Publications by grumble10
Checking (G)LM model assumptions in R
(Generalized) Linear models make some strong assumptions concerning the data structure: Independance of each data points Correct distribution of the residuals Correct specification of the variance structure Linear relationship between the response and the linear predictor For simple lm 2-4) means that the residuals should be normally distribute...
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Importing 100 years of climate change into R
This is a flashback post, I was working on species distribution shifts over the last 40 years last summer and recently Rémi Genevest contacted me asking me how I managed to import the CRU TS 1.2 dataset into R. As always a more readable version of the code can be found here. At that time I used a not very elegant coding involving SpatialPixels ...
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Regular expression and associated functions in R
When working with strings regular expressions are an extremely powerful tool to look for specific patterns in the strings. In informatics a string is several characters put together, this can be words, sentences, or DNA code. Regular expression were developed for the language of Perl (http://www.perl.org/) and have been since then implemented in ...
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Using bootMer to do model comparison in R
Setting the right random effect part in mixed effect models can be tricky in many applied situation. I will not talk here about choosing wether a grouping variable (sites, individuals …) should be included as a fixed term or as a random term, please see Gelman and Hill (2006) and Zuur et al (2009) for informations. Here I will present the use o...
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Ploting SEMs in R using semPlot
This is a short post presenting the great package semPlot developed by Sacha Epskamp (check out his nice website: http://sachaepskamp.com/) to make nice plots from your SEMs. SEMs are a modelling tool that allow the researcher to investiguate complex relationships between the variables, you may find here many links to free tutorials: http://www.s...
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DataFrame manipulation in R from basics to dplyr
In my surroundings at work I see quite a few people managing their data in spreadsheet software like Excel or Calc, these software will do the work but I usually tend to do as little data manipulation in them as possible and to turn as soon as possible my spreadsheets into csv files and then bring the data to R where every single manipulation I d...
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Interpreting regression coefficient in R
Linear models are a very simple statistical techniques and is often (if not always) a useful start for more complex analysis. It is however not so straightforward to understand what the regression coefficient means even in the most simple case when there are no interactions in the model. If we are not only fishing for stars (ie only interested if...
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Using and interpreting different contrasts in linear models in R
When building a regression model with categorical variables with more than two levels (ie “Cold”, “Freezing”, “Warm”) R is doing internally some transformation to be able to compute regression coefficient. What R is doing is that it is turning your categorical variables into a set of contrasts, this number of contrasts is the number o...
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Generating ANOVA-like table from GLMM using parametric bootstrap
This article may also be found on RPubs: http://rpubs.com/hughes/63269 In the list of worst to best way to test for effect in GLMM the list on http://glmm.wikidot.com/faq state that parametric bootstrapping is among the best options. PBmodcomp in the pbkrtest package implement such parametric bootstrapping by comparing a full model to a null one....
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A function to help graphical model checks of lm and ANOVA
As always a more colourful version of this post is available on rpubs. Even if LM are very simple models at the basis of many more complex ones, LM still have some assumptions that if not met would render any interpretation from the models plainly wrong. In my field of research most people were taught about checking ANOVA assumptions using tests ...
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