dummy.coef(object, ...) dummy.coef.lm(object,) dummy.coef.aovlist(object, )
object
| a linear model fit |
use.na
|
logical flag for coefficients in a singular model. If
use.na is true, undetermined coefficients will be missing; if
false they will get one possible value.
|
contr.helmert or contr.sum
will be respected. There will be little point in using
dummy.coef for contr.treatment contrasts, as the missing
coefficients are by definition zero.aov model, such a list for each stratum.The results differ from S for singular values, where S can be incorrect.
aov, model.tables
options(contrasts=c("contr.helmert", "contr.poly"))
## 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)
dummy.coef(npk.aov)
npk.aovE <- aov(yield ~ N*P*K + Error(block), npk)
dummy.coef(npk.aovE)