Geomorph users,Our function plotTangentSpace() performs a Principal Components Analysis (PCA) of shape variation and plots two dimensions of tangent space for a set of Procrustes-aligned specimens and also returns the shape cha...

Not all Principal Component Analysis (PCA) (also called Empirical Orthogonal Function analysis, EOF) approaches are equal when it comes to dealing with a data field that contain missing values (i.e. "gappy"). The following post compares several methods by assessing the accuracy of the derived PCs to reconstruct the "true" data set, as was similarly...

nIntroductionnI work in consulting. If you're a consultant at a certain type of company, agency, organization, consultancy, whatever, this can sometimes mean travelling a lot.nnMany business travellers 'in the know' have heard the old joke that if you want to stay at any type of hotel anywhere in the world and get a great rate, all you have to...

Authors: Jan Smycka, Petr Keil This post introduces experimental R package bPCA which we developed with Jan Smycka, who actually came with the idea. We do not guarantee the very idea to be correct and there certainly are bugs – we invite anyone to show us wrong, or to contribute. … Continue reading →

Principal component analysis(PCA) is one of the classical methods in multivariate statistics. In addition, it is now widely used as a way to implement data-processing and dimension-reduction. Besides statistics, there are numerous applications about PCA in engineering, biology, and so on. There are two main optimal properties of PCA, which are guaranteeing minimal information loss and uncorrelated principal components. That's … Continue reading...

Bioinformatics is becoming more and more a Data Mining field. Every passing day, Genomics and Proteomics yield bucketloads of multivariate data (genes, proteins, DNA, identified peptides, structures), and every one of these biological data units are described by a number of features: length, physicochemical properties, scores, etc. Careful consideration of which features to select when trying...

Working with wide data is already hard enough, add to this row outliers and things can get murky fast. Here is an example of an anlysis of a wide data set, 24 rows x 84 columns. Using imDEV, written in R, to calculate and visualize a principal components analysis (PCA) on this data set. We find that

I found the following post regarding the anomalous metal object observed in a Curiosity Rover photo to be fascinating - specifically, the clever ways that some programmers used for filtering the image for the object. The following answer on mathematica.stackexchange.com was especially illuminating for its use of a multivariate distribution to...