Evolving notes, images and sounds by Luis Apiolaza

Author: Luis (Page 48 of 64)

Split-plot 1: How does a linear mixed model look like?

I like statistics and I struggle with statistics. Often times I get frustrated when I don’t understand and I really struggled to make sense of Krushke’s Bayesian analysis of a split-plot, particularly because ‘it didn’t look like’ a split-plot to me.

Additionally, I have made a few posts discussing linear mixed models using several different packages to fit them. At no point I have shown what are the calculations behind the scenes. So, I decided to combine my frustration and an explanation to myself in a couple of posts. This is number one and the follow up is Split-plot 2: let’s throw in some spatial effects.
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Review: “Forest Analytics with R: an introduction”

Forestry is the province of variability. From a spatial point of view this variability ranges from within-tree variation (e.g. modeling wood properties) to billions of trees growing in millions of hectares (e.g. forest inventory). From a temporal point of view we can deal with daily variation in a physiological model to many decades in an empirical growth and yield model. Therefore, it is not surprising that there is a rich tradition of statistical applications to forestry problems.

At the same time, the scope of statistical problems is very diverse. As the saying goes forestry deals with “an ocean of knowledge, but only one centimeter deep”, which is perhaps an elegant way of saying a jack of all trades, master of none. Forest Analytics with R: an introduction by Andrew Robinson and Jeff Hamann (FAWR hereafter) attempts to provide a consistent overview of typical statistical techniques in forestry as they are implemented using the R statistical system.

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End of May flotsam

The end is near! At least the semester is coming to an end, so students have crazy expectations like getting marks back for assignments, and administrators want to see exam scripts. Sigh! What has been happening meanwhile in Quantum Forest?

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On point of view

Often times we experience mental paralysis: we can only see a problem, a situation or a person from a single point of view (mea culpa). Some times the single mindedness of our view point becomes so bad that we inexorably drift to complete silliness. This is the case when one keeps on insisting on a point that has been shown to be, how to put it, wrong.

Photography is a fascinating hobby. I think it was around 30 years ago, may be a bit earlier, that I started taking it more seriously. Learned to process film and to use an enlarger and to witness the magic of an image slowly appearing on paper submerged in developer, while a dim red light bathed the room. A few years later I stopped taking pictures, mostly due to economic problems: I was not able to even buy film, let alone to process it. Photography stayed dormant for many years, then resurfaced in the digital area, but it did not feel the same.

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R’s increasing popularity. Should we care?

Some people will say ‘you have to learn R if you want to get a job doing statistics/data science’. I say bullshit, you have to learn statistics and learn to work in a variety of languages if you want to be any good, beyond getting a job today coding in R.

R4stats has a recent post discussing the increasing popularity of R against other statistical software, using citation counts in Google Scholar. It is a flawed methodology, at least as flawed as other methodologies used to measure language popularities. Nevertheless, I think is hard to argue against the general trend: R is becoming more popular. There is a deluge of books looking at R from every angle, thousands of packages and many jobs openings asking for R experience, which prompts the following question:

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