compromise {FrF2} | R Documentation |
Addelman (1962) and Ke and Wu (2005) discuss compromise plans of different types. Their creation is supported by the function compromise.
compromise(nfactors, G1, class=3)
nfactors |
overall number of factors |
G1 |
vector with indices of factors in group G1 (cf. details) |
class |
class of compromise designs that is to be generated; 1, 2, 3, or 4 |
For compromise plans, the factors are decomposed into a group G1 and a group G2.
The different classes of compromise plans require estimability of different subsets
of 2fis in addition to main effects:
Class 1: all 2fis within group G1 are estimable
Class 2: all 2fis within group G1 are estimable,
as well as all 2fis within group G2
Class 3: all 2fis within group G1 are estimable,
as well as all 2fis between groups G1 and G2
Class 4: all 2fis between groups G1 and G2 are estimable
The function returns three components (cf. section “Value”).
They can be used as input for the function FrF2
, if compromise
plans are to be created. Both Addelman (1962) and Ke, Tang and Wu (2005) designs
can be constructed, depending on the settings of option clear
in function
FrF2
. More explanations on specifying estimability requirements
for 2fis in general are provided under estimable.2fis
.
Value is a list of the three components perms.full
, requirement
,
and class
. The third component is purely imformative, while the first two
support calling function FrF2
.
requirement
can be used for specifying the required 2fis in the estimable
option,
both with clear=FALSE
and clear=TRUE
.
For clear=FALSE
, perms.full
can be used in the perms
option
for speeding up the search into a hopefully realistic time frame.
Ulrike Groemping
Addelman, S. (1962). Symmetrical and asymmetrical fractional factorial plans. Technometrics 4, 47-58.
Ke, W., Tang, B. and Wu, H. (2005). Compromise plans with clear two-factor interactions. Statistica Sinica 15, 709-715.
See Also FrF2
for creation of regular fractional factorial designs
as well as estimable.2fis
for statistical and algorithmic information on estimability of 2-factor interactions
## seven factors two of which are in group G1 C1 <- compromise(7, c(2,4), class=1) C1$perms.full ## the same for all classes C1$requirement C2 <- compromise(7, c(2,4), class=2) C2$requirement C3 <- compromise(7, c(2,4), class=3) C3$requirement C4 <- compromise(7, c(2,4), class=4) C4$requirement ## Not run: ########## usage of estimable ########################### ## design with with BD clear in 16 runs FrF2(16,7,estimable = C1$requirement) ## design with BD estimable on a distinct column in 16 runs (any design will do, ## if resolution IV!!! FrF2(16,7,estimable = C1$requirement, clear=FALSE, perms=C1$perms.full) ## all four classes, mostly clear, for 32 runs FrF2(32,7,estimable = C1$requirement) FrF2(32,7,estimable = C2$requirement) ## requires resolution V ## as clear class 2 compromise designs do not exist due to Ke et al. 2005 FrF2(32,7,estimable = C2$requirement, clear=FALSE, perms=C2$perms.full) FrF2(32,7,estimable = C3$requirement) FrF2(32,7,estimable = C4$requirement) ## two additional factors H and J that do not show up in the requirement set FrF2(32,9,estimable = C3$requirement) ## two additional factors H and J that do not show up in the requirement set FrF2(32,9,estimable = C3$requirement, clear=FALSE) ## note that this is not possible for distinct designs in case perms is needed, ## because perms must have nfactors columns ## End(Not run)