| summary.aov {stats} | R Documentation |
Summarize an analysis of variance model.
## S3 method for class 'aov':
summary(object, intercept = FALSE, split,
expand.split = TRUE, keep.zero.df = TRUE, ...)
## S3 method for class 'aovlist':
summary(object, ...)
object |
An object of class "aov" or "aovlist". |
intercept |
logical: should intercept terms be included? |
split |
an optional named list, with names corresponding to terms in the model. Each component is itself a list with integer components giving contrasts whose contributions are to be summed. |
expand.split |
logical: should the split apply also to interactions involving the factor? |
keep.zero.df |
logical: should terms with no degrees of freedom be included? |
... |
Arguments to be passed to or from other methods,
for summary.aovlist including those for summary.aov. |
An object of class c("summary.aov", "listof") or
"summary.aovlist" respectively.
For a fits with a single stratum the result will be a list of
ANOVA tables, one for each response (even if there is only one response):
the tables are of class "anova" inheriting from class
"data.frame". They have columns "Df", "Sum Sq",
"Mean Sq", as well as "F value" and "Pr(>F)" if
there are non-zero residual degrees of freedom. There is a row for
each term in the model, plus one for "Residuals" if there
are any.
For multistratum fits the return value is a list of such summaries,
one for each stratum.
The use of expand.split = TRUE is little tested: it is always
possible to set it to FALSE and specify exactly all
the splits required.
aov, summary, model.tables,
TukeyHSD
## 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)
( npk.aov <- aov(yield ~ block + N*P*K, npk) )
summary(npk.aov)
coefficients(npk.aov)
# Cochran and Cox (1957, p.164)
# 3x3 factorial with ordered factors, each is average of 12.
CC <- data.frame(
y = c(449, 413, 326, 409, 358, 291, 341, 278, 312)/12,
P = ordered(gl(3, 3)), N = ordered(gl(3, 1, 9))
)
CC.aov <- aov(y ~ N * P, data = CC , weights = rep(12, 9))
summary(CC.aov)
# Split both main effects into linear and quadratic parts.
summary(CC.aov, split = list(N = list(L = 1, Q = 2),
P = list(L = 1, Q = 2)))
# Split only the interaction
summary(CC.aov, split = list("N:P" = list(L.L = 1, Q = 2:4)))
# split on just one var
summary(CC.aov, split = list(P = list(lin = 1, quad = 2)))
summary(CC.aov, split = list(P = list(lin = 1, quad = 2)),
expand.split=FALSE)