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references.bib
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@Article{lme4,
title = {Fitting Linear Mixed-Effects Models Using {lme4}},
author = {Douglas Bates and Martin M{\"a}chler and Ben Bolker and Steve Walker},
journal = {Journal of Statistical Software},
year = {2015},
volume = {67},
number = {1},
pages = {1--48},
doi = {10.18637/jss.v067.i01},
}
@Manual{broom_mix,
title = {broom.mixed: Tidying Methods for Mixed Models},
author = {Ben Bolker and David Robinson},
year = {2024},
note = {R package version 0.2.9.5},
url = {https://CRAN.R-project.org/package=broom.mixed},
}
@article{broman,
title = {Data {Organization} in {Spreadsheets}},
volume = {72},
issn = {0003-1305},
url = {https://doi.org/10.1080/00031305.2017.1375989},
doi = {10.1080/00031305.2017.1375989},
abstract = {Spreadsheets are widely used software tools for data entry, storage, analysis, and visualization. Focusing on the data entry and storage aspects, this article offers practical recommendations for organizing spreadsheet data to reduce errors and ease later analyses. The basic principles are: be consistent, write dates like YYYY-MM-DD, do not leave any cells empty, put just one thing in a cell, organize the data as a single rectangle (with subjects as rows and variables as columns, and with a single header row), create a data dictionary, do not include calculations in the raw data files, do not use font color or highlighting as data, choose good names for things, make backups, use data validation to avoid data entry errors, and save the data in plain text files.},
number = {1},
journal = {The American Statistician},
author = {Broman, Karl W. and Woo, Kara H.},
month = jan,
year = {2018},
pages = {2--10},
file = {Broman and Woo - 2018 - Data Organization in Spreadsheets.pdf:C\:\\Users\\jpiaskowski\\Zotero\\storage\\DXVZDJA5\\Broman and Woo - 2018 - Data Organization in Spreadsheets.pdf:application/pdf},
}
@Article{glmmtmb,
author = {Mollie E. Brooks and Kasper Kristensen and Koen J. {van Benthem} and Arni Magnusson and Casper W. Berg and Anders Nielsen and Hans J. Skaug and Martin Maechler and Benjamin M. Bolker},
title = {{glmmTMB} Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling},
year = {2017},
journal = {The R Journal},
url = {https://journal.r-project.org/archive/2017/RJ-2017-066/index.html},
pages = {378--400},
volume = {9},
number = {2},
}
@article{greenland,
title = {Statistical tests, {P} values, confidence intervals, and power: a guide to misinterpretations},
volume = {31},
issn = {1573-7284},
url = {https://doi.org/10.1007/s10654-016-0149-3},
doi = {10.1007/s10654-016-0149-3},
abstract = {Misinterpretation and abuse of statistical tests, confidence intervals, and statistical power have been decried for decades, yet remain rampant. A key problem is that there are no interpretations of these concepts that are at once simple, intuitive, correct, and foolproof. Instead, correct use and interpretation of these statistics requires an attention to detail which seems to tax the patience of working scientists. This high cognitive demand has led to an epidemic of shortcut definitions and interpretations that are simply wrong, sometimes disastrously so—and yet these misinterpretations dominate much of the scientific literature. In light of this problem, we provide definitions and a discussion of basic statistics that are more general and critical than typically found in traditional introductory expositions. Our goal is to provide a resource for instructors, researchers, and consumers of statistics whose knowledge of statistical theory and technique may be limited but who wish to avoid and spot misinterpretations. We emphasize how violation of often unstated analysis protocols (such as selecting analyses for presentation based on the P values they produce) can lead to small P values even if the declared test hypothesis is correct, and can lead to large P values even if that hypothesis is incorrect. We then provide an explanatory list of 25 misinterpretations of P values, confidence intervals, and power. We conclude with guidelines for improving statistical interpretation and reporting.},
