model.tables {stats} | R Documentation |
Computes summary tables for model fits, especially complex aov
fits.
model.tables(x, ...) ## S3 method for class 'aov': model.tables(x, type = "effects", se = FALSE, cterms, ...) ## S3 method for class 'aovlist': model.tables(x, type = "effects", se = FALSE, ...)
x |
a model object, usually produced by aov |
type |
type of table: currently only "effects" and
"means" are implemented. |
se |
should standard errors be computed? |
cterms |
A character vector giving the names of the terms for which tables should be computed. The default is all tables. |
... |
further arguments passed to or from other methods. |
For type = "effects"
give tables of the coefficients for each
term, optionally with standard errors.
For type = "means"
give tables of the mean response for each
combinations of levels of the factors in a term.
The "aov"
method cannot be applied to components of a
"aovlist"
fit.
An object of class "tables.aov"
, as list which may contain components
tables |
A list of tables for each requested term. |
n |
The replication information for each term. |
se |
Standard error information. |
The implementation is incomplete, and only the simpler cases have been tested thoroughly.
Weighted aov
fits are not supported.
aov
, proj
,
replications
, TukeyHSD
,
se.contrast
## From Venables and Ripley (2002) p.165. 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) options(contrasts=c("contr.helmert", "contr.treatment")) npk.aov <- aov(yield ~ block + N*P*K, npk) model.tables(npk.aov, "means", se = TRUE) ## as a test, not particularly sensible statistically npk.aovE <- aov(yield ~ N*P*K + Error(block), npk) model.tables(npk.aovE, se=TRUE) model.tables(npk.aovE, "means")