Blog Archives

Conference Presentations

August 15, 2012
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Conference Presentations

I recently gave a talk at the Ecological Society of America (ESA) annual meeting in Portland, OR and a poster presentation at the World Congress of Herpetology meeting in Vancouver, BC, Canada. Both presentations were comparing generalized linear mixed models … Continue reading →

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Plotting 95% Confidence Bands in R

July 24, 2012
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Plotting 95% Confidence Bands in R

I am comparing estimates from subject-specific GLMMs and population-average GEE models as part of a publication I am working on. As part of this, I want to visualize predictions of each type of model including 95% confidence bands. First I … Continue reading →

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Installing and Running JAGS on Mac OS 10.5.8

March 26, 2012
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Installing and Running JAGS on Mac OS 10.5.8

JAGS is an alternative to BUGS (WinBUGS or OpenBUGS) for conducting a Bayesian Analysis. It stands for Just Another Gibbs Sampler, and like WinBUGS, it is essentially an MCMC machine that employs a Gibbs sampler so you don’t have to … Continue reading →

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R script to calculate QIC for Generalized Estimating Equation (GEE) Model Selection

March 23, 2012
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R script to calculate QIC for Generalized Estimating Equation (GEE) Model Selection

Generalized Estimating Equations (GEE) can be used to analyze longitudinal count data; that is, repeated counts taken from the same subject or site. This is often referred to as repeated measures data, but longitudinal data often has more repeated observations. … Continue reading →

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Plotting grouped data vs time with error bars in R

October 31, 2011
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Plotting grouped data vs time with error bars in R

This is my first blog since joining R-bloggers. I’m quite excited to be part of this group and apologize if I bore any experienced R users with my basic blogs for learning R or offend programmers with my inefficient, sloppy … Continue reading →

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Assumptions of the Linear Model

October 6, 2011
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Assumptions of the Linear Model

Linear Assumptions from the Analysis Factor – Assumptions of linear regression (and ANOVA) are about the residuals, not the normality or independence of the response variable (Y). If you don’t know what this means be sure to read this brief … Continue reading →

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