![]() ![]() 01: Linear models and statistical modelling 19: Data wrangling in dplyr, ggplot, tidy data 10: Intro to course, programming, RStudio, and R Markdown To compare models with different fix effects structure using anova use ML, but give final p-values with REML 8) With complex models you can drege them to find the best ones With different radom structures you should use method="REML" as an option. Intervals(m) 7) To compare models in nlme In addition, now we can extract the random effectsįixed and random factors, so we can see the random intercepts (and slopes) as well as Lme(y ~ x, random = ~1 | A) b) random slope model (we expect the trend to be dependent on species identity) –> “average effect in the population”, we use: a) random intercept model (we expect the trend to be equal, but some plants will differ on the intercept) Let’s start assuming normal error distributions: 1) Easy linear model with one predictor: m “the expectation of the distribution of effects”īut if we want to see if there are general trends regardless of A, but we want to control for the variability due to A. Predictors –This is what we think is affecting the response variable as x, z when continuos, and A, B when discrete with levels denoted by A1, A2, etc… Response variable –this is what we want to model as y. Plot everything and understand your data. Also, they may contain errors/discrepancies with your philosophy (please notify me if you find errors, so I can update it!) ![]() Note that those are oversimplified notes, they assume you know your stats and your R and only want a cheat sheet. Those are some notes to self, but may be they are useful to someone else. I am compiling some notes here to avoid visiting the same R-forums every time I work in a new project. I try to stick to general models whenever I can to avoid dealing with both random factors and complex error distributions (not always possible).
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