number = {4},
journal = {European Journal of Epidemiology},
author = {Greenland, Sander and Senn, Stephen J. and Rothman, Kenneth J. and Carlin, John B. and Poole, Charles and Goodman, Steven N. and Altman, Douglas G.},
month = apr,
year = {2016},
pages = {337--350},
file = {Greenland et al. - 2016 - Statistical tests, P values, confidence intervals,.pdf:C\:\\Users\\jpiaskowski\\Zotero\\storage\\A8UC2I48\\Greenland et al. - 2016 - Statistical tests, P values, confidence intervals,.pdf:application/pdf},
}
@Manual{dharma,
title = {DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed)
Regression Models},
author = {Florian Hartig},
year = {2022},
note = {R package version 0.4.5},
url = {https://CRAN.R-project.org/package=DHARMa},
}
@book{john_cyclic,
address = {New York},
edition = {2nd},
title = {Cyclic and {Computer} {Generated} {Designs}},
isbn = {978-0-429-17088-1},
url = {https://doi.org/10.1201/b15075},
publisher = {Chapman and Hall/CRC Press},
author = {John, JA and Williams, ER},
year = {1995},
note = {https://doi.org/10.1201/b15075},
}
@Article{lmertest,
title = {{lmerTest} Package: Tests in Linear Mixed Effects Models},
author = {Alexandra Kuznetsova and Per B. Brockhoff and Rune H. B. Christensen},
journal = {Journal of Statistical Software},
year = {2017},
volume = {82},
number = {13},
pages = {1--26},
doi = {10.18637/jss.v082.i13},
}
@Manual{emmeans,
title = {emmeans: Estimated Marginal Means, aka Least-Squares Means},
author = {Russell V. Lenth},
year = {2022},
note = {R package version 1.8.0},
url = {https://CRAN.R-project.org/package=emmeans},
}
@Article{performance,
title = {{performance}: An {R} Package for Assessment, Comparison and Testing of Statistical Models},
author = {Daniel Lüdecke and Mattan S. Ben-Shachar and Indrajeet Patil and Philip Waggoner and Dominique Makowski},
year = {2021},
journal = {Journal of Open Source Software},
volume = {6},
number = {60},
pages = {3139},
doi = {10.21105/joss.03139},
}
@article{patterson_1976,
title = {A {New} {Class} of {Resolvable} {Incomplete} {Block} {Designs}},
volume = {63},
issn = {00063444},
url = {http://www.jstor.org/stable/2335087},
doi = {10.2307/2335087},
abstract = {[This paper describes an algorithm for constructing resolvable incomplete block designs for any number of varieties v and block size k such that v is a multiple of k. These designs are called α-designs. They include as special cases some lattice and resolvable cyclic designs. Additional designs with two block sizes differing by one plot are derived by omitting one or more varieties of the α-designs. The designs are shown to be available with high efficiency factors for a wide range of parameter values.]},
number = {1},
urldate = {2024-10-14},
journal = {Biometrika},
author = {Patterson, H. D. and Williams, E. R.},
year = {1976},
note = {Publisher: [Oxford University Press, Biometrika Trust]},
pages = {83--92},
}
@Manual{nlme,
title = {nlme: Linear and Nonlinear Mixed Effects Models},
author = {José Pinheiro and Douglas Bates and {R Core Team}},
year = {2023},
note = {R package version 3.1-164},
url = {https://CRAN.R-project.org/package=nlme},
}
@Book{nlme_book,
title = {Mixed-Effects Models in S and S-PLUS},
author = {José C. Pinheiro and Douglas M. Bates},
year = {2000},
publisher = {Springer},
address = {New York},
doi = {10.1007/b98882},
}
@article{yates_1936,
title = {A new method of arranging variety trials involving a large number of varieties},
volume = {26},
journal = {J Agric Sci},
author = {Yates, F},
year = {1936},
pages = {424--455},
}
@article{pvals,
author = {Ronald L. Wasserstein and Nicole A. Lazar},
title = {The ASA Statement on p-Values: Context, Process, and Purpose},
journal = {The American Statistician},
volume = {70},
number = {2},
pages = {129--133},
year = {2016},
publisher = {ASA Website},
doi = {10.1080/00031305.2016.1154108},
URL = {https://doi.org/10.1080/00031305.2016.1154108},
eprint = {https://doi.org/10.1080/00031305.2016.1154108}
}