2223 search results for "regression"

How the MKL speeds up Revolution R Open

October 22, 2014
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How the MKL speeds up Revolution R Open

by Andrie de Vries Last week we announced the availability of Revolution R Open, an enhanced distribution of R. One of the enhancements is the inclusion of high performance linear algebra libraries, specifically the Intel MKL. This library significantly speeds up many statistical calculations, e.g. the matrix algebra that forms the basis of many statistical algorithms. Several years ago,...

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The lapply command 101

October 20, 2014
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The lapply command 101

Next up in our review of the family of apply commands we’ll look at the lapply function, which can be used to loop over the elements of a list (or a vector). This is a true convenience although for those with experience in other programming languages it can seem unnecessary since you are accustomed to

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hts with regressors

October 19, 2014
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hts with regressors

The hts package for R allows for forecasting hierarchical and grouped time series data. The idea is to generate forecasts for all series at all levels of aggregation without imposing the aggregation constraints, and then to reconcile the forecasts so they satisfy the aggregation constraints. (An introduction to reconciling hierarchical and grouped time series is

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Rules of thumb to predict how long you will live

October 15, 2014
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Rules of thumb to predict how long you will live

Figure out how long you will live with these rules of thumb. The post Rules of thumb to predict how long you will live appeared first on Decision Science News.

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New Course! A hands-on introduction to statistics with R by A. Conway (Princeton University)

October 13, 2014
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New Course! A hands-on introduction to statistics with R by A. Conway (Princeton University)

The best way to learn is at your own pace. Combining the interactive R learning environment of DataCamp and the expertise of Prof. Conway of Princeton, we offer you an extensive online course on introductory statistics with R.  Start learning now… Whether you are a professional using statistics in your job, an academic wanting a

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SIR Model of Epidemics

October 12, 2014
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SIR Model of Epidemics

The SIR model divides the population to three compartments: Susceptible, Infected and Recovered. If the disease dynamic fits the SIR model, then the flow of individuals is one direction from the susceptible group to infected group and then to the recovered group. All individuals are assumed to be identical in terms of their susceptibility to infection, infectiousness if infected...

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Building a DGA Classifier: Part 3, Model Selection

October 6, 2014
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Building a DGA Classifier: Part 3, Model Selection

This is part two of a three-part blog series on building a DGA classifier and it is split into the three phases of building a classifier: 1) Data preparation 2) Feature engineering and 3) Model selection (this post) Back in part 1, we prepared the data and we are starting with a nice clean list of domains labeled as either legitimate (“legit”) or generated by an algorithm (“dga”)....

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TBATS with regressors

October 5, 2014
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I’ve received a few emails about including regression variables (i.e., covariates) in TBATS models. As TBATS models are related to ETS models, tbats() is unlikely to ever include covariates as explained here. It won’t actually complain if you include an xreg argument, but it will ignore it. When I want to include covariates in a

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I don’t want to learn R! SPSS is fine! (responses)

October 2, 2014
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I don’t want to learn R! SPSS is fine! (responses)

  I frequently find myself thinking of the best way to convince an SPSS user to make the switch to R. In the process, I came up with the three following most common objections by SPSS users and my responses.   Objection #1) I can’t use R because I don’t know how to program Response: Of ...

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Building a DGA Classifer: Part 2, Feature Engineering

October 2, 2014
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Building a DGA Classifer: Part 2, Feature Engineering

This is part two of a three-part blog series on building a DGA classifier and it is split into the three phases of building a classifier: 1) Data preperation 2) Feature engineering and 3) Model selection. Back in part 1, we prepared the data and we are starting with a nice clean list of domains labeled as either legitamate (“legit”) or generated by...

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