Monthly Archives: April 2014

Interpreting interaction coefficient in R (Part1 lm)

April 8, 2014
By
Interpreting interaction coefficient in R (Part1 lm)

Interaction are the funny interesting part of ecology, the most fun during data analysis is when you try to understand and to derive explanations from the estimated coefficients of your model. However you do need to know what is behind these estimate, there is a mathematical foundation between them that you need to be aware

Read more »

Annotation charts and histograms with googleVis

April 8, 2014
By

After my posts on timeline, Sankey and calendar charts, this will be the last to introduce new chart types of the developer version of googleVis. Today I will give examples for the new annotation charts and histograms.Annotation chartsAnnotation charts...

Read more »

Get notified when R packages update

April 8, 2014
By
Get notified when R packages update

Today’s highly active R user base is developing, re-developing, and releasing R packages at a never-before-seen rate. While this is fantastic news for the R community as such, it inevitably also causes growing pains as mentioned before. One of the often cited problems is the painful and time-consuming task to keep track of changes and version updates of

Read more »

Ensemble Packages in R

April 8, 2014
By
Ensemble Packages in R

by Mike Bowles Mike Bowles is a machine learning expert and serial entrepreneur. This is the second post in what is envisioned as a four part series that began with Mike's Thumbnail History of Ensemble Models. One of the main reasons for using R is the vast array of high-quality statistical algorithms available in R. Ensemble methods provide a...

Read more »

devtools 1.5

April 8, 2014
By
devtools 1.5

devtools 1.5 is now available on CRAN. It includes four new functions make it easier to add useful infrastructure to packages: add_test_infrastructure() will create testthat infrastructure when needed. add_rstudio_project() adds an Rstudio project file to your package. add_travis() adds a basic template for travis-ci. add_build_ignore() makes it easy to add files to .Rbuildignore, escaping special

Read more »

Getting Social Sciences Out of the Black Box: The Open Access Revolution

April 8, 2014
By
Getting Social Sciences Out of the Black Box: The Open Access Revolution

Trading Ethos for LogosUp until very recently (the last 10 years) it has been uncommon for social science researchers to share their data even when the sharing would neither compromise the private information of the subjects nor the validity of the stu...

Read more »

JMBayes R package (webinar)

April 8, 2014
By
JMBayes R package (webinar)

A free webinar will provide an introduction to the “JMBayes” R package which provides methods for Joint Modeling of Longitudinal and Time-to-Event Data under a Bayesian Approach. Webinar Format: - Introduction to Joint Models and the JMBayes R package - … Continue reading →

Read more »

Ontario First Nations Libraries Compared Using Ontario Open Data

April 7, 2014
By
Ontario First Nations Libraries Compared Using Ontario Open Data

I recently downloaded a very cool dataset on Ontario libraries from the Ontario Open Data Catalogue.  The dataset contains 142 columns of information describing 386 libraries in Ontario, representing a fantastically massive data collection effort for such important cultural institutions (although … Continue reading →

Read more »

RcppArmadillo 0.4.200.0

April 7, 2014
By

Conrad released a new upstream release 4.200 for Armadillo, his templated C++ library for linear algebra, earlier today. As usual, this was rolled up in a new RcppArmadillo release 0.4.200.0; I had actually made two pre-releases leading up his 4.20...

Read more »

Quality of Historical Stock Prices from Yahoo Finance

April 7, 2014
By
Quality of Historical Stock Prices from Yahoo Finance

I recently looked at the strategy that invests in the components of S&P/TSX 60 index, and discovered that there are some abnormal jumps/drops in historical data that I could not explain. To help me spot these points and remove them, I created a helper function data.clean() function in data.r at github. Following is an example

Read more »

Sponsors

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)