generalized.word.length: Functions for calculating the generalized word length...
In DoE.base: Full Factorials, Orthogonal Arrays and Base Utilities for DoE Packages
generalized.word.length R Documentation
Functions for calculating the generalized word length pattern,
projection frequency tables or optimizing column selection within an array
Description
Functions length2, length3, length4 and length5 calculate the numbers of generalized words
of lengths 2, 3, 4, and 5 respectively, lengths calculates them all. Functions P3.3 and P4.4
calculate projection frequency tables, functions oa.min3, oa.min4, oa.min34,
oa.maxGR (deprecated), oa.minRelProjAberr,
oa.max3 and oa.max4 determine column allocations with minimum or maximum aliasing.
Function nchoosek is an auxiliary function
for calculating all subsets without replacement.
Usage
length2(design, with.blocks = FALSE, J = FALSE)
length3(design, with.blocks = FALSE, J = FALSE, rela = FALSE)
length4(design, with.blocks = FALSE, separate = FALSE, J = FALSE, rela = FALSE)
length5(design, with.blocks = FALSE, J = FALSE, rela = FALSE)
lengths(design, ...)
## Default S3 method:
lengths(design, ...)
## S3 method for class 'design'
lengths(design, ...)
## S3 method for class 'matrix'
lengths(design, ...)
contr.XuWu(n, contrasts=TRUE)
contr.XuWuPoly(n, contrasts=TRUE)
oa.min3(ID, nlevels, all, rela = FALSE, variants = NULL, crit = "total")
oa.min4(ID, nlevels, all, rela = FALSE, variants = NULL, crit = "total")
oa.min34(ID, nlevels, variants = NULL, min3=NULL, all = FALSE, rela = FALSE)
oa.max3(ID, nlevels, rela = FALSE)
oa.max4(ID, nlevels, rela = FALSE)
oa.maxGR(ID, nlevels, variants = NULL)
oa.minRelProjAberr(ID, nlevels, maxGR = NULL)
P2.2(ID, digits = 4, rela=FALSE, parft=FALSE, parftdf=FALSE, detailed=FALSE)
P3.3(ID, digits = 4, rela=FALSE, parft=FALSE, parftdf=FALSE, detailed=FALSE)
P4.4(ID, digits = 4, rela=FALSE, parft=FALSE, parftdf=FALSE, detailed=FALSE)
nchoosek(n, k)
tupleSel(design, type="complete", selprop=0.25, ...)
## S3 method for class 'design'
tupleSel(design, type="complete", selprop=0.25, ...)
## Default S3 method:
tupleSel(design, type="complete", selprop=0.25, ...)
Arguments
design
an experimental design. This can either be a matrix or a data frame
in which all columns are experimental factors, or a special data frame
of class design, which may also include response data.
In any case, the design should be a factorial design;
the functions are not useful for quantitative designs (like e.g. latin hypercube samples).
with.blocks
a logical, indicating whether or not an existing block factor
is to be included into word counting. This option is ignored if design
is not of class design.
Per default, an existing block factor is ignored.
For designs without a block factor, the option does not have an effect.
If the design is blocked, and with.blocks
is TRUE, the block factor is treated like any other factor in terms of word counting.
J
a logical, indicating whether or not a vector of contributions from
individual degrees of freedom is produced. If TRUE, the entries of the vector
are absolute normalized J-characteristics from all 3- or 4-factor products respectively,
based on normalized Helmert contrasts (cf. Ai and Zhang 2004).
This is not expected to be useful for practical purposes.
J cannot be TRUE simultaneously with separate or rela.
rela
logical indicating whether the word lengths are to be calculated in
absolute terms (as usual) or relative to the maximum possible word length in case of
complete aliasing; if TRUE, each word length is divided by the worst
case word length (that corresponds to a completely aliased set of factors)
derived in Groemping (2011).
rela=TRUE is only permitted for the shortest possible word length,
i.e. length3 or P3.3 for resolution III designs,
length4 or P4.4 for resolution IV designs, or length5
for resolution V designs.
rela cannot be TRUE simultaneously with parft, parftdf,
J or separate.
separate
a logical, indicating whether or not separate (and overlapping)
sums are requested for each two-factor interaction;
the idea is to be able to identify clear two-factor interactions;
this may be useful for a design for which length3 returns zero,
in analogy to clear 2fis for regular fractional factorials,
implemented in function FrF2; this
is currently experimental and may be removed again if it does not prove useful.
separate cannot be TRUE simultaneously with J.
n
integer;
for functions contr.XuWu and contr.XuWuPoly:
number of levels of the factor for which to determine contrasts
for function nchoosek: number of elements to choose from
contrasts
must always be TRUE; option needed for
function model.matrix to work properly
ID
an orthogonal array, either a matrix or a data frame; need not be of class oa;
can also be a character string containing the name of an array listed in data frame oacat
nlevels
a vector of requested level informations (vector with an entry for each factor)
all
logical; if FALSE, the search stops whenever a design with 0 generalized words
of highest requested length is found; otherwise, the function always determines all best designs
variants
matrix of integer column number entries; each row gives the column numbers
for one variant to be compared; the matrix columns must correspond to the entries of the nlevels
option
crit
character string that requests "total" or "worst" triple optimization;
"total" corresponds to the previous version that optimizes the overall number of length 3 words;
"worst" minimizes the aliasing of the worst triple.
