model.tables              package:base              R Documentation

Compute Tables of Results from an Aov Model Fit.

Description:

     Computes summary tables for model fits, especially complex `aov'
     fits.

Usage:

     model.tables(x, ...)
     model.tables.aov(x, type = "effects", se = FALSE, cterms, ...)
     model.tables.aovlist(x, type = "effects", se = FALSE, ...)

Arguments:

       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.

Details:

     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.

Value:

     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.

Warning:

     The implementation is incomplete, and only the simpler cases have
     been tested thoroughly.

Author(s):

     B.D. Ripley

See Also:

     `aov'

Examples:

     ## 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)
     model.tables(npk.aov, "means", se=TRUE)

     ## as a test, not particularly sensible statistically
     options(contrasts=c("contr.helmert", "contr.treatment"))
     npk.aovE <- aov(yield ~  N*P*K + Error(block), npk)
     model.tables(npk.aovE,  se=TRUE)
     model.tables(npk.aovE, "means")

