aov(formula, data=sys.parent(), projections=FALSE, contrasts=NULL, ...)
formula
| A formula specifying the model. |
data
| 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. |
projections
| Not implemented: for compatibility with S. |
contrasts
|
A list of contrasts to be used for some of the factors
in the formula. This is not supported by the current version of
model.matrix.default and so has no effect.
|
...
|
Arguments to be passed to lm , such as subset
or na.action .
|
lm
for fitting linear models to
balanced or unbalanced experimental designs. The call to lm
sets
singular.ok
to TRUE
to allow for aliased effects.
The main difference from lm
is in the way print
,
summary
and so on handle the fit: this is expressed in the
traditional language of the analysis of variance rather than of linear
models.
"lm"
with the additional class "aov"
.Error
is not allowed in the formula and there is no support for
multistratum models such as split-plot designs.lm
,alias
## From Venables and Ripley (1997) p.210. N <- c(0,1,0,1,1,1,0,0,0,1,1,0,1,1,0,0,1,0,1,0,1,1,0,0) P <- c(1,1,0,0,0,1,0,1,1,1,0,0,0,1,0,1,1,0,0,1,0,1,1,0) K <- c(1,0,0,1,0,1,1,0,0,1,0,1,0,1,1,0,0,0,1,1,1,0,1,0) yield <- c(49.5,62.8,46.8,57.0,59.8,58.5,55.5,56.0,62.8,55.8,69.5, 55.0, 62.0,48.8,45.5,44.2,52.0,51.5,49.8,48.8,57.2,59.0,53.2,56.0) npk <- data.frame(block=gl(6,4), N=factor(N), P=factor(P), K=factor(K), yield=yield) npk.aov <- aov(yield ~ block + N*P*K, npk) npk.aov summary(npk.aov) coefficients(npk.aov)