min3
the outcome of a call to oa.min3 with crit="total",
which is to be used for a call to oa.min34
maxGR
the outcome of a call to oa.min3 with crit="worst" and rela=TRUE
(or the outcome of a call to oa.maxGR), which is to be used for a call to oa.minRelProjAberr
digits
number of decimal points to which to round the result
parft
logical indicating whether to tabulate projection averaged R^2
values (see Groemping 2013) instead of word lengths;
if TRUE, the table shows projection averaged R^2 values as
detailed in Groemping (2013, 2017).
parft=TRUE is only permitted for the shortest possible word length,
i.e. length3 or P3.3 for resolution III designs,
length4 or P4.4 for resolution IV designs.
parft cannot be TRUE simultaneously with rela or parftdf.
parftdf
logical indicating whether to tabulate averaged R^2
values, where averaging is over individual degrees of freedom; this variant
is not explicitly described in Groemping (2013, 2017) and usually yields very similar
results as parft, except for some situations where there are factors
with very unequal numbers of levels (e.g. 2-level and 8-level factors).
parftdf=TRUE is only permitted for the shortest possible word length,
i.e. length3 or P3.3 for resolution III designs,
length4 or P4.4 for resolution IV designs.
parftdf cannot be TRUE simultaneously with rela or parft.
detailed
logical indicating whether the vector of all (relative) tuple word lengths
is to become an attribute of the output object (attribute detail);
intended for supporting other functions (can be a very long vector!)
k
number of elements to be chosen, integer from 0 to n
type
character string with type of worst case to consider;
"complete", "worst" and "worst.rel" are available.
For a resolution R design, tuples of R factors are considered (works for R=3 and R=4 only).
"complete" selects all tuples with complete aliasing of at least one factor,
"worst" selects all tuples whose number of words is larger than the 1-selprop
quantile of the word length distribution of R-tuples, "worst.rel" does the same
with relative words (i.e. increases the weight for tuples whose minimum number of levels
is small), "worst.parft" and "worst.parftdf" do the same with the
different versions of projection average R^2 values.
selprop
(approximate) proportion of worst case tuples to be selected (see type)
...
further arguments; for the design method of function lengths,
the defaults with.blocks = FALSE, J = FALSE can be changed here;
for function tupleSel, ... is currently not used
Details
These functions work for factors only and are not intended for quantitative variables.
Nevertheless it is possible to apply them to class design plans with quantitative
variables in them in some situations.
The generalized word length pattern as introduced in Xu and Wu (2001) is the basis
for the functions described here. Consult their article or Groemping (2011)
for rigorous mathematical detail of this concept. A brief explanation is also given here,
before explaining the details for the functions: Assume a design with qualitative
factors, for which all factors are coded with specially normalized Helmert contrasts
(which orthogonalizes the model matrix columns to the intercept column).
Functions contr.XuWu and contr.XuWuPoly provide such contrasts
based on Helmert contrasts or orthogonal polynomial contrasts,
normalized according to the prescription by Xu and Wu (2001)
which implies that all model matrix columns
have Euclidean norm sqrt(n), provided that each
individual factor is balanced.
Then, the number of generalized words of length 3 is determined by taking the sum
of squares of the column averages of all three-factor interaction columns
(from a model matrix with all three-factor interactions included).
Likewise, the number of generalized words of length 4 is determined by taking the sum
of squares of the column averages of all four-factor interaction columns
(from a model matrix with all four-factor interactions included), and so on.
A certain plausibility can be found in these numbers by noting that they provide the
more well-known word length pattern for regular fractional factorial 2-level designs,
implying that they are exactly zero for resolution IV or resolution V fractional
factorial 2-level designs, respectively. Furthermore, Groemping and Xu (2014) provided
an interpretation in terms of R^2-values from linear models for the number of
shortest words.
Function lengths calculates the generalized word length
pattern (numbers of generalized words of lengths 2, 3, 4 and 5 respectively),
functions length2, length3, length4 and length5 calculate
each length separately. For designs with few rows and many columns, the newer
function GWLP is much faster; therefore it will be a better choice
than lengths for most applications. On the other hand, for designs with
many rows, lengths can be much faster. Furthermore, lengths and
the compoment functions length2 to length5 can calculate additional
detail not available from GWLP.
The most important component length functions are
length3 and length4; length2
should yield zero for all orthogonal arrays, and length5 will in most
cases not be of interest either. The number of shortest possible words, e.g.
length 4 for resolution IV designs, can be calculated in relative terms, if
interest is in the extent of complete aliasing (cf. Groemping 2011).
The length functions are fast for small numbers of factors but can take a
long time if the number of factors is large. Note that an orthogonal array
based design is called resolution III if the result of
function length3 is non-zero, resolution IV,
if the result of function length3 is zero and the
result of function length4 is non-zero,
and resolution V+ (at least V), if the result of both functions length3
and length4 are zero.
Functions P3.3 and P4.4
calculate the pattern of generalized words of length 3 for all three-factor
projections of an array and of generalized words of length 3 or 4 for all four-factor
projections of an array. Calculation of such projection frequency tables has been proposed
by Xu, Cheng and Wu (2004). The relative version for P3.3 and P4.4 has been
introduced by Groemping (2011) for better assessment of the projective properties of a design.
