Blog Archives

Interactive exploration of a prior’s impact

February 21, 2014
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Interactive exploration of a prior’s impact

The probably most frequent criticism of Bayesian statistics sounds something like “It’s all subjective – with the ‘right’ prior, you can get any result you want.”. In order to approach this criticism it has been suggested to do a sensitivity analysis (or robustness analysis), that demonstrates how the choice of priors affects the conclusions drawn

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A short taxonomy of Bayes factors

January 21, 2014
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A short taxonomy of Bayes factors

I am starting to familiarize myself with Bayesian statistics. In this post I’ll show some insights I had concerning Bayes factors (BF). What are Bayes factors? Bayes factors provide a numerical value that quantifies how well a hypothesis predicts the empirical data relative to a competing hypothesis. For example, if the BF is 4, this

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New robust statistical functions in WRS package – Guest post by Rand Wilcox

September 16, 2013
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New robust statistical functions in WRS package – Guest post by Rand Wilcox

Today a new version (0.23.1) of the WRS package (Wilcox’ Robust Statistics) has been released. This package is the companion to his rather exhaustive book on robust statistics, “Introduction to Robust Estimation and Hypothesis Testing” (Amazon Link de/us). For a fail-safe installation of the package, follow this instruction. As a guest post, Rand Wilcox describes

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Exploring the robustness of Bayes Factors: A convenient plotting function

August 23, 2013
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Exploring the robustness of Bayes Factors: A convenient plotting function

One critique frequently heard about Bayesian statistics is the subjectivity of the assumed prior distribution. If one is cherry-picking a prior, of course the posterior can be tweaked, especially when only few data points are at hand. For example, see the Scholarpedia article on Bayesian statistics: In the uncommon situation that the data are extensive

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Exploring the robustness of Bayes Factors: A convenient plotting function

August 22, 2013
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Exploring the robustness of Bayes Factors: A convenient plotting function

One critique frequently heard about Bayesian statistics is the subjectivity of the assumed prior distribution. If one is cherry-picking a prior, of course the posterior can be tweaked, especially when only few data points are at hand. For example, see the Scholarpedia article on Bayesian statistics: In the uncommon situation that the data are extensive

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Finally! Tracking CRAN packages downloads

June 11, 2013
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Finally! Tracking CRAN packages downloads

The guys from RStudio now provide CRAN download logs (see also this blog post). Great work! I always asked myself, how many people actually download my packages. Now I finally can

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At what sample size do correlations stabilize?

June 6, 2013
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At what sample size do correlations stabilize?

Maybe you have encountered this situation: you run a large-scale study over the internet, and out of curiosity, you frequently check the correlation between two variables. My experience with this practice is usually frustrating, as in small sample sizes (and we will see what “small” means in this context) correlations go up and down, change sign,

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Installation of WRS package (Wilcox’ Robust Statistics)

April 22, 2013
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Some users had trouble installing the WRS package from R-Forge. Here’s a method that should work automatically and fail-safe: ?View Code RSPLUS# first: install dependent packages install.packages(c("MASS", "akima", "robustbase"))   # second: install suggested packages install.packages(c("cobs", "robust", "mgcv", "scatterplot3d", "quantreg", "rrcov", "lars", "pwr", "trimcluster", "parallel", "mc2d", "psych", "Rfit"))   # third: install WRS install.packages("WRS", repos="http://R-Forge.R-project.org",

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Improved evolution of correlations

January 21, 2013
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Update June 2013: A systematic analysis of the topic has been published:Schönbrodt, F. D., & Perugini, M. (2013). At what sample size do correlations stabilize? Journal of Research in Personality, 47, 609-612. doi:10.1016/j.jrp.2013.05.009 Check also the supplementary website, where you can find the PDF of the paper. As an update of this post: here’s an

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Optimizing parameters for an oscillator – Video

January 10, 2013
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Here’s a video how the modFit function from the FME package optimizes parameters for an oscillation. A Nelder-Mead-optimizer (R function optim) finds the best fitting parameters for an undampened oscillator. Minimum was found after 72 iterations, true parameter eta was -.05: Evolution of parameters in optimization process from Felix Schönbrodt on Vimeo. More on estimating

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