One popular trend in presenting results is the "coefficient plot," an alternative to the table of regression coefficients. I am seeing this a little more often in political science research and have received a few requests for code, so I … Contin...

One popular trend in presenting results is the "coefficient plot," an alternative to the table of regression coefficients. I am seeing this a little more often in political science research and have received a few requests for code, so I … Contin...

The bug-fix in version 0.9.12 of Rcpp turned out to be incomplete, so a new version 0.9.13 is now on CRAN and will get to Debian shortly. The Rcpp::Enviroment constructor is now properly fixed (using the global environment as a default value). As ...

I want to continue with Factor Attribution theme that I presented in the Factor Attribution post. I have re-organized the code logic into the following 4 functions: factor.rolling.regression – Factor Attribution over given rolling window factor.rolling.regression.detail.plot – detail time-series plot and histogram for each factor factor.rolling.regression.style.plot – historical style plot for selected 2 factors factor.rolling.regression.bt.plot

I came across a very descriptive visualization of the Factor Attribution that I will replicate today. There is the Three Factor Rolling Regression Viewer at the mas financial tools web site that performs rolling window Factor Analysis of the “three-factor model” of Fama and French. The factor returns are available from the Kenneth R French:

Arguably, knitr (CRAN link) is the most outstanding R package of this year and its creator, Yihui Xie is the star of the useR! conference 2012. This is because the ease of use comparing to Sweave for making reproducible report. Integration of knitR and R Studio has made reproducible research much more convenience, intuitive and easier to

e-mails with the latest R posts.

(You will not see this message again.)