It divides each absolute number of words by the maximum possible number in case one
factor is completely determined by the combinations of the other two factors.
For P4.4, the relative version is valid only for resolution IV designs.
NOTE: For mixed-level designs, it is meanwhile recommended to use
ARFTs (Groemping 2013, 2017) instead of relative P3.3 and P4.4;
these can be obtained by functions GRind or SCFTs
and have relevant advantages over the projection frequency tables from P3.3 and P4.4
for mixed level designs. SCFTs (also treated in Groemping 2013, 2017) provide more detail
than ARFTs and are interesting for assessing the suitability of a design for screening purposes.
The functions can be used in selecting among different possibilities to accomodate factors
within a given orthogonal array (cf. examples). For general purposes, it is recommended
to use designs with as small an outcome of length3 as possible (either absolute or relative,
either total or worst case), and within the same result
for length3 (particularly 0), with as small a result for length4 as possible. This
corresponds to (a step towards) generalized minimum aberration. It can also be useful
to consider the patterns, particularly P3.3, or for mixed levels the aforementioned
ARFTs or SCFTs obtainable with functions GRind or SCFTs.
Note that some overall information on a design's behavior is available in the catalogue data frames
oacat and oacat3 and can be queried with function show.oas;
this helps for selecting a suitable array from which to start optimization efforts (see below).
Functions oa.min3, oa.min4, oa.min34
optimize column allocation for a given array
for which a certain factor combination must be accomodated: They return designs that allocate
columns such that the number of generalized words of length 3 is minimized (oa.min3;
with a choice between minimizing the total number or minimizing the number for the worst-case triple
of factors), or
the number of generalized words of length 4 is minimized within all designs for which the number
of generalized words of length 3 is minimal (oa.min34, total number only);
oa.min4 does the same as oa.min3, but for designs of resolution IV, either entirely
(e.g. designs from oacat3) or through the selection of suitable column variants.
Option rela allows to switch
from the default consideration of absolute numbers of words to relative numbers of words
according to Groemping (2011). This relative number corresponds to concentrating on the worst-case
ARFT entry for each set of R factors (R the resolution).
Function oa.maxGR maximizes generalized resolution
according to Deng and Tang (1999) as generalized by Groemping (2011).
**Note that function oa.maxGR
can be replaced by the much faster function oa.min3 with options
crit="worst" and rela=TRUE, whenever GR<=4. Only for designs with
GR > 4, the extra effort with function oa.maxGR is useful.**
Function oa.minRelProjAberr conducts minimum relative projection aberration
according to Groemping (2011), with the four steps
(a) maximize GR (using function
oa.min3 with options crit="worst" and rela=TRUE),
(b) minimize rA3 or
rA4 (depending on resolution),
(c) optimize RPFT (as obtained by P3.3
or P4.4) and
(d) minimize absolute words of lengths 4 etc. (only carried through
to length 4 by the function).
Functions oa.max3 and oa.max4
do the opposite: they search for the worst design in terms of the number of
generalized words of lengths 3 or 4. Such a design can e.g. be used
for demonstrating the benefit of optimizing the number of words,
or for exemplifying theoretical properties.
Occasionally, it may also be useful,
if there are severe restrictions on possible combinations.
(oa.max4 should only be used for resolution IV designs.)
Function tupleSel selects worst case tuples of R factors for resolution R designs.
Depending on the type requested, all completely aliased tuples are selected,
or the worst case tuples that exceed the 1-selprop quantile of the numbers
of absolute or relative words are selected.
Value
The functions length3 and length4 (currently) per default
return the number of generalized words.
If option J=TRUE is set, their value
is a named vector of normalized absolute J-characteristics (cf. Ai and Zhang 2004)
for the respective length, based on normalized Helmert contrasts,
with names indicating factor indices.
(For blocked designs with the with.blocks=TRUE option,
the block factor has index 1.)
Functions P3.3 and P4.4 return a matrix
with the numbers of generalized words of length 3 (4) that do occur
for 3 (4) factor projections (column length3 or length4 resp.)
and their frequencies. If option rela=TRUE is set, the numbers of generalized
words are normalized by dividing them by the number of words that corresponds
to perfect aliasing among the factors for each projection. For P4.4, the
relative version is only reasonable for resolution IV designs.
The matrix of projection frequencies has the overall number of generalized words
of the respective length as an attribute; in the case rela=TRUE it also
has the generalized resolution and the overall absolute number of generalized words
of the respective length as an attribute.
The functions oa.min3, oa.min34, oa.max3 and oa.max4
(currently) return a list with elements
GWP (the number(s) of generalized words of length 3 (lengths 3 and 4))
column.variants (the columns to be used for design creation, ordered with
ascending nlevels) and complete (logical indicating whether or not the list is
guaranteed to be complete).
oa.min3, the name of the first element is either GWP3 (crit="total"),
worst.a3 (rela=FALSE, crit="worst") or GR (rela=FALSE, crit="worst").
The function oa.maxGR returns a list with elements GR, column.variants
and complete, the function oa.minRelProjAberr returns a list with elements
GR, GWP, column.variants and complete.
