fac.design {DoE.base}R Documentation

Function for full factorial designs

Description

Function for creating full factorial designs with arbitrary numbers of levels

Usage

fac.design(nlevels=NULL, nfactors=NULL, factor.names = NULL, 
        replications=1, repeat.only = FALSE, randomize=TRUE, seed=NULL)

Arguments

nlevels number(s) of levels, vector with nfactors entries or single number; can be omitted, if obvious from factor.names
nfactors number of factors, can be omitted if obvious from entries nlevels or factor.names
factor.names if nlevels is given, factor.names can be a character vector of factor names. In this case, default factor levels are the numbers from 1 to the number of levels for each factor.
Otherwise it must be a list of vectors with factor levels. If the list is named, list names represent factor names, otherwise default factor names are used. Default factor names are the first elements of the character vector Letters, or the factors position numbers preceded by capital F in case of more than 50 factors. If both nlevels and factor.names are given, they must be compatible.
replications positive integer number. Default 1 (i.e. each row just once). If larger, each design run is executed replication times. If repeat.only, repeated measurements are carried out directly in sequence, i.e. no true replication takes place, and all the repeat runs are conducted together. It is likely that the error variation generated by such a procedure will be too small, so that average values should be analyzed for an unreplicated design.
Otherwise (default), the full experiment is first carried out once, then for the second replication and so forth. In case of randomization, each such blocks is randomized separately. In this case, replication variance is more likely suitable for usage as error variance (unless e.g. the same parts are used for replication runs although build variation is important).
repeat.only logical, relevant only if replications > 1. If TRUE, replications of each run are grouped together (repeated measurement rather than true replication). The default is repeat.only=FALSE, i.e. the complete experiment is conducted in replications blocks, and each run occurs in each block.
randomize logical. If TRUE, the design is randomized. This is the default. In case of replications, the nature of randomization depends on the setting of option repeat.only.
seed optional seed for the randomization process

Details

fac.design creates full factorial designs, i.e. the number of runs is the product of all numbers of levels.

Value

fac.design returns a data frame of S3 class design with attributes attached.
The experimental factors are all stored as R factors.
For factors with 2 levels, contr.FrF2 contrasts (-1 / +1) are used.
For factors with more than 2 numerical levels, polynomial contrasts are used (i.e. analyses will per default use orthogonal polynomials).
For factors with more than 2 categorical levels, the default contrasts are used.
Future versions will most likely allow more user control about the type of contrasts to be used.
The design.info attribute of the data frame has the element nlevels in addition to the standard elements documented for class design.

Note

This package is currently under intensive development. Substantial changes are to be expected in the near future.

Author(s)

Ulrike Groemping

References

Hedayat, A.S., Sloane, N.J.A. and Stufken, J. (1999) Orthogonal Arrays: Theory and Applications, Springer, New York.

See Also

See also FrF2, oa.design, pb

Examples

  ## only specify level combination 
  fac.design(nlevels=c(4,3,3,2))
  ## design requested via factor.names
  fac.design(factor.names=list(one=c("a","b","c"), two=c(125,275), three=c("old","new"), four=c(-1,1), five=c("min","medium","max")))
  ## design requested via character factor.names and nlevels (with a little German lesson for one two three)
  fac.design(factor.names=c("eins","zwei","drei"),nlevels=c(2,3,2))

[Package DoE.base version 0.9-14 Index]