If connecting data to the real world is the next sexy job, then how do I do this? And how do I connect the real world to R? It can be done as Matt Shottwell showed with his home made ECG and a patched version of R at useR! 2011. However, there are ot...
Every year the UK’s general insurance actuarial community organises a big conference, which they call GIRO, short for General Insurance Research Organising committee. This year's conference is in Brussels from 18 - 21 September 2012. Despite the fac...
Today I feel very lucky, as I have been invited to the Royal Statistical Society conference to give a tutorial on interactive web graphs with R and googleVis. I prepared my slides with RStudio, knitr, pandoc and slidy, similar to my Cambridge R talk. ...
In this tutorial I am going to share my R&D and trading experience using the well-known from statistics Autoregressive Moving Average Model (ARMA). There is a lot written about these models, however, I strongly recommend Introductory Time Series with R, which I find is a perfect combination between light theoretical background and practical implementations in
Thank’s to Andrej who wrote this comment: “Is it possible to to color the resulting 12 clusters within your original image to get a feel for visual separation?” You can do so: But how to get values at a location? You will need these values to determine whether the defined class is representing a water
The main question when using remote sensed raster data, as we do, is the question of NaN-treatment. Many R functions are able to use an option like rm.NaN=TRUE to treat these missing values. In our case the kmeans function in R is not capable to use such a parameter. After reading the tif-files and creating
What is Rook?Rook is a web server interface for R, written by Jeffrey Horner, the author of rApache and brew. But unlike other web frameworks for R, such as brew, R.rsp (which I have used in the past1), Rserve, gWidgetWWWW or sumo (which I haven't used...
Disappeared for a while collecting frequent flyer points. In the process I ‘discovered’ that I live in the middle of nowhere, as it took me 36 hours to reach my conference destination (Estoril, Portugal) through Christchurch, Sydney, Bangkok, Dubai, Madrid … Continue reading →
In my last post I was explaining the usage of QGis to do a layerstack of a Landsat-scene. Due to the fact that further research and trying out resulted in frustration I decided to stick with a software I know well: R. So download the needed layers here and open up your flavoured version of
One of the shortcomings of regression (both linear and logistic) is that it doesn’t handle categorical variables with a very large number of possible values (for example, postal codes). You can get around this, of course, by going to another modeling technique, such as Naive Bayes; however, you lose some of the advantages of regression Related posts:
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
