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contrasts.bib
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contrasts.bib
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@article{ii_factorplot:_2013,
title = {factorplot: {Improving} {Presentation} of {Simple} {Contrasts} in {GLMs}},
volume = {5},
url = {http://journal.r-project.org/archive/2013-2/RJournal_2013-2_armstrong.pdf},
number = {2},
journal = {The R Journal},
author = {II, David A. Armstrong},
month = dec,
year = {2013},
pages = {4--16},
}
@article{schad_how_2018,
title = {How to capitalize on a priori contrasts in linear (mixed) models: {A} tutorial},
shorttitle = {How to capitalize on a priori contrasts in linear (mixed) models},
url = {http://arxiv.org/abs/1807.10451},
abstract = {Factorial experiments in research on memory, language, and in other areas are often analyzed using analysis of variance (ANOVA). However, for experimental factors with more than two levels, the ANOVA omnibus F-test is not informative about the source of a main effect or interaction. This is unfortunate as researchers typically have specific hypotheses about which condition means differ from each other. A priori contrasts (i.e., comparisons planned before the sample means are known) between specific conditions or combinations of conditions are the appropriate way to represent such hypotheses in the statistical model. Many researchers have pointed out that contrasts should be "tested instead of, rather than as a supplement to, the ordinary `omnibus' F test" (Hayes, 1973, p. 601). In this tutorial, we explain the mathematics underlying different kinds of contrasts (i.e., treatment, sum, repeated, Helmert, and polynomial contrasts), discuss their properties, and demonstrate how they are applied in the R System for Statistical Computing (R Core Team, 2018). In this context, we explain the generalized inverse which is needed to compute the weight coefficients for contrasts that test hypotheses that are not covered by the default set of contrasts. A detailed understanding of contrast coding is crucial for successful and correct specification in linear models (including linear mixed models). Contrasts defined a priori yield far more precise confirmatory tests of experimental hypotheses than standard omnibus F-test.},
urldate = {2018-09-10},
journal = {arXiv:1807.10451 [stat]},
author = {Schad, Daniel J. and Hohenstein, Sven and Vasishth, Shravan and Kliegl, Reinhold},
month = jul,
year = {2018},
note = {arXiv: 1807.10451},
keywords = {Statistics - Methodology},
annote = {Comment: 54 pages, 4 figures in main text, 1 figure in appendix},
file = {arXiv\:1807.10451 PDF:/home/bolker/Documents/zotero_new/storage/WU4VJNID/Schad et al. - 2018 - How to capitalize on a priori contrasts in linear .pdf:application/pdf;arXiv.org Snapshot:/home/bolker/Documents/zotero_new/storage/YPA8WMCU/1807.html:text/html},
}
@article{davis_contrast_2010,
title = {Contrast {Coding} in {Multiple} {Regression} {Analysis}: {Strengths}, {Weaknesses}, and {Utility} of {Popular} {Coding} {Structures}},
volume = {8},
shorttitle = {Contrast {Coding} in {Multiple} {Regression} {Analysis}},
journal = {Journal of Data Science},
author = {Davis, M.J.},
year = {2010},
pages = {61--73},
file = {Google Scholar Linked Page:/home/bolker/Documents/zotero_new/storage/6GN68ZT7/contrast-coding-multiple-regression-analysis-strengths-weaknesses-utility-popular-coding-structures-2.html:text/html},
}
@techreport{law_guide_2020,
title = {A guide to creating design matrices for gene expression experiments},
copyright = {http://creativecommons.org/licenses/by/4.0/},
url = {https://f1000research.com/articles/9-1444},
abstract = {Differential expression analysis of genomic data types, such as RNA-sequencing experiments, use linear models to determine the size and direction of the changes in gene expression. For RNA-sequencing, there are several established software packages for this purpose accompanied with analysis pipelines that are well described. However, there are two crucial steps in the analysis process that can be a stumbling block for many -- the set up an appropriate model via design matrices and the set up of comparisons of interest via contrast matrices. These steps are particularly troublesome because an extensive catalogue for design and contrast matrices does not currently exist. One would usually search for example case studies across different platforms and mix and match the