The function nchoosek returns a matrix with k rows and choose(n, k) columns,
each of which contains a different subset of k elements.
The function tupleSel returns a sorted list of worst case tuples,
beginning with the worst case. In case of types "worst" or "worst.rel",
attributes provide the (relative) projection frequency tables and the sorted
vector of the worst case projection values corresponding to the listed tuples.
Warning
The functions have been checked on the types of designs for which
they are intended (especially orthogonal arrays produced with oa.design) and
on 2-level fractional factorial designs produced with package FrF2.
They may produce meaningless results for some other types of designs.
Furthermore, all optimizing functions work for relatively small problems only
and will break down for larger problems because of storage space requirements
(size depends on the number of possible selections among columns; for example, selecting
9 out of 31 columns is not doable on my computer because of storage space issues,
while selecting 29 out of 31 columns is doable within the available storage space).
Programming of a less storage-intensive algorithm is underway.
Note
Function nchoosek has been taken from Bioconductor package vsn.
Function GWLP is much faster (but also more inaccurate) than function lengths
and may be a better choice for designs with many factors.
Author(s)
Ulrike Groemping
References
Ai, M.-Y. and Zhang, R.-C. (2004). Projection justification of generalized minimum
aberration for asymmetrical fractional factorial designs.
Metrika 60, 279–285.
Groemping, U. (2011). Relative projection frequency tables for orthogonal arrays.
Report 1/2011, Reports in Mathematics, Physics and Chemistry
http://www1.bht-berlin.de/FB_II/reports/welcome.htm,
Department II, Berliner Hochschule fuer Technik (formerly Beuth University of Applied Sciences), Berlin.
Groemping, U. (2013). Frequency tables for the coding invariant ranking of
orthogonal arrays. Report 2/2013, Reports in Mathematics, Physics and Chemistry
http://www1.bht-berlin.de/FB_II/reports/welcome.htm,
Department II, Berliner Hochschule fuer Technik (formerly Beuth University of Applied Sciences), Berlin.
Groemping, U. (2017). Frequency tables for the coding invariant quality assessment
of factorial designs. IISE Transactions 49(5), 505-517.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/0740817X.2016.1241458")}.
Xu, H.-Q. and Wu, C.F.J. (2001). Generalized minimum aberration for asymmetrical
fractional factorial designs. The Annals of Statistics 29, 1066–1077.
Xu, H., Cheng, S., and Wu, C.F.J. (2004). Optimal projective three-level
designs for factor screening and interaction detection. Technometrics 46,
280–292.
See Also
See also GWLP for a version of lengths that is much
faster for designs with not so many runs, and GRind
for another set of quality criteria for orthogonal arrays.
Package DoE.MIParray can create
arrays for smallish situations for which the catalogued arrays do not provide
satisfactory results; this package requires at least one of the commercial
softwares Mosek or Gurobi to be installed (both provide free academic licenses).
Examples
## check a small design
oa12 <- oa.design(nlevels=c(2,2,6))
length3(oa12)
## length4 is of course 0, because there are only 3 factors
P3.3(oa12)
## the results need not be an integer
oa12 <- oa.design(L12.2.11,columns=1:6)
length3(oa12)
length4(oa12)
P3.3(oa12) ## all projections have the same pattern
## which is known to be true for the complete L12.2.11 as well
P3.3(L18) ## this is the pattern of the Taguchi L18
## also published by Schoen 2009
P3.3(L18[,-2]) ## without the 2nd column (= the 1st 3-level column)
P3.3(L18[,-2], rela=TRUE) ## relative pattern, divided by theoretical upper
## bound for each 3-factor projection
## choosing among different assignment possibilities
## for two 2-level factors and one 3- and 4-level factor each
show.oas(nlevels=c(2,2,3,4))
## default allocation: first two columns for the 2-level factors
oa24.bad <- oa.design(L24.2.13.3.1.4.1, columns=c(1,2,14,15))
length3(oa24.bad)
## much better: columns 3 and 10
oa24.good <- oa.design(L24.2.13.3.1.4.1, columns=c(3,10,14,15))
length3(oa24.good)
length4(oa24.good) ## there are several variants,
## which produce the same pattern for lengths 3 and 4
## the difference matters
plot(oa24.bad, select=c(2,3,4))
plot(oa24.good, select=c(2,3,4))
## generalized resolution differs as well (resolution is III in both cases)
GR(oa24.bad)
GR(oa24.good)
## and analogously also GRind and ARFT and SCFT
GRind(oa24.bad)
GRind(oa24.good)
## GR and GRind can be different
GRind(L18[, c(1:4,6:8)], arft=FALSE, scft=FALSE)
## choices for columns can be explored with functions oa.min3, oa.min34 or oa.max3
oa.min3(L24.2.13.3.1.4.1, nlevels=c(2,2,3,4))
oa.min34(L24.2.13.3.1.4.1, nlevels=c(2,2,3,4))
## columns for designs with maximum generalized resolution
## (can take very long, if all designs have worst-case aliasing)
## then optimize these for overall relative number of words of length 3
## and in addition absolute number of words of length 4
mGR <- oa.maxGR(L18, c(2,3,3,3,3,3,3))
oa.minRelProjAberr(L18, c(2,3,3,3,3,3,3), maxGR=mGR)
