The entry collects short notes, quotes, and links.

đź“Ś rlang provides done() to break out of a loop.

Paul recommended using Frank Harrell’s rms::orm() function for ordinal regression.

đź“„ This paper uses by-word entropy as a measure of intelligibility. https://doi.org/10.1017/S0305000921000714

đź’ˇ Monads in one sentence:

A monad is the minimum amount of structure needed to overload function composition in a way that “performs an extra computation” on the intermediate value. – https://www.youtube.com/watch?v=Nq-q2USYetQ&feature=youtu.be

I had these quotes in some old notes:

In so complex a thing as human nature, we must consider, it is hard to find rules without exception. — George Eliot

If any one faculty of our nature may be called more wonderful than the rest, I do think it is memory…The memory is sometimes so retentive, so serviceable, so obedient; at others, so bewildered and so weak. We are, to be sure, a miracle every way; but our powers of recollecting and of forgetting do seem peculiarly past finding out. — Jane Austen

Asked twitter if they knew any IRR tutorials

R 3.6.0 released! (2019-04-26)

Highlights for me

  • New sample() implementation
  • New function asplit() allow splitting an array or matrix by its margins.
  • Functions mentioned I didn’t know about: lengths(), trimws(), extendrange(), convertColor(), strwidth()

Phylogenetic regression (2019-02-26)

Listened to bits of McElreath’s phylogenetic regression lecture.

  • You can model a simple linear regression as a multivariate regression.
  • Make the covariance matrix the identity matrix and multiple it by the error term sigma.
  • In this formulation, you can swap out the identity correlation matrix with something estimated using a correlation/distance matrix. If the distances were age, you can have units with similar ages have correlated errors. Now you have a gaussian process regression.

multicomp (2019-02-25)

  • We are using the multcomp package and glht() to test hypotheses from fitted models. Never used this package before. Something to learn.
  • glht() will compute a group difference (like asymptote of SMI-LCT vs SMI-LCI) from a fitted model and give you a standard error, z statistic and p value for that difference.
  • I can get very similar results by sampling the multivariate normal distribution of the model coefficients/variance-covariance matrix and computing the group differences from the samples, using the standard deviation of the samples to get the standard error.

Old rolling list of bookmarks

dtool: Manage scientific data https://dtool.readthedocs.io/en/latest/

A very first introduction to Hamiltonian Monte Carlo https://blogs.rstudio.com/tensorflow/posts/2019-10-03-intro-to-hmc/

Some things you maybe didn’t know about linear regression https://ryxcommar.com/2019/09/06/some-things-you-maybe-didnt-know-about-linear-regression/

dbx database tools for R https://github.com/ankane/dbx

Dash for R https://medium.com/@plotlygraphs/announcing-dash-for-r-82dce99bae13

How to interpret F-statistic https://stats.stackexchange.com/questions/12398/how-to-interpret-f-and-p-value-in-anova

The origin of statistically significant https://www.johndcook.com/blog/2008/11/17/origin-of-statistically-significant/

tidymv: Tidy Model Visualisation for Generalised Additive Models https://cran.r-project.org/web/packages/tidymv/index.html

Step-by-step examples of building publication-quality figures in ggplot2 https://github.com/clauswilke/practical_ggplot2

From data to viz https://www.data-to-viz.com/

Shapley model explanation https://github.com/slundberg/shap

JavaScript versus Data Science https://software-tools-in-javascript.github.io/js-vs-ds/en/

Penalized likelihood estimation https://modernstatisticalworkflow.blogspot.com/2017/11/what-is-likelihood-anyway.html

UTF-8 everywhere https://utf8everywhere.org/

Unicode programming https://begriffs.com/posts/2019-05-23-unicode-icu.html

R package to simulate colorblindness https://github.com/clauswilke/colorblindr

Data version control https://dvc.org/

Email tips https://twitter.com/LucyStats/status/1131285346455625734?s=20

colorcet library (python) https://colorcet.pyviz.org/

HCL wizard http://hclwizard.org/hclwizard/

Coloring for colorblindness. Has 8 palettes of color pairs https://davidmathlogic.com/colorblind/

