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R, JAGS and ggplot2
Last week a question was asked on the ggplot2 list about using ggplot2 and jags in R (). Here’s what was my answer (a bit updated): Using as an example the school dataset from R2WinBUGS package: Than...
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Beeswarm Plot with ggplot2
A colleague showed me results of his study project with beeswarm plots made by GraphPad. I was wondering if it could be implemented in R and more specifically with ggplot2. There is a R package...
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Reproducible Research Using Sweave and LaTeX
Here are the slides given during the Montreal R User Group workshop on April 30, 2012. RR-MtlRUsers.beamer
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Veterinary Epidemiologic Research: Linear Regression
This post will describe linear regression as from the book Veterinary Epidemiologic Research, describing the examples provided with R. Regression analysis is used for modeling the relationship between...
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Veterinary Epidemiologic Research: Linear Regression Part 2 – Checking...
We continue on the linear regression chapter the book Veterinary Epidemiologic Research. Using same data as last post and running example 14.12: Now we can create some plots to assess the major...
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Veterinary Epidemiologic Research: Linear Regression Part 3 – Box-Cox and...
In the previous post, I forgot to show an example of Box-Cox transformation when there’s a lack of normality. The Box-Cox procedure computes values of which best “normalises” the errors. value...
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Veterinary Epidemiologic Research: GLM – Logistic Regression
We continue to explore the book Veterinary Epidemiologic Research and today we’ll have a look at generalized linear models (GLM), specifically the logistic regression (chapter 16). In veterinary...
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Veterinary Epidemiologic Research: GLM – Logistic Regression (part 2)
Second part on logistic regression (first one here). We used in the previous post a likelihood ratio test to compare a full and null model. The same can be done to compare a full and nested model to...
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Veterinary Epidemiologic Research: GLM – Evaluating Logistic Regression...
Third part on logistic regression (first here, second here). Two steps in assessing the fit of the model: first is to determine if the model fits using summary measures of goodness of fit or by...
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Veterinary Epidemiologic Research: GLM (part 4) – Exact and Conditional...
Next topic on logistic regression: the exact and the conditional logistic regressions. Exact logistic regression When the dataset is very small or severely unbalanced, maximum likelihood estimates of...
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Veterinary Epidemiologic Research: Count and Rate Data – Poisson & Negative...
Still going through the book Veterinary Epidemiologic Research and today it’s chapter 18, modelling count and rate data. I’ll have a look at Poisson and negative binomial regressions in R. We use count...
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Veterinary Epidemiologic Research: Count and Rate Data – Zero Counts
Continuing on the examples from the book Veterinary Epidemiologic Research, we look today at modelling count when the count of zeros may be higher or lower than expected from a Poisson or negative...
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Veterinary Epidemiologic Research: Count and Rate Data – Poisson Regression...
As noted on paragraph 18.4.1 of the book Veterinary Epidemiologic Research, logistic regression is widely used for binary data, with the estimates reported as odds ratios (OR). If it’s appropriate for...
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Veterinary Epidemiologic Research: Modelling Survival Data – Non-Parametric...
Next topic from Veterinary Epidemiologic Research: chapter 19, modelling survival data. We start with non-parametric analyses where we make no assumptions about either the distribution of survival...
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Veterinary Epidemiologic Research: Modelling Survival Data – Semi-Parametric...
Next on modelling survival data from Veterinary Epidemiologic Research: semi-parametric analyses. With non-parametric analyses, we could only evaluate the effect one or a small number of variables. To...
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Veterinary Epidemiologic Research: Modelling Survival Data – Parametric and...
Last post on modelling survival data from Veterinary Epidemiologic Research: parametric analyses. The Cox proportional hazards model described in the last post make no assumption about the shape of the...
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Bias in Observational Studies – Sensitivity Analysis with R package episensr
When it’s time to interpret the study results from your observational study, you have to estimate if the effect measure you obtained is the truth, if it’s due to bias (systematic error, the effect...
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Bias in Observational Studies – Sensitivity Analysis with R package episensr
When it’s time to interpret the study results from your observational study, you have to estimate if the effect measure you obtained is the truth, if it’s due to bias (systematic error, the effect...
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Bias in Observational Studies – Sensitivity Analysis with R package episensr
When it’s time to interpret the study results from your observational study, you have to estimate if the effect measure you obtained is the truth, if it’s due to bias (systematic error, the effect...
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