1242 search results for "latex"

Veterinary Epidemiologic Research: Linear Regression Part 2 – Checking assumptions

March 6, 2013
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Veterinary Epidemiologic Research: Linear Regression Part 2 – Checking assumptions

We continue on the linear regression chapter the book Veterinary Epidemiologic Research. Using same data as last post and running example 14.12: Now we can create some plots to assess the major assumptions of linear regression. First, let’s have a look at homoscedasticity, or constant variance of residuals. You can run a statistical test, the

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Stan 1.2.0 and RStan 1.2.0

March 6, 2013
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Stan 1.2.0 and RStan 1.2.0

Stan 1.2.0 and RStan 1.2.0 are now available for download. See: http://mc-stan.org/ Here are the highlights. Full Mass Matrix Estimation during Warmup Yuanjun Gao, a first-year grad student here at Columbia (!), built a regularized mass-matrix estimator. This helps for posteriors with high correlation among parameters and varying scales. We’re still testing this ourselves, so The post Stan...

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Barycentric interpolation: fast interpolation on arbitrary grids

March 6, 2013
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Barycentric interpolation: fast interpolation on arbitrary grids

Barycentric interpolation generalises linear interpolation to arbitrary dimensions. It is very fast although suboptimal if the function is smooth. You might now it as algorithm 21.7.1 in Numerical Recipes (Two-dimensional Interpolation on an Irregular Grid). Using package geometry it can be implemented in a few lines of code in R. Here’s a quick explanation of what

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Tools for making a paper

March 1, 2013
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Since it seems to be the fashion, here’s a post about how I make my academic papers. Actually, who am I trying to kid? This is also about how I make slides, letters, memos and “Back in 10 minutes” signs to pin on the door. Nevertheless it’s for making academic papers that I’m going to

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How to make a scientific result disappear

February 27, 2013
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How to make a scientific result disappear

Nathan Danneman (a co-author and one of my graduate students from Emory) recently sent me a New Yorker article from 2010 about the “decline effect,” the tendency for initially promising scientific results to get smaller upon replication. Wikipedia can summarize the phenomenon as well as I can: In his article, Lehrer gives several examples where

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How to make a scientific result disappear

February 27, 2013
By
How to make a scientific result disappear

Nathan Danneman (a co-author and one of my graduate students from Emory) recently sent me a New Yorker article from 2010 about the “decline effect,” the tendency for initially promising scientific results to get smaller upon replication. Wikipedia can summarize the phenomenon as well as I can: In his article, Lehrer gives several examples where

Read more »

Ten Things the Emacs Social Science Starter Kit gives you

February 24, 2013
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I recently made some updates to the Emacs Social Science Starter Kit. I maintain the SSSK for my own convenience, but other people have found it useful as well. By now there are a lot of little bits and pieces in the kit, so I thought it might be usefu...

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Ten Things the Emacs Social Science Starter Kit gives you

February 24, 2013
By

I recently made some updates to the Emacs Social Science Starter Kit. I maintain the SSSK for my own convenience, but other people have found it useful as well. By now there are a lot of little bits and pieces in the kit, so I thought it might be usefu...

Read more »

Workflow w/ reports package

February 24, 2013
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Workflow w/ reports package

NOTE: THIS IS NOW A PACKAGE SEE THIS LINK FOR DETAILS Let me start with a video for people who just want to see what I’m demo-ing first: I’ve been interested in speeding up workflow lately and spending a lot … Continue reading →

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bigcor: Large correlation matrices in R

February 22, 2013
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bigcor: Large correlation matrices in R

As I am working with large gene expression matrices (microarray data) in my job, it is sometimes important to look at the correlation in gene expression of different genes. It has been shown that by calculating the Pearson correlation between genes, one can identify (by high values, i.e. > 0.9) genes that share a common

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