README.md
In sjpierce/piercer: Functions for Research and Statistical Computing
piercer
The goal of piercer is to
provide a set of functions and files written for use in my personal
research and statistical collaboration and consulting work. Packaging
them is a learning experience, a way to shorten my other scripts, and a
way to increase quality, reproducibility, and efficiency.
Installation
This package is not yet available from
CRAN. Releasing it there is a long-term
goal. Meanwhile, the development version of piercer can be installed
from the source stored in a public GitHub
repository using the devtools package.
Precompiled binary installer files are not available at this time, so
installing from the source code is the only option.
Order of Installation Tasks
Before installing piercer, make sure you have:
- Installed R 4.1.0 or later. You can get the most recent version of R
from the Comprehensive R Archive Network
(CRAN).
- Installed any tools required for compiling packages (they will be
specific to your operating system). These will be necessary for the
devtools package to work.
- On Windows, see https://cran.r-project.org/bin/windows/Rtools/.
- On Mac OS X, see https://cran.r-project.org/bin/macosx/tools and
https://mac.R-project.org/tools/.
- Installed the devtools package. The most recent CRAN release of
devtools will likely
be more stable but sometimes you may instead need the development
version at GitHub.
- Updated your R packages. You may be prompted with a dialog box asking
“Do you want to install from sources the packages which need
compilation?” It usually works fine if I choose “no”. Occasionally, it
appears necessary to choose “yes”, but I am more likely to run into
problems when doing that.
- After those tasks are done, you can install piercer.
If you only need piercer because a script you want to run loads the
package and uses one of its functions, then type the following code in
the R console to install the package to your package library. You will
need internet access for this to work.
devtools::install_github("sjpierce/piercer")
That should enable you to use the functions built into the package.
Depending on how your computer and R installation are configured, that
may either install R to the main R package library, or to a personal
package library. If you do not have write permissions to the main R
package library, then R will ask if you want to use a personal package
library instead.
Example
One thing I have been contemplating recently is how to implement some of
the American Statistical Association’s recent recommendations on moving
away from interpreting p-values in terms of statistical significance
(Wasserstein, Schirm, & Lazar, 2019). I found the suggestions to convert
p-values into s-values (Greenland, 2019), Bayes Factor Bounds (BFBs;
Benjamin & Berger, 2019), and posterior probabilities that H1 is true
(Benjamin & Berger, 2019) interesting, so I wrote some functions to do
those conversions.
The example below shows how to apply my convertp function. I use one
of the examples for the base R ttest function to demonstrate it.
library(piercer)
tresult <- t.test(1:10, y = c(7:20))
tresult
#>
#> Welch Two Sample t-test
#>
#> data: 1:10 and c(7:20)
#> t = -5.4349, df = 21.982, p-value = 1.855e-05
#> alternative hypothesis: true difference in means is not equal to 0
#> 95 percent confidence interval:
#> -11.052802 -4.947198
#> sample estimates:
#> mean of x mean of y
#> 5.5 13.5
# Convert p-value into s-value, BFB, & posterior probability that H1 is true.
convertp(p = tresult$p.value, digits = 3)
#> S BFB PPH1
#> 1 15.718 1820.006 0.999
References
Benjamin, D. J., & Berger, J. O. (2019). Three recommendations for
improving the use of p-values. The American Statistician,
73(Supplement 1), 186-191.
doi:10.1080/00031305.2018.1543135
Bryan, J., & Hester, J. (n.d.). What they forgot to teach you about R.
https://rstats.wtf
Greenland, S. (2019). Valid p-values behave exactly as they should: Some
misleading criticisms of p-values and their resolution with s-values.
The American Statistician, 73(Supplement 1), 106-114.
doi:10.1080/00031305.2018.1529625
Wasserstein, R. L., Schirm, A. L., & Lazar, N. A. (2019). Moving to a
world beyond “p \< .05”. The American Statistician, 73(Supplement 1),
1-19.
doi:10.1080/00031305.2019.1583913
Task List
- Add assertthat checks to more functions.
