## Help! I have convergence warnings

After all the hard work of collecting the data, thinking about appropriate models, formatting the data, you are finally running your model, this is it you are going to get the long awaited results and BOUM you get out such kind of message: ## Warning in checkConv(attr(opt, "derivs"), opt\$par, ctrl = ## control\$checkConv, : Model … Continue reading Help! I have convergence warnings

## Adding standard errors for interaction terms

This is something that bugged me for some time, how do we add up standard errors? This is relevant when you fit a model with interaction terms and you are interested not only in the deviation between different categories in your data (like male, female juvenils) but also whether the effect of some covariates on … Continue reading Adding standard errors for interaction terms

## Count data: To Log or Not To Log

Count data are widely collected in ecology, for example when one count the number of birds or the number of flowers. These data follow naturally a Poisson or negative binomial distribution and are therefore sometime tricky to fit with standard LMs. A traditional approach has been to log-transform such data and then fit LMs to … Continue reading Count data: To Log or Not To Log

## 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 distributed, the variance should be homogenous across the … Continue reading Checking (G)LM model assumptions in R

## First Lecture ever

So .. just a quick post to share with you my first lecture ever. It was for a master course that my working group is running on Experimental design and statistics in ecology. I talked about tools for non-normal data in ecology. I felt rather confortable and I hope that my messages got somehow into … Continue reading First Lecture ever

## Generalized Linear Modelling in R (part 1)

In classical linear modelling we are assuming that the response variable (Y) is normally distributed, however for certain type of data like count data or presence/absence data this is not the case. There is in statistic an ensemble of technique called Generalized Linear Modelling (GLM in short) where the reponse variable follow one known distribution, … Continue reading Generalized Linear Modelling in R (part 1)