Dopt.augment {DoE.wrapper}  R Documentation 
Function for comfortably augmenting a design with Doptimal additional points; this functionality is still somewhat experimental.
Dopt.augment(design, m=1, formula=NULL, candidates=NULL, constraint=NULL,
center=FALSE, nRepeats=5,
seed=NULL, randomize=TRUE, ...)
design 
an experimental design of class 
m 
integer number of additional points to add to design 
formula 
a model formula (starting with a tilde), for the estimation of which a Doptimal design is sought; For quantitative factors, functions 
candidates 
data frame of candidate points; if not specified, candidates
are constructed as a full factorial from the

constraint 
a condition (character string!) used for reducing the candidate
set to admissible points only. 
center 
requests that optimization is run for the centered model; the design is nevertheless output in noncentered 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); 
randomize 
logical deciding whether or not the design should be randomized;
if it is 
... 
additional arguments to function 
Function Dopt.augment
augments an existing design by m
Doptimal
additional points (unblocked designs, no splitplot, no parameter or crossed design,
no repeat.only replications), i.e. by points that make the design particularly efficient
for the intended model.
Option center
, which is available for both blocked and unblocked designs as part of the ... argument,
requests optimization for the centered model; the design that is created is nevertheless an uncentered design.
NULL entries in the arguments are filled with automatic values that are determined
from design
.
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.
Note that the first n runs (the ones that are already present before augmentation)
have run numbers in standard order from 1 to n (i.e. their original run numbers
in standard order, if they were also generated by Dopt.design
are lost).
The attribute design.info
is a list of various design properties, with type resolving to “Dopt.augment”.
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.
The list contains further entries regarding the optimality that has been achieved
(D
, Dea
and A
).
Note that the original design is contained in the first rows of the new data set.
The original design also contains columns that are not directly part of the
design, e.g. comment columns.
Note that replications
is always set to 1, even if the original design was
replicated, and repeat.only
is always FALSE.
These elements are only present to fulfill the formal requirements for class design
.)
Since R version 3.6.0, the behavior of function sample
has changed
(correction of a biased previous behavior that should not be relevant for the randomization of designs).
For reproducing a design that was produced with an earlier R version,
please follow the steps described with the argument seed
.
This package is still under (slow) development. Reports about bugs and inconveniences are welcome.
Ulrike Groemping
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 optFederov
, fac.design
,
quad
, cubic
,
Dopt.design
. Furthermore, unrelated to function Dopt.augment
,
see also function gen_design
from package skpr
for a new general R package for creating Doptimal or other letter optimal designs.
## 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)")
summary(plan)
y < rnorm(36)
r.plan < add.response(plan, y)
plan2 < Dopt.augment(r.plan, m=10)
summary(plan2)
## add the new response values after conducting additional experiments
y < c(y, rnorm(10))
r.plan2 < add.response(plan2,y, replace=TRUE)
summary(r.plan2, brief=FALSE)