1505 search results for "tutorial"

R package for effect size calculations for psychology researchers

October 19, 2013
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Dan Gerlanc writes: I read your post the other day on helping psychologists do research and thought you might be interested in our R package, “bootES”, for robust effect size calculation and confidence interval estimation using resampling techniques. The package provides The post R...

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Introduction to Feature selection for bioinformaticians using R, correlation matrix filters, PCA & backward selection

October 17, 2013
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Introduction to Feature selection for bioinformaticians using R, correlation matrix filters, PCA & backward selection

Bioinformatics is becoming more and more a Data Mining field. Every passing day, Genomics and Proteomics yield bucketloads of multivariate data (genes, proteins, DNA, identified peptides, structures), and every one of these biological data units are described by a number of features: length, physicochemical properties, scores, etc. Careful consideration of which features to select when trying...

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A first step towards R from spreadsheets

October 16, 2013
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A first step towards R from spreadsheets

Move your data analysis to a computing environment specifically designed for it. Why R and not spreadsheets? Here are three reasons: complexity graphics money Spreadsheets are easily overwhelmed.  Very complex things can be done in spreadsheets — it is just that complex spreadsheets are inefficient and dangerous. Graphics should be considered vital when doing anything with The post A...

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Latent Gaussian Models and INLA

October 16, 2013
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Latent Gaussian Models and INLA

If you read my post about Fast Bayesian Inference with INLA you might wonder which models are included within the class of latent Gaussian models (LGM), and can therefore be fitted with INLA. Next I will give a general definition about LGM and later I will describe three completely different examples that belong to this

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In case you missed it: September 2013 Roundup

October 11, 2013
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In case you missed them, here are some articles from September of particular interest to R users: Todd Schneider wrote an algorithm in R to find the "most concave" US state (it's NY), and created an animation to show how it works. Rob Hyndman (of the "forecast" package) describes how R-based forecasting saved the Australian government millions, in a...

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Custom Legend in R

October 10, 2013
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Custom Legend in R

This particular custom legend was designed with three purposes: To effectively bin values based on a theoretical minimum and maximum value for that variable (e.g. -1 and 1 or 0 and 100) To use a different interval notation than the default To handle NA values Even though this particular legend was designed with those needs, it should be simple to extrapolate from...

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analyze the panel study of income dynamics (psid) with r

October 7, 2013
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the panel study of income dynamics (psid) is a one-trick pony.  better than anything else out there, this survey allows you to answer the question, "where are they now?"  after tracking the same nationally-representative cohort of americans (...

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Simplicity Explained by The Author

October 4, 2013
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Simplicity Explained by The Author

Source is usually best explained by the source.  Ramnath Vaidyanathan provides an excellent look under the hood in his tutorial on rCharts explaining how he integrates the new d3 library uvCharts. If you want to explore further, here is a list of...

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Post 0: Getting Started with R

October 4, 2013
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Post 0: Getting Started with R

R is an interpreted programming language that makes it easy to think about statistics instead of thinking about programming. Unlike other programming languages, R is commonly used by typing commands one-at-a-time in an interactive session. RStudio is a program that … Continue reading →

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Questions on my online forecasting course

October 3, 2013
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I’ve been getting emails asking questions about my upcoming course on Forecasting using R. Here are some answers. Do I need to use the Revolution Enterprise version of R, or can I use open-source R? Open source R is fine. Revolution Analytics is organizing the course, but there is no requirement to use their software. I will be using...

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