DoE.base (version 0.23-2)

fac.design: Function for full factorial designs

Description

Function for creating full factorial designs with arbitrary numbers of levels, and potentially with blocking

Usage

fac.design(nlevels=NULL, nfactors=NULL, factor.names = NULL, 
        replications=1, repeat.only = FALSE, randomize=TRUE, seed=NULL, 
        blocks=1, block.gen=NULL, block.name="Blocks", bbreps=replications, 
        wbreps=1)

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 wit

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 replic

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 comple

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 (integer number)

blocks

is the number of blocks into which the experiment is to be subdivided; it must be a prime or a product of prime numbers which occur as common divisors of the numbers of levels of several factors (cf. Details section). If the experiment is random

block.gen

provides block generating information. If blocks is a prime or a power of 2 (up to 2^8) or 3 (up to 3^5) or a product of powers of 2, 3, and an individual other prime, block.gen is not needed (but can be optiona

block.name

name of the block factor, default Blocks

bbreps

between block replications; these are always taken as genuine replications, not repeat runs; default: equal to replications; CAUTION: you should not modify bbreps if you do not work with blocks, because

wbreps

within block replications; whether or not these are taken as genuine replications depends on the setting of repeat.only

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. For changing the contrasts, use function change.contr. The design.info attribute of the data frame has the following elements: [object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Details

fac.design creates full factorial designs, i.e. the number of runs is the product of all numbers of levels. It is possible to subdivide the design into blocks (one hierarchy level only) by specifying an appropriate number of blocks. The method used is a generalization of the one implemented in function conf.design for symmetric factorials (i.e. factorials with all factors at the same prime number of levels) and related to the method described in Collings (1984, 1989); function conf.set from package conf.design is used for checking the confounding consequences of blocking. Note that the number of blocks must be compatible with the factor levels; it must factor into primes that occur with high enough frequency among the pseudo-factors of the design. This statement is now explained by an example: Consider a design with five factors at 2, 2, 3, 3, 6 levels. The 6-level factor can be thought of as consisting of two pseudo-factors, a 2-level and a 3-level pseudo-factor, according to the factorization of the number 6 into the two primes 2 and 3. It is possible to obtain two blocks by confounding the two-factor interaction of the two 2-level factors and the 2-level pseudo-factor of the 6-level factor, or to obtain three blocks by confounding the blocking factor with the three-factor interaction of the two three-level factors and the three-level pseudo-factor of the 6-level factor, or to get six blocks, by doing both simultaneously. It is also possible to obtain 4 or 9 or even 36 blocks, if one is happy to confound two-factor interactions with blocks. The 36 blocks are the product of the 4 blocks from the 2-level portion with the nine blocks from the 3-level portion. For each portion separately, there is a lookup-table for blocking possibilities (block.catlg), for up to 128 blocks in 256 runs, or up to 81 blocks in 243 runs. 5 blocks cannot be done for the above example design. Even if there were one additional factor at 5 levels, it would still not be possible to do a number of blocks with divisor 5, because this would confound the main effect of a factor with blocks and would thus generate an error. For any primes apart from 2 or 3, only one at a time can be handled automatically. For example, if a design has three 5-level factors, it can be automatically subdivided into 5 blocks by the option blocks=5. It is also possible to run the design in 25 blocks; however, as 25=5*5, this cannot be done automatically but has to be requested by specifying the block.gen option in addition to the blocks option (in this case, block.gen=rbind(c(1,0,1),c(1,1,0)) would do the job).

References

Collings, B.J. (1984). Generating the intrablock and interblock subgroups for confounding in general factorial experiments. Annals of Statistics 12, 1500--1509. Collings, B.J. (1989). Quick confounding. Technometrics 31, 107--110.

See Also

See also FrF2, oa.design, pb, conf.set, block.catlg

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))
  
  ### blocking designs
  fac.design(nlevels=c(2,2,3,3,6), blocks=6, seed=12345)
  ## the same design, now unnecessarily constructed via option block.gen
  ## preparation: look at the numbers of levels of pseudo factors
  ## (in this order)
  unlist(factorize(c(2,2,3,3,6)))
  ## or, for more annotation, factorize the unblocked design
  factorize(fac.design(nlevels=c(2,2,3,3,6)))
  ## positions 1 2 5 are 2-level pseudo factors
  ## positions 3 4 6 are 4-level pseudo factors
  ## blocking with highest possible interactions
  G <- rbind(two=c(1,1,0,0,1,0),three=c(0,0,1,1,0,1))
  plan.6blocks <- fac.design(nlevels=c(2,2,3,3,6), blocks=6, block.gen=G, seed=12345)
  plan.6blocks
  
  ## two blocks, default design, but unnecessarily constructed via block.gen
  fac.design(nlevels=c(2,2,3,3,6), blocks=2, block.gen=c(1,1,0,0,1,0), seed=12345)
  
  ## three blocks, default design, but unnecessarily constructed via block.gen
  fac.design(nlevels=c(2,2,3,3,6), blocks=3, block.gen=c(0,0,1,1,0,1), seed=12345)
  
  ## nine blocks
  ## confounding two-factor interactions cannot be avoided
  ## there are warnings to that effect
  G <- rbind(CD=c(0,0,1,1,0,0),CE2=c(0,0,1,0,0,1))
  plan.9blocks <- fac.design(nlevels=c(2,2,3,3,6), blocks=9, block.gen=G, seed=12345)

  ## further automatic designs 
  fac.design(nlevels=c(2,2,3,3,6), blocks=4, seed=12345)
  fac.design(nlevels=c(2,2,3,3,6), blocks=9, seed=12345)
  fac.design(nlevels=c(2,2,3,3,6), blocks=36, seed=12345)
  fac.design(nlevels=c(3,5,6,10), blocks=15, seed=12345)
  
  ## independently check aliasing
  ## model with block main effects and all two-factor interactions
  ## 6 factors: not aliased
  summary(plan.6blocks)
  alias(lm(1:nrow(plan.6blocks)~Blocks+(A+B+C+D+E)^2,plan.6blocks))
  ## 9 factors: aliased
  summary(plan.9blocks)
  alias(lm(1:nrow(plan.9blocks)~Blocks+(A+B+C+D+E)^2,plan.9blocks))