oa.max3(L24.2.13.3.1.4.1, nlevels=c(2,2,3,4)) ## this is not for finding
## a good design!!!
## Not run:
## play with selection of optimum design
## somewhat experimental at present
oa.min3(L32.2.10.4.7, nlevels=c(2,2,2,4,4,4,4,4))
best3 <- oa.min3(L32.2.10.4.7, nlevels=c(2,2,2,4,4,4,4,4), rela=TRUE)
oa.min34(L32.2.10.4.7, nlevels=c(2,2,2,4,4,4,4,4))
oa.min34(L32.2.10.4.7, nlevels=c(2,2,2,4,4,4,4,4), min3=best3)
## generalized resolution according to Groemping 2011, manually
best3GR <- oa.min3(L36.2.11.3.12, c(rep(2,3),rep(3,3)), rela=TRUE, crit="worst")
## optimum GR is 3.59
## subsequent optimization w.r.t. rA3
best3reltot.GR <- oa.min3(L36.2.11.3.12, c(rep(2,3),rep(3,3)), rela=TRUE,
variants=best3GR$column.variants)
## optimum rA3 is 0.5069
## (note: different from first optimizing rA3 (0.3611) and then GR (3.5))
## remaining nine designs: optimize RPFTs
L36 <- oa.design(L36.2.11.3.12, randomize=FALSE)
lapply(1:9, function(obj) P3.3(L36[,best3reltot.GR$column.variants[obj,]]))
## all identical
oa.min34(L36, nlevels=c(rep(2,3),rep(3,3)), min3=best3reltot.GR)
## still all identical
## End(Not run)
## select among column variants with projection frequencies
## here, all variants have identical projection frequencies
## for larger problems, this may sometimes be relevant
variants <- oa.min34(L24.2.13.3.1.4.1, nlevels=c(2,2,3,4))
for (i in 1:nrow(variants$column.variants)){
cat("variant ", i, "\n")
print(P3.3(oa.design(L24.2.13.3.1.4.1, columns=variants$column.variants[i,])))
}
## automatic optimization is possible, but can be time-consuming
## (cf. help for oa.design)
plan <- oa.design(L24.2.13.3.1.4.1, nlevels=c(2,2,3,4), columns="min3")
length3(plan)
length4(plan)
plan <- oa.design(L24.2.13.3.1.4.1, nlevels=c(2,2,3,4), columns="min34")
length3(plan)
length4(plan)
## Not run:
## blocked design from FrF2
## the design is of resolution IV
## there is one (generalized) 4-letter word that does not involve the block factor
## there are four more 4-letter words involving the block factor
## all this and more can also be learnt from design.info(plan)
require(FrF2)
plan <- FrF2(32,6,blocks=4)
length3(plan)
length3(plan, with.blocks=TRUE)
length4(plan)
length4(plan, with.blocks=TRUE)
design.info(plan)
## End(Not run)
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| generalized.word.length | R Documentation |
Functions for calculating the generalized word length pattern, projection frequency tables or optimizing column selection within an array
Description
Functions length2, length3, length4 and length5 calculate the numbers of generalized words of lengths 2, 3, 4, and 5 respectively, lengths calculates them all. Functions P3.3 and P4.4 calculate projection frequency tables, functions oa.min3, oa.min4, oa.min34, oa.maxGR (deprecated), oa.minRelProjAberr, oa.max3 and oa.max4 determine column allocations with minimum or maximum aliasing. Function nchoosek is an auxiliary function for calculating all subsets without replacement.
Usage
length2(design, with.blocks = FALSE, J = FALSE)
length3(design, with.blocks = FALSE, J = FALSE, rela = FALSE)
length4(design, with.blocks = FALSE, separate = FALSE, J = FALSE, rela = FALSE)
length5(design, with.blocks = FALSE, J = FALSE, rela = FALSE)
lengths(design, ...)
## Default S3 method:
lengths(design, ...)
## S3 method for class 'design'
lengths(design, ...)
## S3 method for class 'matrix'
lengths(design, ...)
contr.XuWu(n, contrasts=TRUE)
contr.XuWuPoly(n, contrasts=TRUE)
oa.min3(ID, nlevels, all, rela = FALSE, variants = NULL, crit = "total")
oa.min4(ID, nlevels, all, rela = FALSE, variants = NULL, crit = "total")
oa.min34(ID, nlevels, variants = NULL, min3=NULL, all = FALSE, rela = FALSE)
oa.max3(ID, nlevels, rela = FALSE)
oa.max4(ID, nlevels, rela = FALSE)
oa.maxGR(ID, nlevels, variants = NULL)
oa.minRelProjAberr(ID, nlevels, maxGR = NULL)
P2.2(ID, digits = 4, rela=FALSE, parft=FALSE, parftdf=FALSE, detailed=FALSE)
P3.3(ID, digits = 4, rela=FALSE, parft=FALSE, parftdf=FALSE, detailed=FALSE)
P4.4(ID, digits = 4, rela=FALSE, parft=FALSE, parftdf=FALSE, detailed=FALSE)
nchoosek(n, k)
tupleSel(design, type="complete", selprop=0.25, ...)
## S3 method for class 'design'
tupleSel(design, type="complete", selprop=0.25, ...)
