To put is plainly - running anova in R is a hot mess. It is a patchwork of functions from different packages and different authors with differing philosophies.pic.twitter.com/fEgd4gqix3
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To put is plainly - running anova in R is a hot mess. It is a patchwork of functions from different packages and different authors with differing philosophies.pic.twitter.com/fEgd4gqix3
The first thing to understand is formula notation, which generally takes the form: `formula(y ~ x)`. The formula itself is only a string of characters with the class "formula" attached to it - it is not (by itself) linked to any data.pic.twitter.com/7968KHEHTI
Main effects: x1 + x2 interactions: x1|x2 both: x1*x2 And like in other languages, check the variable type to make sure factors are factors or characters and integers are integers - so year 2020 is not read as 2,020.
I don't use aov() at all because the output is limited and it does not handle missing or unbalanced data well. Instead I run a linear model (or nonlinear or generalised model) followed by anova.
The function `Anova()` from the car package is preferred because it can handle type 1, 2 or 3 sums of squares easily.pic.twitter.com/XSQL1ebStk
There is a tremendous number of functions associated with an object of class "lm" - use the `methods()` to investigate this.pic.twitter.com/RvpdzqZW5T
To evaluate if a reduced model is statistically different from a fuller model, use `anova()`.pic.twitter.com/S7Q08klqab
For extracting treatment least squares means, the package "emmeans" provides an excellent set of options for obtaining the means, confidence intervals and performing all types of contrasts.pic.twitter.com/i4iSNPmQWA
"emmeans" has many types of pre-built contrasts to enable all sorts of common contrasts - pairwise, comparison to a control, comparison of linear v polynomial contrasts...many useful options! Search `?contrast.emmGrid` and `?`emmc-functions`` to learn more.pic.twitter.com/P9wwQ3D967
For mixed models, there are two main options: nlme and lme4. Both are useful and provide substantially equivalent results, provided the models are specified correctly. Here is an lme4 example. It calls an lmer version of `anova()`pic.twitter.com/1ZQlCCc716
Many of these functions with common names like "anova" are actually doing different things, depending on the object it acts upon:pic.twitter.com/ausnHBkS0k
objects from lme4 are "merMod" objects with a large number of accessory function that can act on them:pic.twitter.com/ZBf8NRCPIe
much of lme4 functionality is the same as "lm" objects. Likelihood ratio tests and the least squares of fixed effects can be accessed the same:pic.twitter.com/2RL1WymKEW
Accessing the variance components can be done with `VarCorr()`pic.twitter.com/65DPNY0P9k
What I have not talked about is assessing diagnostics. This is surprisingly easy - call `plot()` on lm, nlme or merMod objects to see some diagnostic plots. The function `bartlett.test()` can be used for testing for heteroscedasticity.
One of the most important things not discussed at all is having a discussion with the client about their research objects and what precisely they want to know - this guides my process more than anything. The next thing I do is plot the data prior to running any analyses.
This is the general framework I follow when helping a client conduct an analysis of variance. While this process may take different paths depending on the data - like a generalised linear model - the overall process is the same.
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