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Category: Blog

TensorGrasp

One major research program in our lab aims to understand how the motor system makes use of task related and perceptual information when planning and executing movement. For example, a right handed “reach and grasp” action is executed more quickly … Read more

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Misconceptions and useful counterexamples in statistics

This page contains an assortment of useful or informative examples/counterexamples in probability and statistics. I plan to continue updating this page as I come across new examples.

A Bayes factor which contradicts the posterior

The following is an example of … Read more

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Quantile models with random effects

More experimenting with the quantile regression model described here. Good regression models are multi-level, so I’ve been playing around at validating a quantile model with random effects.

The data

In the validation of the simple quantile regression model, we … Read more

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Linear models for quantiles

Most statistical models used in experimental psychology are designed to estimate the mean of a response variable given some set of predictors. This is all well and good when errors are largely symmetric and our predictors are expected to primarily … Read more

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Factorizing motion trajectories

It’s difficult to analyze trajectory data, since the “object” of analysis (the trajectory) can’t easily be summarized by a single value without throwing away large amounts of information, unless you already know what you’re looking for (e.g. if you’re looking … Read more

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Publication quality plots in R

The trend is slowly moving towards interactive plotting and reproducible research with e.g. knitR (which I fully support), but it seems to me that R/ggplot2 is still the best plotting system for static graphics. That said, ggplot’s stock settings … Read more

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Mixture modelling for reaction time distributions

I’m swimming in reaction time data at the moment. My usual approach to analyzing reaction times in cognitive psychology is through some sort of normal random effects model of the log or inverse RT. This is really only good for … Read more

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A note on the CLT

Introductory statistics courses (particularly when they’re taught outside of the statistics department) often gloss over the details of the central limit theorem, describing it only as something that let’s you do t-tests without worrying about normality. I recently came across … Read more

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Echo-state networks in R

Echo-state networks (ESN’s) are a class of neural network designed to work with temporal data. I normally don’t like neural networks, since they rarely actually tell you anything interpretable about your data (and they pretty much just do regression anyway), … Read more

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