## Default S3 method:
tupleSel(design, type="complete", selprop=0.25, ...)
Arguments
design |
an experimental design. This can either be a matrix or a data frame
in which all columns are experimental factors, or a special data frame
of class |
with.blocks |
a logical, indicating whether or not an existing block factor
is to be included into word counting. This option is ignored if |
J |
a logical, indicating whether or not a vector of contributions from
individual degrees of freedom is produced. If |
rela |
logical indicating whether the word lengths are to be calculated in
absolute terms (as usual) or relative to the maximum possible word length in case of
complete aliasing; if |
separate |
a logical, indicating whether or not separate (and overlapping)
sums are requested for each two-factor interaction;
the idea is to be able to identify clear two-factor interactions;
this may be useful for a design for which |
n |
integer; |
contrasts |
must always be |
ID |
an orthogonal array, either a matrix or a data frame; need not be of class |
nlevels |
a vector of requested level informations (vector with an entry for each factor) |
all |
logical; if |
variants |
matrix of integer column number entries; each row gives the column numbers
for one variant to be compared; the matrix columns must correspond to the entries of the |
crit |
character string that requests |
min3 |
the outcome of a call to |
maxGR |
the outcome of a call to |
digits |
number of decimal points to which to round the result |
parft |
logical indicating whether to tabulate projection averaged |
parftdf |
logical indicating whether to tabulate averaged |
detailed |
logical indicating whether the vector of all (relative) tuple word lengths
is to become an attribute of the output object (attribute |
k |
number of elements to be chosen, integer from 0 to n |
type |
character string with type of worst case to consider; |
selprop |
(approximate) proportion of worst case tuples to be selected (see |
... |
further arguments; for the |
Details
These functions work for factors only and are not intended for quantitative variables.
Nevertheless it is possible to apply them to class design plans with quantitative
variables in them in some situations.
The generalized word length pattern as introduced in Xu and Wu (2001) is the basis
for the functions described here. Consult their article or Groemping (2011)
for rigorous mathematical detail of this concept. A brief explanation is also given here,
before explaining the details for the functions: Assume a design with qualitative
factors, for which all factors are coded with specially normalized Helmert contrasts
(which orthogonalizes the model matrix columns to the intercept column).
Functions contr.XuWu and contr.XuWuPoly provide such contrasts
based on Helmert contrasts or orthogonal polynomial contrasts,
normalized according to the prescription by Xu and Wu (2001)
which implies that all model matrix columns
have Euclidean norm sqrt(n), provided that each
individual factor is balanced.
Then, the number of generalized words of length 3 is determined by taking the sum
of squares of the column averages of all three-factor interaction columns
(from a model matrix with all three-factor interactions included).
Likewise, the number of generalized words of length 4 is determined by taking the sum
of squares of the column averages of all four-factor interaction columns
(from a model matrix with all four-factor interactions included), and so on.
A certain plausibility can be found in these numbers by noting that they provide the
more well-known word length pattern for regular fractional factorial 2-level designs,
implying that they are exactly zero for resolution IV or resolution V fractional
factorial 2-level designs, respectively. Furthermore, Groemping and Xu (2014) provided
an interpretation in terms of R^2-values from linear models for the number of
shortest words.
Function lengths calculates the generalized word length
pattern (numbers of generalized words of lengths 2, 3, 4 and 5 respectively),
functions length2, length3, length4 and length5 calculate
each length separately. For designs with few rows and many columns, the newer
function GWLP is much faster; therefore it will be a better choice
than lengths for most applications. On the other hand, for designs with
many rows, lengths can be much faster. Furthermore, lengths and
the compoment functions length2 to length5 can calculate additional
detail not available from GWLP.
The most important component length functions are
length3 and length4; length2
should yield zero for all orthogonal arrays, and length5 will in most
cases not be of interest either. The number of shortest possible words, e.g.
length 4 for resolution IV designs, can be calculated in relative terms, if
interest is in the extent of complete aliasing (cf. Groemping 2011).
The length functions are fast for small numbers of factors but can take a
long time if the number of factors is large. Note that an orthogonal array
based design is called resolution III if the result of
function length3 is non-zero, resolution IV,
if the result of function length3 is zero and the
result of function length4 is non-zero,
and resolution V+ (at least V), if the result of both functions length3
and length4 are zero.
Functions P3.3 and P4.4
calculate the pattern of generalized words of length 3 for all three-factor
projections of an array and of generalized words of length 3 or 4 for all four-factor
projections of an array. Calculation of such projection frequency tables has been proposed
by Xu, Cheng and Wu (2004). The relative version for P3.3 and P4.4 has been
introduced by Groemping (2011) for better assessment of the projective properties of a design.
It divides each absolute number of words by the maximum possible number in case one
factor is completely determined by the combinations of the other two factors.
For P4.4, the relative version is valid only for resolution IV designs.
NOTE: For mixed-level designs, it is meanwhile recommended to use
ARFTs (Groemping 2013, 2017) instead of relative P3.3 and P4.4;
these can be obtained by functions GRind or SCFTs
and have relevant advantages over the projection frequency tables from P3.3 and P4.4
for mixed level designs. SCFTs (also treated in Groemping 2013, 2017) provide more detail
than ARFTs and are interesting for assessing the suitability of a design for screening purposes.
