avoid making overconfident statements based on statistical outputs. Instead, reports should emphasize the compatibility of results with a range of plausible explanations, including assumption violations.}, + urldate = {2022-11-10}, + publisher = {arXiv}, + author = {Greenland, Sander and Rafi, Zad and Matthews, Robert and Higgs, Megan}, + month = jul, + year = {2022}, + note = {arXiv:1909.08583 [q-bio, stat]}, + keywords = {Quantitative Biology - Quantitative Methods, Statistics - Applications, Statistics - Methodology}, +} + +@article{rafi_semantic_2020, + title = {Semantic and cognitive tools to aid statistical science: replace confidence and significance by compatibility and surprise}, + volume = {20}, + issn = {1471-2288}, + shorttitle = {Semantic and cognitive tools to aid statistical science}, + url = {https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-020-01105-9}, + doi = {10.1186/s12874-020-01105-9}, + language = {en}, + number = {1}, + urldate = {2023-03-22}, + journal = {BMC Medical Research Methodology}, + author = {Rafi, Zad and Greenland, Sander}, + month = dec, + year = {2020}, + pages = {244}, +} + +@article{schweder_confidence_2018, + title = {Confidence is epistemic probability for empirical science}, + volume = {195}, + issn = {03783758}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0378375817301738}, + doi = {10.1016/j.jspi.2017.09.016}, + language = {en}, + urldate = {2022-11-10}, + journal = {Journal of Statistical Planning and Inference}, + author = {Schweder, Tore}, + month = may, + year = {2018}, + pages = {116--125}, +} + +@incollection{schweder_frequentist_2003, + address = {Princeton}, + title = {Frequentist {Analogues} of {Priors} and {Posteriors}}, + url = {https://www.duo.uio.no/handle/10852/10425}, + language = {eng}, + urldate = {2024-09-03}, + booktitle = {Econometrics and the {Philosophy} of {Economics}: {Theory}-{Data} {Confrontations} in {Economics}}, + publisher = {Princeton University Press}, + author = {Schweder, Tore and Hjort, Nils Lid}, + editor = {Stigum, Bernt}, + year = {2003}, + pages = {285--217} +} + +@article{vos_frequentist_2022, + title = {Frequentist statistical inference without repeated sampling}, + volume = {200}, + issn = {0039-7857, 1573-0964}, + url = {https://link.springer.com/10.1007/s11229-022-03560-x}, + doi = {10.1007/s11229-022-03560-x}, + language = {en}, + number = {2}, + urldate = {2024-09-03}, + journal = {Synthese}, + author = {Vos, Paul and Holbert, Don}, + month = apr, + year = {2022}, + pages = {89} +}