Blog Archives

Efficiency of Importing Large CSV Files in R

February 10, 2014
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Efficiency of Importing Large CSV Files in R

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Julia and SQLite

February 8, 2014
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Julia and SQLite

Similar to R and Pandas in Python, Julia provides a simple yet efficient interface with SQLite database. In addition, it is extremely handy to use sqldf() function, which is almost identical to the sqldf package in R, in SQLite package for data munging.

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Simplex Model in R

February 2, 2014
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Simplex Model in R

R CODE R OUTPUT SAS CODE & OUTPUT FOR COMPARISON

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rPython – R Interface to Python

October 13, 2013
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rPython – R Interface to Python

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Generate and Retrieve Many Objects with Sequential Names

September 8, 2013
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Generate and Retrieve Many Objects with Sequential Names

While coding ensemble methods in data mining with R, e.g. bagging, we often need to generate many data and models objects with sequential names. Below is a quick example how to use assign() function to generate many prediction objects on the fly and then retrieve these predictions with mget() to do the model averaging.

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Prototyping Multinomial Logit with R

August 21, 2013
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Prototyping Multinomial Logit with R

Recently, I am working on a new modeling proposal based on the competing risk and need to prototype multinomial logit models with R. There are R packages implementing multinomial logit models that I’ve tested, namely nnet and vgam. Model outputs with iris data are shown below. However, in my view, above methods are not flexible

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GRNN and PNN

June 23, 2013
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GRNN and PNN

From the technical prospective, people usually would choose GRNN (general regression neural network) to do the function approximation for the continuous response variable and use PNN (probabilistic neural network) for pattern recognition / classification problems with categorical outcomes. However, from the practical standpoint, it is often not necessary to draw a fine line between GRNN

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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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