Monthly Archives: January 2013

Lomb-Scargle periodogram for unevenly sampled time series

January 10, 2013
By
Lomb-Scargle periodogram for unevenly sampled time series

In the natural sciences, it is common to have incomplete or unevenly sampled time series for a given variable. Determining cycles in such series is not directly possible with methods such as Fast Fourier Transform (FFT) and may require some degree o...

Read more »

Lomb-Scargle periodogram for unevenly sampled time series

January 10, 2013
By
Lomb-Scargle periodogram for unevenly sampled time series

In the natural sciences, it is common to have incomplete or unevenly sampled time series for a given variable. Determining cycles in such series is not directly possible with methods such as Fast Fourier Transform (FFT) and may require some degree of interpolation to fill in gaps. An alternative is the Lomb-Scargle method (or...

Read more »

Optimizing parameters for an oscillator – Video

January 10, 2013
By

Here’s a video how the modFit function from the FME package optimizes parameters for an oscillation. A Nelder-Mead-optimizer (R function optim) finds the best fitting parameters for an undampened oscillator. Minimum was found after 72 iterations, true parameter eta was -.05: Evolution of parameters in optimization process from Felix Schönbrodt on Vimeo. More on estimating

Read more »

A first lambda function with C++11 and Rcpp

January 10, 2013
By

Yesterday’s post started to explore the nice additions which the new C++11 standard is bringing to the language. One particularly interesting feature are lambda functions which resemble the anonymous functions R programmers have enjoyed all along...

Read more »

Reading Codebook Files in R

January 10, 2013
By

One issue I continuously encounter when starting to work with a new dataset is that of the codebook. In general, I prefer to load a codebook into R like any other data source, specifically as a data frame. And ideally, one data frame to provides the variable names with descriptions and any other meta data available, and a separate...

Read more »

Maps in R: Plotting data points on a map

January 10, 2013
By
Maps in R: Plotting data points on a map

In the introductory post of this series I showed how to plot empty maps in R. Today I'll begin to show how to add data to R maps. The topic of this post is the visualization of data points on … Continue reading →

Read more »

Elements of Statistical Learning: free book download

January 9, 2013
By

The go-to bible for this data scientist and many others is The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani, and Jerome Friedman. Each of the authors is an expert in machine learning / prediction, and in some cases invented the techniques we turn to today to make sense of big data: ensemble...

Read more »

Every NFL punt since 2002

January 9, 2013
By
Every NFL punt since 2002

The site reddit told us about data on every single NFL (U.S. National Football League) play since 2002. We read it in and did an analysis of punting. The results are beautiful. The post Every NFL punt since 2002 appeared first on Decision Science News.

Read more »

Getting Access data into R

January 9, 2013
By

Revisiting Cronbach 1951 via Simulation with Shiny

January 9, 2013
By
Revisiting Cronbach 1951 via Simulation with Shiny

At the time of the creation of this blog, Cronbach’s 1951 piece on coefficient alpha has 18,132 citations according to google scholar.  The main use of coefficient alpha is to assess internal consistency reliability of a test or survey.   Although it may have been forgotten, the proof Cronbach demonstrated established that coefficient alpha is the mean of all split...

Read more »