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...
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PCA for NIR Spectra_part 003: "NIPALS"
> 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...
PCA for NIR Spectra_part 002: "Score planes"
The idea of this post is to compare the score plots for the first 3 principal components obtained with the algorithm “svd” with the scores plot of other chemometric software (Win ISI in this case). Previously I had exported the yarn spectra t...
Visualization in regression analysis
Visualization is a key to success in regression analysis. This is one of the (many) reasons I am also suspicious when I read an article with a quantitative (econometric) analysis without any graph. Consider for instance the following dataset, obtai...
Maps with R (III)
In my previous posts (1 and 2) I wrote about maps with complex legends but without any kind of interactivity. …
Visualization in regression analysis
Visualization is a key to success in regression analysis. This is one of the (many) reasons I am also suspicious when I read an article with a quantitative (econometric) analysis without any graph. Consider for instance the following dataset, obtained from http://data.worldbank.org/, with, for each country, the GDP per capita (in some common currency) and the infant mortality rate...
PCA for NIR Spectra_part 001: "Plotting the loadings"
There are different algorithms to calculate the Principal Components (PCs). Kurt Varmuza & Peter Filzmozer explain them in their book: “Introduction to Multivariate Statistical Analysis in Chemometrics”.I´m going to apply one of them, to...
GARCH estimation using maximum likelihood
In my previous post I presented my findings from my finance project under the guidance of Dr Susan Thomas. The results in my paper suggested that there are macroeconomic variables, particularly the INR/USD exchange rates, that help us understand the dynamics of stock returns. Although the results that I obtained were significant at 5%...
Foreign Currencies and US 10y Treasury Yields
Since I explored the relationship between the Japanese Yen and the US 10y Treasury Yield on Friday, I thought it might be worthwhile to extend the exploration to a much broader range of currencies. I personally am most interested on how Asian Central B...
New version of analogue (0.8-0)
Yesterday I pushed an update of my analogue package to CRAN. The new version is 0.8-0 and contains some new functions, several bug fixes and a major change arising from additions to R 2.14.x requiring all packages to have a … Continue reading →
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
