# Posts Tagged ‘ Tutorials ’

## NYT: In Simulation Work, the Demand Is Real

June 16, 2009
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The New York Times published this interesting article on how the ability to design and perform computer simulations is a highly marketable skill for careers across many disciplines.In methodology development we use simulation nearly every day. We've developed our own specialized genetic data simulation software, genomeSIMLA, that's freely available here by request for PC, Mac, and Linux.But if...

## Side by side analyses in Stata, SPSS, SAS, and R

June 15, 2009
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I've linked to UCLA's stat computing resources once before on a previous post about choosing the right analysis for the questions your asking and the data types you have. Here's another section of the same website that has code to run an identical analysis in all of these statistical packages, with examples to walk through (as they note...

## Free one-day R course at Vanderbilt

May 26, 2009
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The Vanderbilt Kennedy Center is offering a free (repeat, free) one-day introductory course to the R statistical computing language on June 23, taught by Theresa Scott from the department of Biostatistics. You can find contact/registration info at the link below.Vanderbilt Kennedy Center - An Introduction to the Fundamentals & Functionality of the R LanguageIn case you missed it,...

## Documentation and tutorial roundup

June 4, 2007
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I recently lost my documentation folder (oops), so I had to go online and retrieve the documentation files and tutorials that I find indispensible for working. I decided I’d save myself and everyone else the trouble by posting the list here. All of the files are available in PDF format. All R manuals Scilab documentation.

## Merging data: A tutorial

May 7, 2006
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The situation: you have two datasets with a common variable, and you want to incorporate both into one large dataset containing all of the variables. This is called merging data, and it’s easy to do in any standard statistical package. In these examples, I assume that there is only one variable between any datasets to

## Basic factor analysis in R

February 17, 2006
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The call to perform factor analysis on a set of variables in R is: fact1<- factanal(x,factors,scores=c(“regression”),rotation=”varimax”) where “x” is a dataframe containing the appropriate variables, and “factors” is the number of factors to be extracted. socres=”…” and rotation=”…” are optional, and varimax is the default rotation. The factanal function doesn’t seem to handle missing observations