knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" ) library(PRDA)

{PRDA} allows performing a prospective or retrospective design analysis to evaluate inferential risks (i.e., power, Type M error, and Type S error) in a study considering Pearson's correlation between two variables or mean comparisons (one-sample, paired, two-sample, and Welch's *t*-test).

For an introduction to design analysis and a general overview of the package see `vignette("PRDA")`

.
Examples for retrospective design analysis and prospective design analysis are provided in `vignette("retrospective")`

and `vignette("prospective")`

respectively.

All the documentation is available at https://claudiozandonella.github.io/PRDAbeta/.

You can install the development version from GitHub with:

# install.packages("devtools") devtools::install_github("ClaudioZandonella/PRDAbeta", ref = "develop", build_vignettes = TRUE)

{PRDA} package can be used for Pearson's correlation between two variables or mean comparisons (one-sample, paired, two-sample, and Welch's t-test) considering a hypothetical value of *ρ* or Cohen's *d* respectively. See `vignette("retrospective")`

and `vignette("prospective")`

to know how to set function arguments for the different effect types.

In {PRDA} there are two main functions `retrospective()`

and `prospective()`

.

`retrospective()`

Given the hypothetical population effect size and the study sample size, the function `retrospective()`

performs a retrospective design analysis. According to the defined alternative hypothesis and the significance level, the inferential risks (i.e., Power level, Type M error, and Type S error) are computed together with the critical effect value (i.e., the minimum absolute effect size value that would result significant).

Consider a study that evaluated the correlation between two variables with a sample of 30 subjects. Suppose that according to the literature the hypothesized effect is *ρ* = .25. To evaluate the inferential risks related to the study we use the function `retrospective()`

.

retrospective(effect_size = .25, sample_n1 = 30, effect_type = "correlation", test_method = "pearson", seed = 2020)

In this case, the statistical power is almost 30% and the associated Type M error and Type S error are respectively around 1.80 and 0.003. That means, statistical significant results are on average an overestimation of 80% of the hypothesized population effect and there is a .3% of probability to obtain a statistically significant result in the opposite direction.

To know more about function arguments and further examples see the function documentation `?retrospective`

and `vignette("retrospective")`

.

`prospective()`

Given the hypothetical population effect size and the required power level, the function `prospective()`

performs a prospective design analysis. According to the defined alternative hypothesis and the significance level, the required sample size is computed together with the associated Type M error, Type S error, and the critical effect value (i.e., the minimum absolute effect size value that would result significant).

Consider a study that will evaluate the correlation between two variables. Knowing from the literature that we expect an effect size of *ρ* = .25, the function `prospective()`

can be used to compute the required sample size to obtain a power of 80%.

prospective(effect_size = .25, power = .80, effect_type = "correlation", test_method = "pearson", display_message = FALSE, seed = 2020)

The required sample size is $n=126$, the associated Type M error is around 1.10 and the Type S error is approximately 0.

To know more about function arguments and further examples see the function documentation `?prospective`

and `vignette("prospective")`

.

The hypothetical population effect size can be defined as a single value according to previous results in the literature or experts indications. Alternatively, {PRDA} allows users to specify a distribution of plausible values to account for their uncertainty about the hypothetical population effect size. To know how to specify the hypothetical effect size according to a distribution and an example of application see `vignette("retrospective")`

.

To propose a new feature or report a bug, please open an issue on GitHub.

To cite {PRDA} in publications use:

Claudio Zandonella Callegher, Massimiliano Pastore, Angela Andreella, Anna Vesely, Enrico Toffalini, Giulia Bertoldo, & Gianmarco Altoè. (2020). PRDA: Prospective and Retrospective Design Analysis (Version v0.1). Zenodo. http://doi.org/10.5281/zenodo.3630733

A BibTeX entry for LaTeX users is

@Misc{, title = {{PRDA}: Prospective and Retrospective Design Analysis}, author = {Claudio {Zandonella Callegher} and Massimiliano Pastore and Angela Andreella and Anna Vesely and Enrico Toffalini and Giulia Bertoldo and Gianmarco Altoè}, year = {2020}, publisher = {Zenodo}, version = {v0.1}, doi = {10.5281/zenodo.3630733}, url = {https://doi.org/10.5281/zenodo.3630733}, }

nocite: | @altoeEnhancingStatisticalInference2020, @bertoldoDesigningStudiesEvaluating2020, @gelmanPowerCalculationsAssessing2014 ...

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