pb: Function to generate non-regular fractional factorial...
In FrF2: Fractional Factorial Designs with 2-Level Factors
pb R Documentation
Function to generate non-regular fractional factorial screening designs
Description
The function generates Plackett-Burman designs and in some cases other screening
designs in run numbers that are a multiple of 4. These designs are particularly suitable for screening a
large number of factors, since interactions are not fully aliased with one main effect each
but partially aliased. (The design in 8 runs is an exception from this rule.)
Usage
pb(nruns, nfactors = nruns - 1, factor.names = if (nfactors <= 50)
Letters[1:nfactors] else paste("F", 1:nfactors, sep = ""),
default.levels = c(-1, 1), ncenter=0, center.distribute=NULL,
boxtyssedal = TRUE, n12.taguchi = FALSE,
replications = 1, repeat.only = FALSE,
randomize = TRUE, seed = NULL, oldver = FALSE, ...)
pb.list
Arguments
nruns
number of runs, must be a multiple of 4
nfactors
number of factors, default is nruns - 1,
and it is recommended to retain this default.
It is possible to specify factor names for fewer factors,
and the remaining columns will be named e1, e2, ...
They are useful for representing error in effects plots
(so-called dummy factors).
factor.names
a character vector of factor names (length up to nfactors)
or a list with nfactors elements;
if the list is named, list names represent factor names, otherwise default factor names are used;
the elements of the list are
EITHER vectors of length 2 with factor levels for the respective factor
OR empty strings.
For each factor with an empty string in factor.names, the levels given in
default.levels are used;
Default factor names are the first elements of the character vector Letters,
or the factors position numbers preceded by capital F in case of more than 50 factors.
default.levels
default levels (vector of length 2) for all factors for which no specific levels are given
ncenter
number of center points; ncenter > 0
is permitted, if all factors are quantitative
center.distribute
the number of positions over which the center points
are to be distributed ; if NULL (default), center points are
distributed over end, beginning, and middle (in that order, if there are fewer than three center points)
for randomized designs, and appended to the end for non-randomized designs.
for more detail, see function add.center, which does the work.
boxtyssedal
logical, relevant only for nruns=16.
If FALSE, the geometric (=standard) 16 run plan is used.
If TRUE, the proposal by Box and Tyssedal is used instead, which has the advantage (for screening) of aliasing each interaction with several main effects, like the other Plackett-Burman designs.
n12.taguchi
logical, relevant only for nruns=12. If TRUE, the 12 run design is given in Taguchi order.
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 replication takes place,
and all the repeat runs are conducted together. It is likely that the error
variation generated by such a procedure will be too small, so that average values
should be analyzed for an unreplicated design.
Otherwise (default), the full experiment is first carried out once, then
for the second replication and so forth. In case of randomization,
each such blocks is randomized separately. In this case, replication variance is
more likely suitable for usage as error variance
(unless e.g. the same parts are used for replication runs although build
variation is important).
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 complete experiment
is conducted in replications blocks, and each run occurs in each block.
randomize
logical. If TRUE, the design is randomized. This is the default.
seed
optional seed for the randomization process
In R version 3.6.0 and later, the default behavior of function sample
has changed. If you work in a new (i.e., >= 3.6.-0) R version and want to reproduce
a randomized design from an earlier R version (before 3.6.0),
you have to change the RNGkind setting by
RNGkind(sample.kind="Rounding")
before running function pb.
It is recommended to change the setting back to the new recommended way afterwards:
RNGkind(sample.kind="default")
For an example, see the documentation of the example data set VSGFS.
oldver
logical. If TRUE, the column ordering from
package versions 1.0-5 to 1.2.10 is used. This affects designs in
40, 52, 56, 64, 76, 92, 96 and 100 runs.
Usually, option oldver should not be set to is useful for reproducing an
old design, or for making a design in 40, 56, 64, 88 or 96 runs with
exactly half the number of factors resolution IV.
...
currently not used
Details
pb stands for Plackett-Burman. Plackett-Burman designs (Plackett and Burman 1946) are generally
used for screening many variables in relatively few runs, when interest is in
main effects only, at least initially. Different from the regular
fractional factorial designs created by function FrF2, they
do not perfectly confound interaction terms with main effects but distribute
interaction effects over several main effects. The designs with number of runs
a power of 2 are an exception to this rule: they are just the resolution III
regular fractional factorial designs and are as such not very suitable for
screening because of a high risk of very biased estimates for the main effects
of the factors. Where possible, these are therefore replaced by different designs (cf. below).
For most run numbers, function pb uses Plackett-Burman designs,
and simply fills columns from left to right.
The generating rows for these designs can be found in the list pb.list
(a 0 entry indicates that the design is constructed by a different method, e.g. doubling).
