Guest essay by Dr. Richard Tol
In their eagerness to discredit a colleague Harvey et al. (2017) got ahead of themselves. The write-up shows signs of haste – typographical errors (“principle component analysis”, “refereces cited”) and nonsensical statements (“95% normal probability”) escaped the attention of the 14 authors, 3 referees and editor – but so does the analysis. The paper does three things: It creates a database, it classifies subjects, and it conducts a principal component analysis. Details have not been shared on the database construction or the classification (Lewandowsky and Bishop 2016), so I focus on the principal component analysis.
Principal component analysis (PCA) aims to reduce the dimension of a dataset by a linear transformation of its variables into orthogonal components and limiting the attention to those components that are principal in explaining the variation in the variables. Harvey et al. (2017) reduce seven variables to two. One variable…
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