Posts Tagged ‘ graphics ’

Two figures on the accuracy of polygraphs as lie detectors.

September 2, 2016
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Two figures on the accuracy of polygraphs as lie detectors.

Here's a pair of figures from a 2003 report by the National Academies 'Committee to Review the Scientific Evidence on the Polygraph' (full text), which includes several well-known statisticians. The figure below shows the sensitivity versus false-positive rate for 52 controlled laboratory studies of naive examinees, untrained in polygraph countermeasures. Each study examinee was assigned … Continue reading Two figures on the accuracy of polygraphs as lie detectors. →

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Principal curves example (Elements of Statistical Learning)

April 21, 2016
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Principal curves example (Elements of Statistical Learning)

The bit of R code below illustrates the principal curves methods as described in The Elements of Statistical Learning, by Hastie, Tibshirani, and Friedman (Ch. 14; the book is freely available from the authors' website). Specifically, the code generates some bivariate data that have a nonlinear association, initializes the principal curve using the first (linear) principal … Continue reading Principal curves example (Elements of Statistical Learning) →

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"To Pie or Not To Pie" That is the question! Graph theory

March 2, 2016
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"To Pie or Not To Pie" That is the question! Graph theory

In several recent posts I have attempted to convey the nature of how the current primary season is funded (on the Democratic side). In order to assist in conveying this information I have employed several different analytical angles and graphical strat...

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Using SVG graphics in blog posts

February 9, 2016
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Using SVG graphics in blog posts

My traditional work flow for embedding R graphics into a blog post has been via a PNG files that I upload online. However, when I created a 'simple' graphic with only basic curves and triangles for a recent post, I noticed that the PNG output didn't lo...

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My Poster at Rocky 2015: Estimating parameters of the Hodgkin-Huxley cardiac cell model by integrating raw data from multiple types of voltage-clamp experiments

December 18, 2015
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My Poster at Rocky 2015: Estimating parameters of the Hodgkin-Huxley cardiac cell model by integrating raw data from multiple types of voltage-clamp experiments

I'm recently returned from the 2015 Rocky Mountain Bioinformatics Conference, where I presented the above poster. This is work with a colleague, Rick Gray, at the FDA. He and I collaborate on our NIH award "Optimal Design of Challenge-Response Experiments in Cardiac Electrophysiology" (HL118392) The (original) poster abstract is below, but the poster content is … Continue reading My Poster at Rocky 2015: Estimating parameters of the Hodgkin-Huxley cardiac cell…

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Reference Chart for Precision of Wilson Binomial Proportion Confidence Interval

October 16, 2015
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Reference Chart for Precision of Wilson Binomial Proportion Confidence Interval

I am often asked about the number of subjects needed to study a binary outcome, which usually leads to a discussion of confidence intervals for binary proportions, and the associated precision. Sometimes the precision is quantified as the width or half-width of a 95% confidence interval. For proportions, I like the Wilson score interval because … Continue reading Reference Chart for Precision of Wilson Binomial Proportion Confidence Interval →

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Delta Method Confidence Bands for Gaussian Mixture Density (Can Behave Badly)

October 9, 2015
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Delta Method Confidence Bands for Gaussian Mixture Density (Can Behave Badly)

This post follows from a previous post (2798), in which the delta method was used to create an approximate pointwise 95% confidence band for a Gaussian density estimate. Note that the quality of this estimate was not assessed (e.g., whether the band has the correct pointwise coverage). Here we extend that approach to the Gaussian … Continue reading Delta Method Confidence Bands for Gaussian Mixture Density (Can Behave Badly) →

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Delta Method Confidence Bands for Gaussian Density

October 2, 2015
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Delta Method Confidence Bands for Gaussian Density

During one of our Department's weekly biostatistics "clinics", a visitor was interested in creating confidence bands for a Gaussian density estimate (or a Gaussian mixture density estimate). The mean, variance, and two "nuisance" parameters, were simultaneously estimated using least-squares. Thus, the approximate sampling variance-covariance matrix (4x4) was readily available. The two nuisance parameters do not … Continue reading Delta Method Confidence Bands for Gaussian Density →

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Notes on Multivariate Gaussian Quadrature (with R Code)

September 25, 2015
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Notes on Multivariate Gaussian Quadrature (with R Code)

Statisticians often need to integrate some function with respect to the multivariate normal (Gaussian) distribution, for example, to compute the standard error of a statistic, or the likelihood function in of a mixed effects model. In many (most?) useful cases, these integrals are intractable, and must be approximated using computational methods. Monte-Carlo integration is one … Continue reading Notes on Multivariate Gaussian Quadrature (with R Code) →

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Recipe for Computing and Sampling Multivariate Kernel Density Estimates (and Plotting Contours for 2D KDEs).

September 19, 2015
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Recipe for Computing and Sampling Multivariate Kernel Density Estimates (and Plotting Contours for 2D KDEs).

The code snippet below creates the above graphic: ## radially symmetric kernel (Gussian kernel) RadSym

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