1500 search results for "regression"

Factor Attribution

June 19, 2012
By
Factor Attribution

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:

Read more »

Reproducible reports & research with knitr in R Studio

June 18, 2012
By
Reproducible reports & research with knitr in R Studio

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

Read more »

Standard, Robust, and Clustered Standard Errors Computed in R

June 15, 2012
By
Standard, Robust, and Clustered Standard Errors Computed in R

Where do these come from? Since most statistical packages calculate these estimates automatically, it is not unreasonable to think that many researchers using applied econometrics are unfamiliar with the exact details of their computation. For the purposes of illustration, I am going to estimate different standard errors from a basic linear regression model: , using the

Read more »

Estimation of hydraulic conductivity and its uncertainty from grain-size data using GLUE and artificial neural networks.

June 13, 2012
By
Estimation of hydraulic conductivity and its uncertainty from grain-size data using GLUE and artificial neural networks.

AbstractVarious approaches exist to relate saturated hydraulic conductivity (Ks) to grain-size data. Most methods use a single grain-size parameter and hence omit the information encompassed by the entire grain-size distribution. This study compares two data-driven modelling methods—multiple linear regression and artificial neural networks—that use the entire grain-size distribution data as input for Ks prediction. Besides the predictive capacity of the methods,...

Read more »

Why R is Hard to Learn

June 13, 2012
By
Why R is Hard to Learn

The open source R software for analytics has a reputation for being hard to learn. It certainly can be, especially for people who are already familiar with similar packages such as SAS, SPSS or Stata. Training and documentation that leverages … Continue reading →

Read more »

Data distillation with Hadoop and R

June 11, 2012
By
Data distillation with Hadoop and R

We're definitely in the age of Big Data: today, there are many more sources of data readily available to us to analyze than there were even a couple of years ago. But what about extracting useful information from novel data streams that are often noisy and minutely transactional ... aye, there's the rub. One of the great things about...

Read more »

Autoplot: Graphical Methods with ggplot2

June 11, 2012
By
Autoplot:  Graphical Methods with ggplot2

Background As of ggplot2 0.9.0 released in March 2012, there is a new generic function autoplot.  This uses R's S3 methods (which is essentially oop for babies) to let you have some simple overloading of functions.  I'm not going to get deep into oop, because honestly we don't need to. The idea is very simple.  If I say "I'm...

Read more »

Classifying the UCI mushrooms

In my last post, I considered the shifts in two interestingness measures as possible tools for selecting variables in classification problems.  Specifically, I considered the Gini and Shannon interestingness measures applied to the 22 categorical mushroom characteristics from the UCI mushroom dataset.  The proposed variable selection strategy was to compare these values when computed from only edible mushrooms...

Read more »

R, the master troll of statistical languages

June 8, 2012
By
R, the master troll of statistical languages

Warning: what follows is a somewhat technical discussion of my love-hate relationship with the R statistical language, in which I somehow manage to waste 2,400 words talking about a single line of code. Reader discretion is advised. I’ve been using R to do most of my statistical analysis for about 7 or 8 years now–ever

Read more »

Simulation in the profiling model

June 7, 2012
By
Simulation in the profiling model

In this post I try to make a small simulation of the sensory (flavour) profiling data, and examine if the parameters of simulated data can be retrieved by the Bayesian model build in the previous posts.The conclusion is that it is difficult, the amount...

Read more »