Digital heritage
Author: Luis (Page 49 of 66)
Split-plot 2: let’s throw in some spatial effects
Disappeared for a while collecting frequent flyer points. In the process I ‘discovered’ that I live in the middle of nowhere, as it took me 36 hours to reach my conference destination (Estoril, Portugal) through Christchurch, Sydney, Bangkok, Dubai, Madrid and Lisbon.
Where was I? Showing how split-plots look like under the bonnet (hood for you US readers). Yates presented a nice diagram of his oats data set in the paper, so we have the spatial location of each data point which permits us playing with within-trial spatial trends.
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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.
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?