For 12 runs, the isomorphic design by Taguchi can be requested.
For 16 runs, the default is to use the designs suggested by Box and Tyssedal (2001),
which up to 14 factors do not suffer from perfect aliasing.
For 32 runs, a cyclic design with generating row given in Samset and Tyssedal (1999) is used.
For 64 runs, the 32 run design is doubled.
For 92 runs, a design is constructed according to the Williamson construction with matrices A, B, C and D from Hedayat and Stufken (1999), p. 160.
Designs up to 100~runs are covered.
Usage of the 8 run design for more than 4 factors is discouraged,
as it completely aliases main effects with individual two-factor interactions.
It is recommended to use at least the 12 run design instead for screening more than 4 factors.
Value
Value is a data frame of S3 class design
and has attached attributes that can be accessed
by functions desnum,
run.order and
design.info.
The data frame itself contains the design with levels coded as requested.
If no center points have been requested, the design columns are factors with
contrasts -1 and +1 (cf. also contr.FrF2); in case
of center points, the design columns are numeric.
The following attributes are attached to it:
desnum
Design matrix in -1/1 coding
run.order
three column data frame, first column contains the run number in
standard order, second column the run number as randomized, third column the
run number with replication number as postfix;
useful for switching back and forth between actual and standard run
number
design.info
list with entries
- type
character string “pb”, except for 8~runs with up to
4~factors, for which a type “FrF2” design is output
- nruns
number of runs (replications are not counted)
- nfactors
number of factors
- factor.names
list named with (treatment) factor names and containing as
entries vectors of length two each with coded factor levels
- ndummies
number of dummy factors for error
- replication
option setting in call to pb
- repeat.only
option setting in call to pb
- randomize
option setting in call to pb
- seed
option setting in call to pb
- creator
call to function pb
(or stored menu settings, if the function has been called via the R commander plugin
RcmdrPlugin.DoE)
Warning
With version 1.0-5 of package FrF2,
design generation for the designs based on doubling has changed (internal function
double.des). This affected designs for 40,56,64,88,96 runs.
With version 1.3 of package FrF2, this and further behaviors (52, 76) has changed
again, in the interest of improving generalized resolution of desigs produced by function pb.
For the affected run sizes, package versions from 1.0-5 onwards
cannot exactly reproduce pb designs that have been created with a version before
1.0-5. Package versions from 1.3 onwards reproduce the behavior of versions 1.0-5 to 1.2-10
through option oldver.
Warning
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 randomized design that was produced with an earlier R version,
please follow the steps described with the argument seed.
Author(s)
Ulrike Groemping
References
Box, G.E.P. and Tyssedal, J. (2001) Sixteen Run Designs of High Projectivity for Factor Screening.
Communications in Statistics - Simulation and Computation 30, 217-228.
Hedayat, A.S., Sloane, N.J.A. and Stufken, J. (1999) Orthogonal Arrays: Theory and Applications, Springer, New York.
Groemping, U. (2014). R Package FrF2 for Creating and Analyzing Fractional
Factorial 2-Level Designs. Journal of Statistical Software, 56,
Issue 1, 1-56. https://www.jstatsoft.org/v56/i01/.
Mee, R. (2009). A Comprehensive Guide to Factorial Two-Level Experimentation.
New York: Springer.
Plackett, R.L.; Burman, J.P. (1946) The design of optimum multifactorial experiments. Biometrika 33, 305-325.
Samset, O.; Tyssedal, J. (1999) Two-level designs with good projection properties. Technical Report 12,
Department of Mathematical Sciences, The Norwegian University of Science and Technology, Norway.
Williamson, J. (1946) Determinants whose elements are 0 and 1. American Mathematical Monthly 53, 427-434.
