Description Usage Arguments Details Value Warning Note Author(s) References See Also Examples
Function for comfortably augmenting a design with Doptimal additional points; this functionality is still somewhat experimental.
1 2 3 
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  ## 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)

Loading required package: FrF2
Loading required package: DoE.base
Loading required package: grid
Loading required package: conf.design
Attaching package: 'DoE.base'
The following objects are masked from 'package:stats':
aov, lm
The following object is masked from 'package:graphics':
plot.design
The following object is masked from 'package:base':
lengths
Loading required package: rsm
creating full factorial with 72 runs ...
Call:
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)")
Experimental design of type Dopt
36 runs
Factor settings (scale ends):
eins zwei drei
1 100 10 25
2 150 20 15
3 200 30 5
4 250 5
5 15
6 25
Optimality criteria:
D Dea A G
7704.94759 0.57400 25.87194 0.64300
The design itself:
eins zwei drei
1 100 10 25
2 250 30 15
3 150 10 25
4 250 10 25
5 100 30 15
6 100 10 5
7 100 20 5
8 250 10 15
9 200 30 5
10 100 10 15
11 100 30 5
12 150 30 25
13 150 30 25
14 250 20 15
15 250 30 25
16 100 30 25
17 200 10 25
18 100 30 25
19 200 10 5
20 250 30 15
21 200 10 25
22 100 20 25
23 100 30 5
24 100 10 25
25 100 20 15
26 250 30 5
27 150 20 5
28 150 10 25
29 250 10 25
30 100 20 25
31 150 30 15
32 150 20 25
33 250 10 5
34 200 20 5
35 250 20 25
36 250 20 25
class=design, type= Dopt
creating full factorial with 72 runs ...
Multistepcall:
[[1]]
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)")
[[2]]
Dopt.augment(r.plan, m = 10)
Experimental design of type Dopt.augment
46 runs
Factor settings (scale ends):
eins zwei drei
1 100 10 25
2 150 20 15
3 200 30 5
4 250 5
5 15
6 25
Optimality criteria:
D Dea A G
7989.41609 0.67800 23.13393 0.72000
Responses:
[1] y
Multistepcall:
[[1]]
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)")
[[2]]
Dopt.augment(r.plan, m = 10)
Experimental design of type Dopt.augment
46 runs
Factor settings (scale ends):
eins zwei drei
1 100 10 25
2 150 20 15
3 200 30 5
4 250 5
5 15
6 25
Optimality criteria:
D Dea A G
7989.41609 0.67800 23.13393 0.72000
Responses:
[1] y
The design itself:
eins zwei drei y
1 100 10 25 0.165165891
2 250 30 15 4.049173744
3 150 10 25 0.497561991
4 250 10 25 0.415589896
5 100 30 15 0.578829477
6 100 10 5 0.353437944
7 100 20 5 0.458009635
8 250 10 15 0.907762646
9 200 30 5 0.594144909
10 100 10 15 0.704684057
11 100 30 5 0.293987896
12 150 30 25 0.511790328
13 150 30 25 0.154129567
14 250 20 15 0.391230760
15 250 30 25 0.393092868
16 100 30 25 1.219362025
17 200 10 25 0.110762453
18 100 30 25 1.850220832
19 200 10 5 1.538848408
20 250 30 15 0.269801339
21 200 10 25 0.535841253
22 100 20 25 0.443960262
23 100 30 5 0.751019964
24 100 10 25 0.700339963
25 100 20 15 0.747196888
26 250 30 5 0.569930912
27 150 20 5 0.801239928
28 150 10 25 0.331122637
29 250 10 25 0.315355607
30 100 20 25 0.214420138
31 150 30 15 0.001855051
32 150 20 25 0.465356582
33 250 10 5 0.161487472
34 200 20 5 0.961158087
35 250 20 25 2.254710020
36 250 20 25 0.758444119
40 250 10 25 0.190539416
99 200 10 25 1.009786613
45 100 30 25 0.724731431
37 100 10 25 0.786783104
61 100 10 5 1.754582180
97 100 10 25 0.238835329
48 250 30 25 0.972513148
96 250 30 15 0.104141131
78 150 20 5 0.510437427
105 100 30 25 0.242342544
class=design, type= Dopt.augment
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