DoE.wrapper (version 0.8-10)

Dopt.design: Function for creating D-optimal designs with or without blocking from package AlgDesign

Description

Function for comfortably creating a D-optimal design with or without blocking based on functions optFederov or optBlock from package AlgDesign; this functionality is still somewhat experimental.

Usage

Dopt.design(nruns, data=NULL, formula=~., factor.names=NULL, nlevels=NULL, 
    digits=NULL, constraint=NULL, center=FALSE, nRepeats=5, seed=NULL, randomize=TRUE, 
    blocks=1, block.name="Blocks", wholeBlockData=NULL, ...)

Arguments

nruns

number of runs in the requested design

data

data frame or matrix of candidate design points; if data is specified, factor.names and levels are ignored

formula

a model formula (starting with a tilde), for the estimation of which a D-optimal design is sought; it can contain all column names from data or elements or element names from factor.names, respectively; usage

factor.names

is used for creating a candidate set (for the within Block factors) with the help of function fac.design, if data is not specified. It is a list of vect

nlevels

can be omitted if the list factor.names explicitly lists all factor levels (which of course defines the number of levels). For numeric factors for which factor.names only specifies the two scale ends, these ar

digits

is used for creating a candidate set if data is not specified. It specifies the digits to which numeric design columns are rounded in case of automatic creation of intermediate values. It can consist of one single value (t

constraint

a condition (character string!) used for reducing the candidate set to admissible points only. constraint is evaluated on the specified data set or after automatic creation of a full factorial candidate data set.

center

requests that optimization is run for the centered model; the design is nevertheless output in non-centered coordinates

nRepeats

number of independent repeats of the design optimization process; increasing this number may improve the chance of finding a global optimum, but will also increase search time

seed

seed for generation and randomization of the design (integer number); here, the seed is needed even if the design is not randomized, because the generation process for the optimum design involves random numbers, even if the order of th

randomize

logical deciding whether or not the design should be randomized; if it is TRUE, the design (or the additional portion of the design) returned by the workhorse function optF

blocks

a single integer giving the number of blocks (default 1, if no blocking is needed) OR a vector of block sizes which enable blocks of different sizes; for a scalar value, nruns must be divisible into blocks equally-sized blocks; for

block.name

character string: name of the blocking variable (used only if blocks are requested)

wholeBlockData

optional matrix or data frame that specifies the whole block characteristics; can only be used if blocks are requested; if used, it must have as many rows as there are block sizes. If this is specified, the resulting design is a split-plot design with the

...

additional arguments to functions optFederov or optBlock (if blocking is requested) from package AlgDesign

Value

• The function returns a data frame of S3 class design with attributes attached. The data frame contains the experimental settings. The matrix desnum attached as attribute desnum contains the model matrix of the design, using the formula as specified in the call. Function Dopt.augment preserves additional variables (e.g. responses) that have been added to the design design before augmenting. Note, however, that the response data are NOT used in deciding about which points to augment the design with. The attribute run.order provides the run number in standard order (as returned from function optFederov in package AlgDesign) as well as the randomized actual run order. The third column is always identical to the first. The attribute design.info is a list of various design properties, with type resolving to Dopt, Dopt.blocked, Dopt.splitplot. In addition to the standard list elements (cf. design), the element quantitative is a vector of nfactor logical values or NAs, and the optional digits elements indicates the number of digits to which the data were rounded. For blocked and splitplot designs, the list contains additional information on numbers and sizes of blocks or plots, as well as the number of whole plot factors (which are always the first few factors) and split-plot factors. The list contains a list of optimality criteria as calculated by function optFederov, see documentation there) with elements D, Dea, A and G. (Note that replications is always 1 and repeat.only is always FALSE; these elements are only present to fulfill the formal requirements for class design. Note however, that blocked designs do in fact repeat experimental runs if nruns and blocks imply this.)

Details

Function Dopt.design creates a D-optimal design, optionally with blocking, and even as a split-plot design. If no blocks are required, calculations are carried out through function optFederov from package AlgDesign. In case of blocked designs, function optBlock from package AlgDesign is behind the calculations. By specifying wholeBlockData, a blocked design becomes a split-plot design. The model formula can refer to both the within block data (only those are referred to by the . notation) and the whole block data and interactions between both. In comparison to direct usage of package AlgDesign, the function adds the possibility of automatically creating the candidate points on the fly, with or without constraints. Furthermore, it embeds the D-optimal designs into the class design. On the other hand, it sacrifices some of AlgDesigns flexibility; of course, users can still use AlgDesign directly. The D-optimal designs are particularly useful, if the classical regular designs are too demanding in run size requirements, or if constraints preclude automatic generation of orthogonal designs. Note, however, that the best design in few runs can still be very bad in absolute terms! When specifying the design without the data option, a full factorial in the requested factors is the default candidate set of design points. For some situations - especially with many factors - it may be better to start from a restricted candidate set. Such a candidate set can be produced with another R function, e.g. oa.design or FrF2, or can be manually created. If there are doubts, whether the process has delivered a design close to the absolute optimum, nRepeats can be increased. For unblocked designs, it is additionally possible to increase maxIteration. Also, improving the starting value by nullify=1 or nullify=2 may lead to an improved design. These options are handed through to function optFederov from package AlgDesign and are documented there.

References

Atkinson, A.C. and Donev, A.N. (1992). Optimum experimental designs. Clarendon Press, Oxford. Federov, V.V. (1972). Theory of optimal experiments. Academic Press, New York. Wheeler, R.E. (2004). Comments on algorithmic design. Vignette accompanying package AlgDesign. ../../AlgDesign/doc/AlgDesign.pdf.

See Also

See also optFederov, fac.design, quad, cubic, Dopt.augment

Examples

## a full quadratic model with constraint in three quantitative factors 
   plan <- Dopt.design(36,factor.names=list(eins=c(100,250),zwei=c(10,30),drei=c(-25,25)),
                          nlevels=c(4,3,6), 
                          formula=~quad(.), 
                          constraint="!(eins>=200 & zwei==30 & drei==25)")
   plan
   cor(plan)
   y <- rnorm(36)
   r.plan <- add.response(plan, y)
   plan2 <- Dopt.augment(r.plan, m=10)
   plot(plan2)
   cor(plan2)
   
   ## designs with qualitative factors and blocks for
   ## an experiment on assessing stories of social situations
   ## where each subject is a block and receives a deck of 5 stories
   plan.v <- Dopt.design(480, factor.names=list(cause=c("sick","bad luck","fault"), 
             consequences=c("alone","children","sick spouse"),
             gender=c("Female","Male"),
             Age=c("young","medium","old")),
             blocks=96,
             constraint="!(Age=="young" & consequences=="children")",
             formula=~.+cause:consequences+gender:consequences+Age:cause)
   ## an experiment on assessing stories of social situations
   ## with the whole block (=whole plot) factor gender of the assessor
   ##    not run for saving test time on CRAN
   plan.v.splitplot <- Dopt.design(480, factor.names=list(cause=c("sick","bad luck","fault"), 
             consequences=c("alone","children","sick spouse"),
             gender.story=c("Female","Male"),
             Age=c("young","medium","old")),
             blocks=96,
             wholeBlockData=cbind(gender=rep(c("Female","Male"),each=48)),
             constraint="!(Age==\"young\" & consequences==\"children\")",
             formula=~.+gender+cause:consequences+gender.story:consequences+
                 gender:consequences+Age:cause+gender:gender.story)