See the last post for all the details. I just could not help creating a couple more.
p2 <- xyplot(value ~ date | indexname, data = jgb.melt,
type = "l", layout = c(length(unique(jgb.melt$indexname)),
...In water resource management, climate change, hydrology and related disciplines long time series of precipitation/rainfall data is required. Since historical records are relatively short, typically 50 years or less, mathematical/statistical models are ...
This blog is littered with posts about Japan. In one sentence, I think Japan presents opportunity and is a very interesting real-time test of much of my macro thinking. Proper visualization is absolutely essential for me to understand all of the dynami...
This question was posed on crossvalidated.com: I have a monthly time series (for 2009–2012 non-stationary, with seasonality). I can use ARIMA (or ETS) to obtain point and interval forecasts for each month of 2013, but I am interested in forecasting the total for the whole year, including prediction intervals. Is there an easy way in R to obtain interval...
Stack Exchange is a series of question-and-answer sites, including Stack Overflow for programming and Cross Validated for statistics. I was introduced to these sites at a short talk by Barry Rowlingson at the 2011 UseR! meeting, “Why R-help must die!“ These sites have a lot of advantages over R-help: The format is easier to read, 
Update: I am aware the table of contents are being displayed in bullet form as I intended. The web template I'm using seems to be buggy. It also seems to think this page is in Indonesian...Working on it!
Table of Contents:
Noam Ross recently shared a very useful guide to speeding up your R code. Get a bigger computer (for example, renting an instance on the Amazon cloud for a few cents an hour) Use parallel programming techniques Using the R byte-compiler Profiling and benchmarking your code Using high-performance packages (like xts, for time series) And lastly, rewriting your code...
Previously, I talked about genetic algorithms (GA) for feature selection and illustrated the algorithm using a modified version of the GA R package and simulated data. The data were simulated with 200 non-informative predictors and 12 linear effects and three non-linear effects. Quadratic discriminant analysis (QDA) was used to model the data. The last set of...
Encouraged by this post I had another look at quandl for collecting datasets from different agencies. Right now I need to get data for four countries on a couple of dozen indicators. This graphic is just a quick example with only two indicators of what I am aiming to be able to do. The process
Model selection is a process of seeking the model in a set of candidate models that gives the best balance between model fit and complexity (Burnham & Anderson 2002). I have always used AIC for that. But you can also…
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
