dummy.coef(object, ...) dummy.coef.lm(object, use.na = FALSE) dummy.coef.aovlist(object, use.na = FALSE) print.dummy.coef[.list](x, ..., title)
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."dummy.coef"
list giving for each term the values of
the coefficients. For a multistratum aov
model, such a list
(class "dummy.coef.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)