See Also
See also FrF2 for regular fractional factorial designs,
generalized.word.length for functions length3
and length4 used in examples
Examples
pb(12,randomize=FALSE)
pb(12,randomize=FALSE,n12.taguchi=TRUE)
pb(20,seed=29869)
pb(16,factor.names=list(A="",B="",C="",D=c("min","max"),
E="",F="",G="",H="",J=c("new","old")))
pb(8,default.levels=c("current","new"))
test <- pb(40) ## design created by doubling the 20 run design
pb(12, ncenter=6) ## 6 center points with default placement
## Not run:
## note: designs in 40, 56, 64, 88, and 96 runs are resolution IV,
## if the number of factors is up to nruns/2 - 1, e.g.:
plan1 <- pb(40, 19)
length3(plan1) ## 0 generalized words of length 3
length4(plan1) ## 228 generalized words of length 4
## they can be made resolution IV by oldver=TRUE for
## nfactors=nruns/2, e.g.:
plan2 <- pb(40, 20)
plan3 <- pb(40, 20, oldver=TRUE)
length3(plan2) ## 9 generalized words of length 3
length3(plan3) ## 0 generalized words of length 3
length4(plan3) ## 285 generalized words of length 4
## note: designs in 52, 76, and 100 runs are almost resolution IV,
## if the number of factors is up to nruns/2 - 1, e.g.:
plan4 <- pb(52, 25)
GR(plan4) ## generalized resolution 3.92
## note: versions >1.3 avoid complete and heavy aliasing of triples of factors
## for up to nruns-2 factors for 40, 52, 56, 64, 76, 88, 92 and 96 runs
## (the same for 100 runs, which were not implemented before version 1.3)
plan5 <- pb(40, 38)
plan6 <- pb(40, 38, oldver=TRUE)
GR(plan5) ## generalized resolution 3.4
GR(plan6) ## generalized resolution 3
plan7 <- pb(52, 50)
plan8 <- pb(52, 50, oldver=TRUE)
GR(plan7) ## generalized resolution 3.62
GR(plan8) ## generalized resolution 3.15
## End(Not run)
FrF2 documentation built on Sept. 20, 2023, 9:08 a.m.
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| pb | R Documentation |
Function to generate non-regular fractional factorial screening designs
Description
The function generates Plackett-Burman designs and in some cases other screening designs in run numbers that are a multiple of 4. These designs are particularly suitable for screening a large number of factors, since interactions are not fully aliased with one main effect each but partially aliased. (The design in 8 runs is an exception from this rule.)
Usage
pb(nruns, nfactors = nruns - 1, factor.names = if (nfactors <= 50)
Letters[1:nfactors] else paste("F", 1:nfactors, sep = ""),
default.levels = c(-1, 1), ncenter=0, center.distribute=NULL,
boxtyssedal = TRUE, n12.taguchi = FALSE,
replications = 1, repeat.only = FALSE,
randomize = TRUE, seed = NULL, oldver = FALSE, ...)
pb.list
Arguments
nruns |
number of runs, must be a multiple of 4 |
nfactors |
number of factors, default is nruns - 1,
and it is recommended to retain this default. |
factor.names |
a character vector of factor names (length up to nfactors)
or a list with |
default.levels |
default levels (vector of length 2) for all factors for which no specific levels are given |
ncenter |
number of center points; |
center.distribute |
the number of positions over which the center points
are to be distributed ; if NULL (default), center points are
distributed over end, beginning, and middle (in that order, if there are fewer than three center points)
for randomized designs, and appended to the end for non-randomized designs.
for more detail, see function |
boxtyssedal |
logical, relevant only for nruns=16. If FALSE, the geometric (=standard) 16 run plan is used. If TRUE, the proposal by Box and Tyssedal is used instead, which has the advantage (for screening) of aliasing each interaction with several main effects, like the other Plackett-Burman designs. |
n12.taguchi |
logical, relevant only for nruns=12. If TRUE, the 12 run design is given in Taguchi order. |
replications |
positive integer number. Default 1 (i.e. each row just once).
If larger, each design run is executed replication times.
If Otherwise (default), the full experiment is first carried out once, then for the second replication and so forth. In case of randomization, each such blocks is randomized separately. In this case, replication variance is more likely suitable for usage as error variance (unless e.g. the same parts are used for replication runs although build variation is important). |
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
|
randomize |
logical. If TRUE, the design is randomized. This is the default. |
seed |
optional seed for the randomization process |
oldver |
logical. If |
... |
currently not used |
Details
pb stands for Plackett-Burman. Plackett-Burman designs (Plackett and Burman 1946) are generally
used for screening many variables in relatively few runs, when interest is in
main effects only, at least initially. Different from the regular
fractional factorial designs created by function FrF2, they
do not perfectly confound interaction terms with main effects but distribute
interaction effects over several main effects. The designs with number of runs
a power of 2 are an exception to this rule: they are just the resolution III
regular fractional factorial designs and are as such not very suitable for
screening because of a high risk of very biased estimates for the main effects
of the factors. Where possible, these are therefore replaced by different designs (cf. below).
For most run numbers, function pb uses Plackett-Burman designs,
and simply fills columns from left to right.
The generating rows for these designs can be found in the list pb.list
(a 0 entry indicates that the design is constructed by a different method, e.g. doubling).
For 12 runs, the isomorphic design by Taguchi can be requested. For 16 runs, the default is to use the designs suggested by Box and Tyssedal (2001), which up to 14 factors do not suffer from perfect aliasing. For 32 runs, a cyclic design with generating row given in Samset and Tyssedal (1999) is used. For 64 runs, the 32 run design is doubled. For 92 runs, a design is constructed according to the Williamson construction with matrices A, B, C and D from Hedayat and Stufken (1999), p. 160.
