R

Blogs on the R software.

abcfr 0.9-3

August 26, 2015
By
abcfr 0.9-3

In conjunction with our reliable ABC model choice via random forest paper, about to be resubmitted to Bioinformatics, we have contributed an R package called abcrf that produces a most likely model and its posterior probability out of an ABC reference table. In conjunction with the realisation that we could devise an approximation to the […]

Read more »

Best Paper Award for the “Evolution of correlations”

August 25, 2015
By

I am pleased to announce that Marco Perugini and I have received the 2015 Best Paper Award from the Association of Research in Personality (ARP) for our paper: Schönbrodt, F. D., & Perugini, M. (2013). At what sample size do correlations stabilize...

Read more »

Visualising the predictive distribution of a log-transformed linear model

August 25, 2015
By
Visualising the predictive distribution of a log-transformed linear model

Last week I presented visualisations of theoretical distributions that predict ice cream sales statistics based on linear and generalised linear models, which I introduced in an earlier post.Theoretical distributionsToday I will take a closer look at t...

Read more »

Canberra Data Miners: Seminar on Text, Knowledge and Information Extraction, by Dr Lizhen Qu (NICTA), Canberra, 4:30-5:30pm, Tuesday 1 Sept

August 24, 2015
By
Canberra Data Miners: Seminar on Text, Knowledge and Information Extraction, by Dr Lizhen Qu (NICTA), Canberra, 4:30-5:30pm, Tuesday 1 Sept

Topic: Text, Knowledge, and Information Extraction Speaker: Dr. Lizhen Qu, Researcher at NICTA Organizer: Canberra Data miners Meetup Group Date and time: 4:30-5:30pm, Tuesday 1 Sept Location: Teal Room of Inspire Centre, University of Canberra, Building 25, University of Canberra, … Continue reading →

Read more »

Predicting Titanic deaths on Kaggle IV: random forest revisited

August 23, 2015
By
Predicting Titanic deaths on Kaggle IV: random forest revisited

On July 19th I used randomForest to predict the deaths on Titanic in the Kaggle competition. Subsequently I found that both bagging and boosting gave better predictions than randomForest. This I found somewhat unsatisfactory, hence I am now revisi...

Read more »

Data frames and tables in Scala

August 21, 2015
By
Data frames and tables in Scala

Introduction To statisticians and data scientists used to working in R, the concept of a data frame is one of the most natural and basic starting points for statistical computing and data analysis. It always surprises me that data frames aren’t a core concept in most programming languages’ standard libraries, since they are essentially a … Continue reading Data frames and tables in Scala

Read more »

Kickin’ it with elastic net regression

August 20, 2015
By
Kickin’ it with elastic net regression

With the kind of data that I usually work with, overfitting regression models can be a huge problem if I'm not careful. Ridge regression is a really effective technique for thwarting overfitting. It does this by penalizing the L2 norm… Continue reading →

Read more »

Display of Geographic Data in R

August 18, 2015
By

This was a deck used in my presentation to the Inland Northwest R user Group this past Friday (August 14, 2015). It relies on the work done by many folks, including Ari Lamstein, Kyle Walker, Erik Erhardt, and the kind folks at R-Studio. Please click o...

Read more »

Visualising theoretical distributions of GLMs

August 18, 2015
By
Visualising theoretical distributions of GLMs

Two weeks ago I discussed various linear and generalised linear models in R using ice cream sales statistics. The data showed not surprisingly that more ice cream was sold at higher temperatures.icecream

Read more »

Slides of 10+ excellent tutorials at KDD 2015: Spark, graph mining and many more

August 17, 2015
By
Slides of 10+ excellent tutorials at KDD 2015: Spark, graph mining and many more

by Yanchang Zhao RDataMining.com I attended the KDD 2015 conference in Sydney last week. At the conference, there were more than 10 tutorials and I went to two of them, which are 1) Graph-Based User Behavior Modeling: From Prediction to … Continue reading →

Read more »


Subscribe

Email:

  Subscribe