- Add more testthat unit tests for functions.
sjpierce/piercer documentation built on Dec. 30, 2024, 3:28 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
piercer
The goal of piercer is to provide a set of functions and files written for use in my personal research and statistical collaboration and consulting work. Packaging them is a learning experience, a way to shorten my other scripts, and a way to increase quality, reproducibility, and efficiency.
Installation
This package is not yet available from CRAN. Releasing it there is a long-term goal. Meanwhile, the development version of piercer can be installed from the source stored in a public GitHub repository using the devtools package. Precompiled binary installer files are not available at this time, so installing from the source code is the only option.
Order of Installation Tasks
Before installing piercer, make sure you have:
- Installed R 4.1.0 or later. You can get the most recent version of R from the Comprehensive R Archive Network (CRAN).
- Installed any tools required for compiling packages (they will be specific to your operating system). These will be necessary for the devtools package to work.
- On Windows, see https://cran.r-project.org/bin/windows/Rtools/.
- On Mac OS X, see https://cran.r-project.org/bin/macosx/tools and https://mac.R-project.org/tools/.
- Installed the devtools package. The most recent CRAN release of devtools will likely be more stable but sometimes you may instead need the development version at GitHub.
- Updated your R packages. You may be prompted with a dialog box asking “Do you want to install from sources the packages which need compilation?” It usually works fine if I choose “no”. Occasionally, it appears necessary to choose “yes”, but I am more likely to run into problems when doing that.
- After those tasks are done, you can install piercer.
If you only need piercer because a script you want to run loads the package and uses one of its functions, then type the following code in the R console to install the package to your package library. You will need internet access for this to work.
devtools::install_github("sjpierce/piercer")
That should enable you to use the functions built into the package. Depending on how your computer and R installation are configured, that may either install R to the main R package library, or to a personal package library. If you do not have write permissions to the main R package library, then R will ask if you want to use a personal package library instead.
Example
One thing I have been contemplating recently is how to implement some of the American Statistical Association’s recent recommendations on moving away from interpreting p-values in terms of statistical significance (Wasserstein, Schirm, & Lazar, 2019). I found the suggestions to convert p-values into s-values (Greenland, 2019), Bayes Factor Bounds (BFBs; Benjamin & Berger, 2019), and posterior probabilities that H1 is true (Benjamin & Berger, 2019) interesting, so I wrote some functions to do those conversions.
The example below shows how to apply my convertp function. I use one of the examples for the base R ttest function to demonstrate it.
library(piercer)
tresult <- t.test(1:10, y = c(7:20))
tresult
#>
#> Welch Two Sample t-test
#>
#> data: 1:10 and c(7:20)
#> t = -5.4349, df = 21.982, p-value = 1.855e-05
#> alternative hypothesis: true difference in means is not equal to 0
#> 95 percent confidence interval:
#> -11.052802 -4.947198
#> sample estimates:
#> mean of x mean of y
#> 5.5 13.5
# Convert p-value into s-value, BFB, & posterior probability that H1 is true.
convertp(p = tresult$p.value, digits = 3)
#> S BFB PPH1
#> 1 15.718 1820.006 0.999
References
Benjamin, D. J., & Berger, J. O. (2019). Three recommendations for improving the use of p-values. The American Statistician, 73(Supplement 1), 186-191. doi:10.1080/00031305.2018.1543135
Bryan, J., & Hester, J. (n.d.). What they forgot to teach you about R. https://rstats.wtf
Greenland, S. (2019). Valid p-values behave exactly as they should: Some misleading criticisms of p-values and their resolution with s-values. The American Statistician, 73(Supplement 1), 106-114. doi:10.1080/00031305.2018.1529625
Wasserstein, R. L., Schirm, A. L., & Lazar, N. A. (2019). Moving to a world beyond “p \< .05”. The American Statistician, 73(Supplement 1), 1-19. doi:10.1080/00031305.2019.1583913
Task List
- Add assertthat checks to more functions.
- Add more testthat unit tests for functions.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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