I'm a huge supporter of the Free and Open Source Software movement. I've written more about R than anything else on this blog, all the code I post here is free and open-source, and a while back I invited you to steal this blog under a cc-by-sa license....
We quite regularly use genetic algorithms to optimise over the ad-hoc functions we develop when trying to solve problems in applied mathematics. However it’s a bit disconcerting to have your algorithm roam through a high dimensional solution space while not being able to picture what it’s doing or how close one solution is to another. … Continue reading...
As a developer on the vegan package for R, one of the most FAQs is how to customise ordination diagrams, usually to colour the sample points according to an external grouping variable. Now, just because we get asked how to … Continue reading →
I’ve come across the need to analyze TCP flows from time to time, and while scripts like flowtime and EasyTimeline are nice, they aren’t really, well, pretty. ggplot2, on the other hand is, and it turns out to be really easy to … Continue reading →
A couple of weeks ago, I had a discussion with a practitioner, working in some financial company, about ruin, and infinite time. And it remind me a weird result. Well, not a weird result, but a result I found disturbing, at first, when I was a stud...
1.1 Signatures of the d-p-q-r functions in libRmath Users of R are familiar with the functions for evaluating properties of and for sampling from probability distributions, the so-called d-p-q-r functions. The designation d-p-q-r reflects the fact t...
Outliers have an important influence over the PCs, for this reason they must be detected and examinee.We have just the spectra without lab data, and we have to check if any of the sample spectra is an outlier ( a noisy spectrum, a sample which belongs ...
We saw how to plot the raw spectra (X), how to calculate the mean spectrum, how to center the sprectra (subtracting the mean spectrum from every spectra of the original matrix X). After that we have developed the PCAs with the NIPALS algorithm, getting...
This plot in 2D, help us to decide the number of PCs, it is easy to create in R, once we have discompose the X matrix into a P matrix (loadings) and a T matrix (scores).For this plot, we just need the T matrix.> CPs<-seq(1,10,by=1)> matp...
> X<-yarn$NIR> X_nipals<-nipals(X,a=10,it=100)Two matrices are generated (P and T)As in other posts, we are going to look to the loadings & scores, for firsts three principal components:> wavelengths<-seq(1,268,by=1)> matplot(w...
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