Designs up to 100~runs are covered.
Usage of the 8 run design for more than 4 factors is discouraged, as it completely aliases main effects with individual two-factor interactions. It is recommended to use at least the 12 run design instead for screening more than 4 factors.
Value
Value is a data frame of S3 class design
and has attached attributes that can be accessed
by functions desnum,
run.order and
design.info.
The data frame itself contains the design with levels coded as requested.
If no center points have been requested, the design columns are factors with
contrasts -1 and +1 (cf. also contr.FrF2); in case
of center points, the design columns are numeric.
The following attributes are attached to it:
desnum |
Design matrix in -1/1 coding |
run.order |
three column data frame, first column contains the run number in standard order, second column the run number as randomized, third column the run number with replication number as postfix; useful for switching back and forth between actual and standard run number |
design.info |
list with entries
|
Warning
With version 1.0-5 of package FrF2,
design generation for the designs based on doubling has changed (internal function
double.des). This affected designs for 40,56,64,88,96 runs.
With version 1.3 of package FrF2, this and further behaviors (52, 76) has changed
again, in the interest of improving generalized resolution of desigs produced by function pb.
For the affected run sizes, package versions from 1.0-5 onwards
cannot exactly reproduce pb designs that have been created with a version before
1.0-5. Package versions from 1.3 onwards reproduce the behavior of versions 1.0-5 to 1.2-10
through option oldver.
Warning
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 randomized design that was produced with an earlier R version,
please follow the steps described with the argument seed.
Author(s)
Ulrike Groemping
References
Box, G.E.P. and Tyssedal, J. (2001) Sixteen Run Designs of High Projectivity for Factor Screening. Communications in Statistics - Simulation and Computation 30, 217-228.
Hedayat, A.S., Sloane, N.J.A. and Stufken, J. (1999) Orthogonal Arrays: Theory and Applications, Springer, New York.
Groemping, U. (2014). R Package FrF2 for Creating and Analyzing Fractional Factorial 2-Level Designs. Journal of Statistical Software, 56, Issue 1, 1-56. https://www.jstatsoft.org/v56/i01/.
Mee, R. (2009). A Comprehensive Guide to Factorial Two-Level Experimentation. New York: Springer.
Plackett, R.L.; Burman, J.P. (1946) The design of optimum multifactorial experiments. Biometrika 33, 305-325.
Samset, O.; Tyssedal, J. (1999) Two-level designs with good projection properties. Technical Report 12, Department of Mathematical Sciences, The Norwegian University of Science and Technology, Norway.
Williamson, J. (1946) Determinants whose elements are 0 and 1. American Mathematical Monthly 53, 427-434.
See Also
See also FrF2 for regular fractional factorial designs,
generalized.word.length for functions length3
and length4 used in examples
Examples
pb(12,randomize=FALSE)
pb(12,randomize=FALSE,n12.taguchi=TRUE)
pb(20,seed=29869)
pb(16,factor.names=list(A="",B="",C="",D=c("min","max"),
E="",F="",G="",H="",J=c("new","old")))
pb(8,default.levels=c("current","new"))
test <- pb(40) ## design created by doubling the 20 run design
pb(12, ncenter=6) ## 6 center points with default placement
## Not run:
## note: designs in 40, 56, 64, 88, and 96 runs are resolution IV,
## if the number of factors is up to nruns/2 - 1, e.g.:
plan1 <- pb(40, 19)
length3(plan1) ## 0 generalized words of length 3
length4(plan1) ## 228 generalized words of length 4
## they can be made resolution IV by oldver=TRUE for
## nfactors=nruns/2, e.g.:
plan2 <- pb(40, 20)
plan3 <- pb(40, 20, oldver=TRUE)
length3(plan2) ## 9 generalized words of length 3
length3(plan3) ## 0 generalized words of length 3
length4(plan3) ## 285 generalized words of length 4
## note: designs in 52, 76, and 100 runs are almost resolution IV,
## if the number of factors is up to nruns/2 - 1, e.g.:
plan4 <- pb(52, 25)
GR(plan4) ## generalized resolution 3.92
## note: versions >1.3 avoid complete and heavy aliasing of triples of factors
## for up to nruns-2 factors for 40, 52, 56, 64, 76, 88, 92 and 96 runs
## (the same for 100 runs, which were not implemented before version 1.3)
plan5 <- pb(40, 38)
plan6 <- pb(40, 38, oldver=TRUE)
GR(plan5) ## generalized resolution 3.4
GR(plan6) ## generalized resolution 3
plan7 <- pb(52, 50)
plan8 <- pb(52, 50, oldver=TRUE)
GR(plan7) ## generalized resolution 3.62
GR(plan8) ## generalized resolution 3.15
## End(Not run)
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