A reader asked in a comment to my post on interpreting two-way interactions if I could also explain interaction between two categorical variables and one continuous variable. Rather than just dwelling on this particular case, here is a full blog post with all possible combination of categorical and continuous variables and how to interprete standard … Continue reading Interpreting three-way interactions in R

# Tag: LM

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

[Updated 22nd January 2017, corrected mistakes for getting the fixed effect estimates of factor variables that need to be averaged out] [Updated 14th July 2017, the function is now on github: https://github.com/Lionel68/Blog/tree/master/PlotFit any modifications to it will be posted there before updating the post. The function has 2 new functionalities: (i) taking into account offset … Continue reading Plotting regression curves with confidence intervals for LM, GLM and GLMM in R

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

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 … Continue reading A function to help graphical model checks of lm and ANOVA

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 of … Continue reading Using and interpreting different contrasts in linear models 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 … Continue reading Interpreting regression coefficient 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