advice from those sources to suit the dataset they have at hand. This article guides the reader through the basics of how to set up design and contrast matrices. We take a practical approach by providing code and graphical representation of each case study, starting with simpler examples (e.g. models with a single explanatory variable) and move onto more complex ones (e.g. interaction models, mixed effects models, higher order time series and cyclical models). Although our work has been written specifically with a limma -style pipeline in mind, most of it is also applicable to other software packages for differential expression analysis, and the ideas covered can be adapted to data analysis of other high-throughput technologies. Where appropriate, we explain the interpretation and differences between models to aid readers in their own model choices. Unnecessary jargon and theory is omitted where possible so that our work is accessible to a wide audience of readers, from beginners to those with experience in genomics data analysis.},
language = {en},
number = {9:1444},
urldate = {2022-02-12},
institution = {F1000Research},
author = {Law, Charity W. and Zeglinski, Kathleen and Dong, Xueyi and Alhamdoosh, Monther and Smyth, Gordon K. and Ritchie, Matthew E.},
month = dec,
year = {2020},
doi = {10.12688/f1000research.27893.1},
note = {Type: article},
keywords = {contrast matrix, Design matrix, gene expression analysis, model matrix, statistical models},
file = {Full Text PDF:/home/bolker/Documents/zotero_new/storage/IFBRKNJZ/Law et al. - 2020 - A guide to creating design matrices for gene expre.pdf:application/pdf},
}
@techreport{soneson_exploremodelmatrix_2020,
title = {{ExploreModelMatrix}: {Interactive} exploration for improved understanding of design matrices and linear models in {R}},
copyright = {http://creativecommons.org/licenses/by/4.0/},
shorttitle = {{ExploreModelMatrix}},
url = {https://f1000research.com/articles/9-512},
abstract = {Linear and generalized linear models are used extensively in many scientific fields, to model observed data and as the basis for hypothesis tests. The use of such models requires specification of a design matrix, and subsequent formulation of contrasts representing scientific hypotheses of interest. Proper execution of these steps requires a thorough understanding of the meaning of the individual coefficients, and is a frequent source of uncertainty for end users. Here, we present an R/Bioconductor package, ExploreModelMatrix , which enables interactive exploration of design matrices and linear model diagnostics. Given a sample data table and a desired design formula, the package displays how the model coefficients are combined to give the fitted values for each combination of predictor variables, which allows users to both extract the interpretation of each individual coefficient, and formulate desired linear contrasts. In addition, the interactive interface displays informative characteristics for the regular linear model corresponding to the provided design, such as variance inflation factors and the pseudoinverse of the design matrix. We envision the package and the built-in collection of common types of linear model designs to be useful for teaching and self-learning purposes, as well as for assisting more experienced users in the interpretation of complex model designs.},
language = {en},
number = {9:512},
urldate = {2022-02-12},
institution = {F1000Research},
author = {Soneson, Charlotte and Marini, Federico and Geier, Florian and Love, Michael I. and Stadler, Michael B.},
month = sep,
year = {2020},
doi = {10.12688/f1000research.24187.2},
note = {Type: article},
keywords = {Design Matrix, Experimental Design, Interactivity, Linear Model, R, Shiny},
file = {Full Text PDF:/home/bolker/Documents/zotero_new/storage/BD94N6AG/Soneson et al. - 2020 - ExploreModelMatrix Interactive exploration for im.pdf:application/pdf},
}
@misc{venables_codingmatrices_2021,
title = {{codingMatrices}: {Alternative} {Factor} {Coding} {Matrices} for {Linear} {Model} {Formulae}},
url = {https://CRAN.R-project.org/package=codingMatrices},
author = {Venables, Bill},
year = {2021},
annote = {R package version 0.3.3},
}
@misc{debruine_faux_2021,
title = {faux: {Simulation} for {Factorial} {Designs}},
url = {https://debruine.github.io/faux/},
publisher = {Zenodo},
author = {DeBruine, Lisa},
year = {2021},
doi = {10.5281/zenodo.2669586},
annote = {R package version 1.1.0},
}