Leo Spizzirri does an excellent job of providing mathematical intuition behind eigenvector centrality. As I was reading through it, I found it easier to just work through the matrix operations he proposes using R. You can find his paper her...
Posts Tagged ‘ R code ’
Do you still have time to sleep ?
Last week, @3wen (Ewen) helped me to write nice R functions to extract tweets in R and build datasets containing a lot of information. I've tried a couple of time on my own. Once on tweet contents, but it was not convincing and once on the activit...
Using SNA in Predictive Modeling
In a previous post, I described the basics of social network analysis. I plan to extend that example here with an application in predictive analytics. Let's suppose we have the following network (visualized in R)Suppose we have used the igraph package ...
An Introduction to Social Network Analysis with R and NetDraw
With the rise in the use of social media, data related to social networks is ripe for analysis using techniques from social network analysis and graph theory. According to International Network for Social Network Analysis, ‘Social network analysis is...
Regression via Gradient Descent in R
In a previous post I derived the least squares estimators using basic calculus, algebra, and arithmetic, and also showed how the same results can be achieved using the canned functions in SAS and R or via the matrix programming capabilities offered by ...
Basic Econometrics in R and SAS
Regression Basicsy= b0 + b1 *X ‘regression line we want to fit’The method of least squares minimizes the squared distance between the line ‘y’ andindividual data observations yi. That is minimize: ∑ ei2 = ∑ (yi - b0 - b1 Xi...
Gradient Descent in R
In a previous post I discussed the concept of gradient descent. Given some recent work in the online machine learning course offered at Stanford, I'm going to extend that discussion with an actual example using R-code (the actual code...
Bayesian Models with Censored Data: A comparison of OLS, tobit and bayesian models
The following R code models a censored dependent variable (in this case academic aptitude) using a traditional least squares, tobit, and Bayesian approaches. As depicted below, the OLS estimates (blue) for censored data are inconsistent and will ...
Elements of Bayesian Econometrics
posterior = (likelihood x prior) / integrated likelihood
The combination of a prior distribution and a likelihood function is utilized to produce a posterior distribution. Incorporating information from both the prior distribution and the likelihood function leads to a reduction in variance and an improved estimator.
As n→...
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
