How We Know Us

Tag: analysis

  • Calculating Conditional Entropy in R

      conditionalEntropy <- function( graph ) {    # graph is a 2 or 3 column dataframe    if (ncol(graph) == 2 ) {       names(graph) <- c(“from”,”to”)       graph$weight <- 1    } else if (ncol(graph) == 3)       names(graph) <- c(“from”,”to”,”weight”)    max <- length(rle(paste(graph$from, graph$to))$values)  …

  • The Lambert Effect – Subtleties in Cloud Modeling

    After you’ve done all of the hard work in creating the perfect model that fits your data comes the hard part: does it make sense? Have you overly fitted your data? Are the results confirming or surprising? If surprising, is that because there’s a surprise or your model is broken? Here’s an example: iterating on…

  • Determining Application Performance Profiles in the Cloud

    I want to know how to characterize my workloads in the cloud. With that, I should be able to find systems both over-provisioned and resource starved to aid in right-sizing and capacity planning. CloudForms by Red Hat can do these at the system level, which is where you would most likely take any actions, but I want…

  • Analyzing Cloud Performance with CloudForms and R

    CloudForms by Red Hat has extensive reporting and predictive analysis built into the product. But what if you already have a reporting engine? Or want to do analysis not already built into the system? This project was created as an example of using Cloud Forms with external reporting tools (our example uses R). Take special care that you can miss context to…

  • Health Care Leans Republican

    3.6-times as many former congressional staffers turned health care lobbyists and their immediate connections have network ties closer to former President Bush, than to current President Obama. The connections in the network map shown below, and used for the analysis above, include people and organizations (e.g. corporate, not-for-profit, public, etc.) the people have been identified…

  • Mathematicians Do It Randomly

    What it look like if you took all of the Mathematics articles from JSTOR, the digital journal archive, and mapped co-authorship of the papers? It would look something like this.  Interesting to note, that while the distribution does hold to the small world network distribution exponent, there’s some “peakiness” about it that may suggest it’s…

  • Health Care Lobbyists Part Deux

    Thanks everyone for showing the strong interest in the Lobbyist map.  I got a couple nice mentions at Mother Jones and LittleSis.org, but more importantly, I’ve added in all of the other names in the map. Circles are people, squares are organizations, and white circles are the lobbyists in question. If you’d rather the image…

  • Best Networked Healthcare Lobbyists? [updated]

    The Huffington Post, along with public contributors, has been collecting a list of former Congressional staffers turned healthcare lobbyists.  LittleSis.org has been keeping track of these former staffers, and thanks to their API, we now have a social graph of their relationships. Former staffers in white (with names), and the rest of the visual field…

  • Healthcare and the Senate Finance Committee

    Late last month, the NY Times had an article about the debate over healthcare legislation taking place in the Senate Finance Committee. Coincidentally, around that time, the folks over at LittleSis, the “free database detailing the connections between powerful people and organizations,” were kind enough to give me early access to their API (thanks Kevin…

  • Twitter Communication is Scale Free

    Creating a network from a sample of communications from approximately 900,000 people on Twitter, the distribution of distinct communication partners result fits the definition of a scale-free network.  The power is a little higher than scale-free networks usually described for social networks (2<k<3), but not much.