Posts Tagged ‘ mixed models ’

Plotting 95% Confidence Bands in R

July 26, 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 had to ma...

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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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Model Validation: Interpreting Residual Plots

July 18, 2011
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Model Validation: Interpreting Residual Plots

When conducting any statistical analysis it is important to evaluate how well the model fits the data and that the data meet the assumptions of the model. There are numerous ways to do this and a variety of statistical tests to evaluate deviations from model assumptions. However, there is little general acceptance of any of the statistical tests. Generally...

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GLMM Hell

July 7, 2011
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GLMM Hell

I have been starting to analyze some data I have of repeated counts of salamanders from 5 plots over 4 years. I am trying to develop a predictive model of salamander nighttime surface activity as a function of weather variables. The repeated counting l...

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Multiple Comparisons for GLMMs using glmer() & glht()

June 14, 2011
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Multiple Comparisons for GLMMs using glmer() & glht()

...that's an example of how to apply multiple comparisons to a generalised linear mixed model using the function glmer from package lme4 & glht() from package multcomp. By the way you see a nice example for visualizing data from a nested sampli...

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Mixed models – Part 2: lme lmer

February 15, 2011
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Mixed models – Part 2: lme lmer

Getting more into mixed models, I’ve been playing around with both nlme::lme and lme4::lmer. http://tolstoy.newcastle.edu.au/R/e2/help/06/10/3345.html was quite a good post at explaining the differences, which from what I gather is largely performance based when using crossed or partially crossed models. In the models I am tinkering with at the moment I am noticing differences in

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Mixed Models – Part 1

February 3, 2011
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Mixed Models – Part 1

Very brief. Have been exploring mixed models in R using nlme::lme. Am looking forward to understanding them more, they’re going to be used more and more in years to come I’ve no doubt of that. Here are some scripts, very rough, for diagnostics when running simple 2 levels, or models with 1 grouping variable. CLICK

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Introducing Influence.ME: Tools for detecting influential data in mixed models

April 29, 2009
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I’m highly excited to announce that influence.ME is now available. Influence.ME is a new software package for R, providing statistical tools for detecting influential data in mixed models. It has been developed by Rense Nieuwenhuis, Ben Pelzer, a...

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