The functions can be used in selecting among different possibilities to accomodate factors
within a given orthogonal array (cf. examples). For general purposes, it is recommended
to use designs with as small an outcome of length3 as possible (either absolute or relative,
either total or worst case), and within the same result
for length3 (particularly 0), with as small a result for length4 as possible. This
corresponds to (a step towards) generalized minimum aberration. It can also be useful
to consider the patterns, particularly P3.3, or for mixed levels the aforementioned
ARFTs or SCFTs obtainable with functions GRind or SCFTs.
Note that some overall information on a design's behavior is available in the catalogue data frames
oacat and oacat3 and can be queried with function show.oas;
this helps for selecting a suitable array from which to start optimization efforts (see below).
Functions oa.min3, oa.min4, oa.min34
optimize column allocation for a given array
for which a certain factor combination must be accomodated: They return designs that allocate
columns such that the number of generalized words of length 3 is minimized (oa.min3;
with a choice between minimizing the total number or minimizing the number for the worst-case triple
of factors), or
the number of generalized words of length 4 is minimized within all designs for which the number
of generalized words of length 3 is minimal (oa.min34, total number only);
oa.min4 does the same as oa.min3, but for designs of resolution IV, either entirely
(e.g. designs from oacat3) or through the selection of suitable column variants.
Option rela allows to switch
from the default consideration of absolute numbers of words to relative numbers of words
according to Groemping (2011). This relative number corresponds to concentrating on the worst-case
ARFT entry for each set of R factors (R the resolution).
Function oa.maxGR maximizes generalized resolution
according to Deng and Tang (1999) as generalized by Groemping (2011).
**Note that function oa.maxGR
can be replaced by the much faster function oa.min3 with options
crit="worst" and rela=TRUE, whenever GR<=4. Only for designs with
GR > 4, the extra effort with function oa.maxGR is useful.**
Function oa.minRelProjAberr conducts minimum relative projection aberration
according to Groemping (2011), with the four steps
(a) maximize GR (using function
oa.min3 with options crit="worst" and rela=TRUE),
(b) minimize rA3 or
rA4 (depending on resolution),
(c) optimize RPFT (as obtained by P3.3
or P4.4) and
(d) minimize absolute words of lengths 4 etc. (only carried through
to length 4 by the function).
Functions oa.max3 and oa.max4
do the opposite: they search for the worst design in terms of the number of
generalized words of lengths 3 or 4. Such a design can e.g. be used
for demonstrating the benefit of optimizing the number of words,
or for exemplifying theoretical properties.
Occasionally, it may also be useful,
if there are severe restrictions on possible combinations.
(oa.max4 should only be used for resolution IV designs.)
Function tupleSel selects worst case tuples of R factors for resolution R designs.
Depending on the type requested, all completely aliased tuples are selected,
or the worst case tuples that exceed the 1-selprop quantile of the numbers
of absolute or relative words are selected.
Value
The functions length3 and length4 (currently) per default
return the number of generalized words.
If option J=TRUE is set, their value
is a named vector of normalized absolute J-characteristics (cf. Ai and Zhang 2004)
for the respective length, based on normalized Helmert contrasts,
with names indicating factor indices.
(For blocked designs with the with.blocks=TRUE option,
the block factor has index 1.)
Functions P3.3 and P4.4 return a matrix
with the numbers of generalized words of length 3 (4) that do occur
for 3 (4) factor projections (column length3 or length4 resp.)
and their frequencies. If option rela=TRUE is set, the numbers of generalized
words are normalized by dividing them by the number of words that corresponds
to perfect aliasing among the factors for each projection. For P4.4, the
relative version is only reasonable for resolution IV designs.
The matrix of projection frequencies has the overall number of generalized words
of the respective length as an attribute; in the case rela=TRUE it also
has the generalized resolution and the overall absolute number of generalized words
of the respective length as an attribute.
The functions oa.min3, oa.min34, oa.max3 and oa.max4
(currently) return a list with elements
GWP (the number(s) of generalized words of length 3 (lengths 3 and 4))
column.variants (the columns to be used for design creation, ordered with
ascending nlevels) and complete (logical indicating whether or not the list is
guaranteed to be complete).
oa.min3, the name of the first element is either GWP3 (crit="total"),
worst.a3 (rela=FALSE, crit="worst") or GR (rela=FALSE, crit="worst").
The function oa.maxGR returns a list with elements GR, column.variants
and complete, the function oa.minRelProjAberr returns a list with elements
GR, GWP, column.variants and complete.
The function nchoosek returns a matrix with k rows and choose(n, k) columns,
each of which contains a different subset of k elements.
The function tupleSel returns a sorted list of worst case tuples,
beginning with the worst case. In case of types "worst" or "worst.rel",
attributes provide the (relative) projection frequency tables and the sorted
vector of the worst case projection values corresponding to the listed tuples.
Warning
The functions have been checked on the types of designs for which they are intended (especially orthogonal arrays produced with oa.design) and on 2-level fractional factorial designs produced with package FrF2. They may produce meaningless results for some other types of designs.
Furthermore, all optimizing functions work for relatively small problems only and will break down for larger problems because of storage space requirements (size depends on the number of possible selections among columns; for example, selecting 9 out of 31 columns is not doable on my computer because of storage space issues, while selecting 29 out of 31 columns is doable within the available storage space). Programming of a less storage-intensive algorithm is underway.