5 things to consider when creating your CSS style guide by @malimirkeccita https://medium.com/p/5-things-to-consider-when-creating-your-css-style-guide-7b85fa70039d

Tesseract OCR engine for R https://cran.r-project.org/web/packages/tesseract/vignettes/intro.html

Lua filters for rmarkdown documents https://github.com/crsh/rmdfiltr

An NIH Rmd template https://github.com/tgerke/nih-rmd-template

Commit message guide https://github.com/RomuloOliveira/commit-messages-guide

Linear regression diagnostic plots in ggplot2 https://github.com/yeukyul/lindia

A graphical introduction to dynamic programming https://avikdas.com/2019/04/15/a-graphical-introduction-to-dynamic-programming.html

Why software projects take longer than you think https://erikbern.com/2019/04/15/why-software-projects-take-longer-than-you-think-a-statistical-model.html

Automatic statistical reporting https://github.com/easystats/report

Multilevel models and CSD https://pubs.asha.org/doi/pdf/10.1044/2018_JSLHR-S-18-0075

Map of cognitive science http://www.riedlanna.com/cognitivesciencemap.html

An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data https://www.nature.com/articles/s41467-019-09785-8

Common statistical tests are linear models (or: how to teach stats) https://lindeloev.github.io/tests-as-linear/

Monte Carlo sampling does not “explore” the posterior https://statmodeling.stat.columbia.edu/2019/03/25/mcmc-does-not-explore-posterior/

How to develop the five skills that will make you a great analyst https://mode.com/blog/how-to-develop-the-five-soft-skills-that-will-make-you-a-great-analyst

Confidence intervals are a ring toss https://twitter.com/epiellie/status/1073385427317465089

Mathematics for Machine Learning https://mml-book.github.io/

20 Tips for Senior Thesis Writers http://hwpi.harvard.edu/files/complit/files/twenty_tips_for_senior_thesis_writers_revised_august_2012.pdf

Comparing common analysis strategies for repeated measures data http://eshinjolly.com/2019/02/18/rep_measures/

Cosine similarity, Pearson correlation, and OLS coefficients https://brenocon.com/blog/2012/03/cosine-similarity-pearson-correlation-and-ols-coefficients/

qqplotr is a nice package for plotting qqplots https://cran.r-project.org/web/packages/qqplotr/index.html

Multidimensional item response theory https://github.com/philchalmers/mirt

Aki’s tutorials/materials on model selection https://github.com/avehtari/modelselection_tutorial

An Introverts Guide to Conferences https://laderast.github.io/2018/05/17/a-introvert-s-survival-guide-to-conferences/

Best practice guidance for linear mixed-effects models in psychological science https://psyarxiv.com/h3duq/

Viewing matrices and probabilities as graphs https://www.math3ma.com/blog/matrices-probability-graphs

Cross-validation for hierarchical models https://avehtari.github.io/modelselection/rats_kcv.html

All of Aki’s tutorials https://avehtari.github.io/modelselection/

User-friendly p values http://thenode.biologists.com/user-friendly-p-values/research/

Iodide is a Javascript notebook https://alpha.iodide.io/

Interesting question about what do when transformation changes the “test” of a highest-density interval. https://discourse.mc-stan.org/t/exponentiation-or-transformation-of-point-estimates/7848

Some ways to rethink statistical rules https://allendowney.blogspot.com/2015/12/many-rules-of-statistics-are-wrong.html

Toward a Principled Bayesian Workflow https://betanalpha.github.io/assets/case_studies/principled_bayesian_workflow.html

Stumbled across an article on mixed models and effect sizes: https://www.journalofcognition.org/articles/10.5334/joc.10/

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