Archive for Dante Alighieri

inf R ! [book review]

Posted in Books, R, Travel with tags , , , , , , , , , , , on June 10, 2021 by xi'an

Thanks to my answering a (basic) question on X validated involving an R code, R mistakes and some misunderstanding about Bayesian hierarchical modelling, I got pointed out to Patrick Burns’ The R inferno. This is not a recent book as the second edition is of 2012, with a 2011 version still available on-line. Which is the version I read. As hinted by the cover, the book plays on Dante’s Inferno and each chapter is associated with a circle of Hell… Including drawings by Botticelli. The style is thus most enjoyable and sometimes hilarious. Like hell!

The first circle (reserved for virtuous pagans) is about treating integral reals as if they were integers, the second circle (attributed to gluttons, although Dante’s is for the lustful) is about allocating more space along the way, as in the question I answered and in most of my students’ codes! The third circle (allocated here to blasphemous sinners, destined for Dante’s seven circle, when Dante’s third circle is to the gluttons) points out the consequences of not vectorising, with for instance the impressive capacities of the ifelse() function [exploited to the max in R codecolfing!].  And the fourth circle (made for the lustfuls rather than Dante’s avaricious and prodigals) is a short warning about the opposite over-vectorising. Circle five (destined for the treasoners, and not Dante’s wrathfuls) pushes for and advises about writing R functions. Circle six recovers Dante’s classification, welcoming (!) heretics, and prohibiting global assignments, in another short chapter. Circle seven (alloted to the simoniacs—who should be sharing the eight circle with many other sinners—rather than the violents as in Dante’s seventh) discusses object attributes, with the distinction between S3 and S4 methods somewhat lost on me. Circle eight (targeting the fraudulents, as in Dante’s original) is massive as it covers “a large number of ghosts, chimeras and devils”, a collection of difficulties and dangers and freak occurences, with the initial warning that “It is a sin to assume that code does what is intended”. A lot of these came as surprises to me and I was rarely able to spot the difficulty without the guidance of the book. Plenty to learn from these examples and counter-examples. Reaching Circle nine (where live (!) the thieves, rather than Dante’s traitors). A “special place for those who feel compelled to drag the rest of us into hell.” Discussing the proper ways to get help on fori. Like Stack Exchange. Concluding with the tongue-in-cheek comment that “there seems to be positive correlation between a person’s level of annoyance at [being asked several times the same question] and ability to answer questions.” This being a hidden test, right?!, as the correlation should be negative.

abandon all o(p) ye who enter here

Posted in Books, Statistics, University life with tags , , , , , , on September 28, 2017 by xi'an

Today appeared on arXiv   a joint paper by Blakeley McShane, David Gal, Andrew Gelman, Jennifer Tackett, and myself, towards the abandonment of significance tests, which is a response to the 72 author paper in Nature Methods that recently made the news (and comments on the ‘Og). Some of these comments have been incorporated in the paper, along with others more on the psychology testing side. From the irrelevance of point null hypotheses to the numerous incentives for multiple comparisons, to the lack of sufficiency of the p-value itself, to the limited applicability of the uniformly most powerful prior principle…

“…each [proposal] is a purely statistical measure that fails to take a more holistic view of the evidence that includes the consideration of the traditionally neglected factors, that is, prior and related evidence, plausibility of mechanism, study design and data quality, real world costs and benefits, novelty of finding, and other factors that vary by research domain.”

One may wonder about this list of grievances and its impact on statistical practice. The paper however suggests two alternatives, one being to investigate the potential impact of (neglected) factors rather than relying on thresholds. Another one, maybe less realistic, unless it is the very same, is to report the entirety of the data associated with the experiment. This makes the life of journal editors and grant evaluators harder, possibly much harder, but it indeed suggests an holistic and continuous approach to data analysis, rather than the mascarade of binary outputs. (Not surprisingly, posting this item of news on Andrew’s blog a few hours ago generated a large amount of discussion.)