summary.aov

0th

Percentile

Summarize an Analysis of Variance Model

Summarize an analysis of variance model.

Keywords
models, regression
Usage
## S3 method for class 'aov':
summary(object, intercept = FALSE, split,
        expand.split = TRUE, keep.zero.df = TRUE, \dots)

## S3 method for class 'aovlist': summary(object, \dots)

Arguments
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.
Value

  • An object of class c("summary.aov", "listof") or "summary.aovlist" respectively.

    For 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.

Note

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.

See Also

aov, summary, model.tables, TukeyHSD

Aliases
  • summary.aov
  • summary.aovlist
  • print.summary.aov
  • print.summary.aovlist
Examples
library(stats) ## For a simple example see example(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)
Documentation reproduced from package stats, version 3.3, License: Part of R 3.3

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