{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)
#>
#> Design Analysis
#>
#> Hypothesized effect: rho = 0.25
#>
#> Study characteristics:
#> test_method sample_n1 sample_n2 alternative sig_level df
#> pearson 30 NULL two_sided 0.05 28
#>
#> Inferential risks:
#> power typeM typeS
#> 0.27 1.826 0.003
#>
#> Critical value(s): rho = ± 0.361
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)
#>
#> Design Analysis
#>
#> Hypothesized effect: rho = 0.25
#>
#> Study characteristics:
#> test_method sample_n1 sample_n2 alternative sig_level df
#> pearson 126 NULL two_sided 0.05 124
#>
#> Inferential risks:
#> power typeM typeS
#> 0.807 1.107 0
#>
#> Critical value(s): rho = ± 0.175
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},
}
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