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updated slides for ABC PhD course

February 7, 2012
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updated slides for ABC PhD course

Over the weekend, I have added a few slides referring to recent papers mentioning the convergence of ABC algorithms, in particular the very relevant paper by Dean et al. I had already discussed in an earlier post. (This is taking a larger chunk of my time than expected! I am glad I will use the

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"R": PLS Regression (Gasoline) – 004

February 7, 2012
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"R": PLS Regression (Gasoline) – 004

In the previous post we plot the Cross Validation predictions with:> plot(gas1, ncomp = 3, asp = 1, line = TRUE)We can plot the fitted values instead with:> plot(gas1, ncomp = 3, asp = 1, line = TRUE,which=train) Graphics are different:Of course, using "train" we get  overoptimisc statistics and we should look...

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Gauging Interest in a Montreal R User Group

February 7, 2012
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Gauging Interest in a Montreal R User Group

Some of us over at McGill’s Biology Graduate Student Association have been developing and delivering R/Statistics workshops over the last few years. Through invited graduate students and faculty, we have tackled  everything from multi-part introductory workshops to get your feet wet, to special topics such as GLMs, GAMs, Multi-model inference, Phylogenetic analysis, Bayesian modeling, Meta-analysis,

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Early-February flotsam

February 7, 2012
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Early-February flotsam

Mike Croucher at Walking Randomly points out an interesting difference in operator precedence for several mathematical packages to evaluate a simple operation 2^3^4. It is pretty much a divide between Matlab and Excel (does the later qualify as mathematical software?) … Continue reading →

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What’s new in futile.paradigm 2.0.4

February 6, 2012
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What’s new in futile.paradigm 2.0.4

Well this certainly took a while but the latest installment of my functional dispatching library for R is finally released …Continue reading »

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General Bayesian estimation using MHadaptive

February 6, 2012
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General Bayesian estimation using MHadaptive

If you can write the likelihood function for your model, MHadaptive will take care of the rest (ie. all that MCMC business). I wrote this R package to simplify the estimation of posterior distributions of arbitrary models. Here’s how it works: 1) Define your model (ie the likelihood * prior). In this example, lets build

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Multiple Factor Model – Building Fundamental Factors

February 4, 2012
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Multiple Factor Model – Building Fundamental Factors

This is the second post in the series about Multiple Factor Models. I will build on the code presented in the prior post, Multiple Factor Model – Fundamental Data, and I will show how to build Fundamental factors described in the CSFB Alpha Factor Framework. For details of the CSFB Alpha Factor Framework please read

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"R": PLS Regression (Gasoline) – 003

February 3, 2012
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"R": PLS Regression  (Gasoline) – 003

The gasoline data set has the spectra of 60 samples acquired by diffuse reflectance from 900 to 1700 nm. We saw how to plot the spectra in the previous post.Now, following the tutorial of Bjorn-Helge Mevik published in "R-News Volume 6/3, August 2006", we will do the PLS regression:gas1 <- plsr(octane~NIR, ncomp = 10,data = gasoline, validation...

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Monty Hall by simulation in R

February 3, 2012
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Monty Hall by simulation in R

(Almost) every introductory course in probability introduces conditional probability using the famous Monte Hall problem. In a nutshell, the problem is one of deciding on a best strategy in a simple game. In the game, the contestant is asked to select one of three doors. Behind one of the doors is a great prize (free

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speed of R, C, &tc.

February 2, 2012
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speed of R, C, &tc.

My Paris colleague (and fellow-runner) Aurélien Garivier has produced an interesting comparison of 4 (or 6 if you consider scilab and octave as different from matlab) computer languages in terms of speed for producing the MLE in a hidden Markov model, using EM and the Baum-Welch algorithms. His conclusions are that matlab is a lot

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