Fit an analysis of variance model by a call to
lm for each stratum.
aov(formula, data = NULL, projections = FALSE, qr = TRUE, contrasts = NULL, ...)
- A formula specifying the model.
- A data frame in which the variables specified in the formula will be found. If missing, the variables are searched for in the standard way.
- Logical flag: should the projections be returned?
- Logical flag: should the QR decomposition be returned?
- A list of contrasts to be used for some of the factors in the formula. These are not used for any
Errorterm, and supplying contrasts for factors only in the
Errorterm will give a warning.
- Arguments to be passed to
lm, such as
na.action. See ‘Details’ about
This provides a wrapper to
lm for fitting linear models to balanced or unbalanced experimental designs.
If the formula contains a single
Error term, this is used to specify error strata, and appropriate models are fitted within each error stratum.
The formula can specify multiple responses.
An object of class
c("aov", "lm") or for multiple responses of class
c("maov", "aov", "mlm", "lm") or for multiple error strata of class
"aovlist". There are
summary methods available for these.
Chambers, J. M., Freeny, A and Heiberger, R. M. (1992) Analysis of variance; designed experiments. Chapter 5 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.
aov is designed for balanced designs, and the results can be hard to interpret without balance: beware that missing values in the response(s) will likely lose the balance. If there are two or more error strata, the methods used are statistically inefficient without balance, and it may be better to use
lme in package nlme.
Balance can be checked with the
The default ‘contrasts’ in R are not orthogonal contrasts, and
aov and its helper functions will work better with such contrasts: see the examples for how to select these.
## From Venables and Ripley (2002) p.165. ## Set orthogonal contrasts. op <- options(contrasts = c("contr.helmert", "contr.poly")) ( npk.aov <- aov(yield ~ block + N*P*K, npk) ) summary(npk.aov) coefficients(npk.aov) ## to show the effects of re-ordering terms contrast the two fits aov(yield ~ block + N * P + K, npk) aov(terms(yield ~ block + N * P + K, keep.order = TRUE), npk) ## as a test, not particularly sensible statistically npk.aovE <- aov(yield ~ N*P*K + Error(block), npk) npk.aovE summary(npk.aovE) options(op) # reset to previous
Documentation reproduced from R 3.0.2. License: GPL-2.