Monthly Archives: March 2015

Configuring the R BatchJobs package for Torque batch queues

March 31, 2015
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Configuring the R BatchJobs package for Torque batch queues

I was asked recently to look at some R code which performs “embarrassingly parallel” computations (the same function, multiple times, different parameters) and see whether I could modify it to run on one of our high-performance computing clusters. The machine has 63 virtual compute nodes and uses the TORQUE batch queue system to allocate nodes

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Modeling Count Time Series with tscount Package

March 31, 2015
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Modeling Count Time Series with tscount Package

The example below shows how to estimate a simple univariate Poisson time series model with the tscount package. While the model estimation is straightforward and yeilds very similar parameter estimates to the ones generated with the acp package (https://statcompute.wordpress.com/2015/03/29/autoregressive-conditional-poisson-model-i), the prediction mechanism is a bit tricky. 1) For the in-sample and the 1-step-ahead predictions: yhat_

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A new open source data set for anomaly detection

March 31, 2015
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Yahoo Labs has just released an interesting new data set useful for research on detecting anomalies (or outliers) in time series data. There are many contexts in which anomaly detection is important. For Yahoo, the main use case is in detecting unusual traffic on Yahoo servers. The data set comprises real traffic to Yahoo services, along

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an example of drawing beast tree using ggtree

March 31, 2015
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an example of drawing beast tree using ggtree

FigTree is designed for viewing beast output as demonstrated by their example data: BEAST output is well supported by ggtree and it's easy to reproduce such a tree view. ggtree supports parsing beast output by read.beast function. We can visualize the tree directly by using ggtree function. Since this is a time scale tree, we can set the parameter time_scale...

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More Airline Crashes via the Hadleyverse

March 31, 2015
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I saw a fly-by #rstats mention of more airplane accident data on — of all places — LinkedIn (email) today which took me to a GitHub repo by @philjette. It seems there’s a web site (run by what seems to be a single human) that tracks plane crashes. Here’s a tweet from @philjette announcing it:

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Standardising Function Names in R

March 31, 2015
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Standardising Function Names in R

The renamer Package Tired of the disparate naming systems in R? Then this is the package for you. Installing the package The package is located in my drat. To install install.packages("renamer", repos="http://csgillespie.github.io/drat", type="source") or if you have drat installed drat::addRepo("csgillespie") install.packages("renamer", type="source") The source is available on my github page Example: The CamelCaseR If have an

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Le Monde puzzle [#905]

March 31, 2015
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Le Monde puzzle [#905]

A recursive programming  Le Monde mathematical puzzle: Given n tokens with 10≤n≤25, Alice and Bob play the following game: the first player draws an integer1≤m≤6 at random. This player can then take 1≤r≤min(2m,n) tokens. The next player is then free to take 1≤s≤min(2r,n-r) tokens. The player taking the last tokens is the winner. There is

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Student Performance Indicators

March 31, 2015
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Student Performance Indicators

Check out my: Portfolio Site Github LinkedIn Source: http://archive.ics.uci.edu/ml/datasets/Student+Performance This project is based upon two datasets of the academic performance of Portuguese students in two different

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Registration Open for R/Finance 2015!

March 31, 2015
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You can find registration information and agenda details (as they become available) on the conference website.  Or you can go directly to the registration page.  Note that there's an early-bird registration deadl...

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Targeted Learning R Packages for Causal Inference and Machine Learning

March 31, 2015
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Targeted Learning R Packages for Causal Inference and Machine Learning

by Sherri Rose Assistant Professor of Health Care Policy Harvard Medical School Targeted learning methods build machine-learning-based estimators of parameters defined as features of the probability distribution of the data, while also providing influence-curve or bootstrap-based confidence internals. The theory offers a general template for creating targeted maximum likelihood estimators for a data structure, nonparametric or semiparametric statistical model,...

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