Blog Archives

Prototyping A General Regression Neural Network with SAS

June 22, 2013
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Prototyping A General Regression Neural Network with SAS

Last time when I read the paper “A General Regression Neural Network” by Donald Specht, it was exactly 10 years ago when I was in the graduate school. After reading again this week, I decided to code it out with SAS macros and make this excellent idea available for the SAS community. The prototype of

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General Regression Neural Network with R

June 16, 2013
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General Regression Neural Network with R

Similar to the back propagation neural network, the general regression neural network (GRNN) is also a good tool for the function approximation in the modeling toolbox. Proposed by Specht in 1991, GRNN has advantages of instant training and easy tuning. A GRNN would be formed instantly with just a 1-pass training with the development data.

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Improve The Efficiency in Joining Data with Index

June 9, 2013
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Improve The Efficiency in Joining Data with Index

When managing big data with R, many people like to use sqldf() package due to its friendly interface or choose data.table() package for its lightening speed. However, very few would pay special attentions to small details that might significantly boost the efficiency of these packages by adding index to the data.frame or data.table. In my

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Estimating Finite Mixture Models with Flexmix Package

June 9, 2013
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Estimating Finite Mixture Models with Flexmix Package

In my post on 06/05/2013 (http://statcompute.wordpress.com/2013/06/05/estimating-composite-models-for-count-outcomes-with-fmm-procedure), I’ve shown how to estimate finite mixture models, e.g. zero-inflated Poisson and 2-class finite mixture Poisson models, with FMM and NLMIXED procedure in SAS. Today, I am going to demonstrate how to achieve the same results with flexmix package in R. R Code R Output for 2-Class Finite Mixture

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R and MongoDB

June 7, 2013
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R and MongoDB

MongoDB is a document-based noSQL database. Different from the relational database storing data in tables with rigid schemas, MongoDB stores data in documents with dynamic schemas. In the demonstration below, I am going to show how to extract data from a MongoDB with R. Before starting the R session, we need to install the MongoDB

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Grid Search for Free Parameters with Parallel Computing

June 1, 2013
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Grid Search for Free Parameters with Parallel Computing

In my previous post (http://statcompute.wordpress.com/2013/05/25/test-drive-of-parallel-computing-with-r) on 05/25/2013, I’ve demonstrated the power of parallel computing with various R packages. However, in the real world, it is not straight-forward to utilize these powerful tools in our day-by-day computing tasks without carefully formulate the problem. In the example below, I am going to show how to use the

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Rmagic, A Handy Interface Bridging Python and R

May 31, 2013
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Rmagic, A Handy Interface Bridging Python and R

Rmagic (http://ipython.org/ipython-doc/dev/config/extensions/rmagic.html) is the ipython extension that utilizes rpy2 in the back-end and provides a convenient interface accessing R from ipython. Compared with the generic use of rpy2, the rmagic extension allows users to exchange objects between ipython and R in a more flexible way and to run a single R function or a block

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Import All Text Files in A Folder with Parallel Execution

May 26, 2013
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Import All Text Files in A Folder with Parallel Execution

Sometimes, we might need to import all files, e.g. *.txt, with the same data layout in a folder without knowing each file name and then combine all pieces together. With the old method, we can use lapply() and do.call() functions to accomplish the task. However, when there are a large number of such files and

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Test Drive of Parallel Computing with R

May 25, 2013
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Test Drive of Parallel Computing with R

Today, I did a test run of parallel computing with snow and multicore packages in R and compared the parallelism with the single-thread lapply() function. In the test code below, a data.frame with 20M rows is simulated in a Ubuntu VM with 8-core CPU and 10-G memory. As the baseline, lapply() function is employed to

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Conversion between Factor and Dummies in R

May 18, 2013
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Conversion between Factor and Dummies in R

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