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

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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

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)