Note
Function nchoosek has been taken from Bioconductor package vsn.
Function GWLP is much faster (but also more inaccurate) than function lengths
and may be a better choice for designs with many factors.
Author(s)
Ulrike Groemping
References
Ai, M.-Y. and Zhang, R.-C. (2004). Projection justification of generalized minimum aberration for asymmetrical fractional factorial designs. Metrika 60, 279–285.
Groemping, U. (2011). Relative projection frequency tables for orthogonal arrays. Report 1/2011, Reports in Mathematics, Physics and Chemistry http://www1.bht-berlin.de/FB_II/reports/welcome.htm, Department II, Berliner Hochschule fuer Technik (formerly Beuth University of Applied Sciences), Berlin.
Groemping, U. (2013). Frequency tables for the coding invariant ranking of orthogonal arrays. Report 2/2013, Reports in Mathematics, Physics and Chemistry http://www1.bht-berlin.de/FB_II/reports/welcome.htm, Department II, Berliner Hochschule fuer Technik (formerly Beuth University of Applied Sciences), Berlin.
Groemping, U. (2017). Frequency tables for the coding invariant quality assessment of factorial designs. IISE Transactions 49(5), 505-517. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/0740817X.2016.1241458")}.
Xu, H.-Q. and Wu, C.F.J. (2001). Generalized minimum aberration for asymmetrical fractional factorial designs. The Annals of Statistics 29, 1066–1077.
Xu, H., Cheng, S., and Wu, C.F.J. (2004). Optimal projective three-level designs for factor screening and interaction detection. Technometrics 46, 280–292.
See Also
See also GWLP for a version of lengths that is much
faster for designs with not so many runs, and GRind
for another set of quality criteria for orthogonal arrays.
Package DoE.MIParray can create
arrays for smallish situations for which the catalogued arrays do not provide
satisfactory results; this package requires at least one of the commercial
softwares Mosek or Gurobi to be installed (both provide free academic licenses).
Examples
## check a small design
oa12 <- oa.design(nlevels=c(2,2,6))
length3(oa12)
## length4 is of course 0, because there are only 3 factors
P3.3(oa12)
## the results need not be an integer
oa12 <- oa.design(L12.2.11,columns=1:6)
length3(oa12)
length4(oa12)
P3.3(oa12) ## all projections have the same pattern
## which is known to be true for the complete L12.2.11 as well
P3.3(L18) ## this is the pattern of the Taguchi L18
## also published by Schoen 2009
P3.3(L18[,-2]) ## without the 2nd column (= the 1st 3-level column)
P3.3(L18[,-2], rela=TRUE) ## relative pattern, divided by theoretical upper
## bound for each 3-factor projection
## choosing among different assignment possibilities
## for two 2-level factors and one 3- and 4-level factor each
show.oas(nlevels=c(2,2,3,4))
## default allocation: first two columns for the 2-level factors
oa24.bad <- oa.design(L24.2.13.3.1.4.1, columns=c(1,2,14,15))
length3(oa24.bad)
## much better: columns 3 and 10
oa24.good <- oa.design(L24.2.13.3.1.4.1, columns=c(3,10,14,15))
length3(oa24.good)
length4(oa24.good) ## there are several variants,
## which produce the same pattern for lengths 3 and 4
## the difference matters
plot(oa24.bad, select=c(2,3,4))
plot(oa24.good, select=c(2,3,4))
## generalized resolution differs as well (resolution is III in both cases)
GR(oa24.bad)
GR(oa24.good)
## and analogously also GRind and ARFT and SCFT
GRind(oa24.bad)
GRind(oa24.good)
## GR and GRind can be different
GRind(L18[, c(1:4,6:8)], arft=FALSE, scft=FALSE)
## choices for columns can be explored with functions oa.min3, oa.min34 or oa.max3
oa.min3(L24.2.13.3.1.4.1, nlevels=c(2,2,3,4))
oa.min34(L24.2.13.3.1.4.1, nlevels=c(2,2,3,4))
## columns for designs with maximum generalized resolution
## (can take very long, if all designs have worst-case aliasing)
## then optimize these for overall relative number of words of length 3
## and in addition absolute number of words of length 4
mGR <- oa.maxGR(L18, c(2,3,3,3,3,3,3))
oa.minRelProjAberr(L18, c(2,3,3,3,3,3,3), maxGR=mGR)
oa.max3(L24.2.13.3.1.4.1, nlevels=c(2,2,3,4)) ## this is not for finding
## a good design!!!
## Not run:
## play with selection of optimum design
## somewhat experimental at present
oa.min3(L32.2.10.4.7, nlevels=c(2,2,2,4,4,4,4,4))
best3 <- oa.min3(L32.2.10.4.7, nlevels=c(2,2,2,4,4,4,4,4), rela=TRUE)
oa.min34(L32.2.10.4.7, nlevels=c(2,2,2,4,4,4,4,4))
oa.min34(L32.2.10.4.7, nlevels=c(2,2,2,4,4,4,4,4), min3=best3)
## generalized resolution according to Groemping 2011, manually
best3GR <- oa.min3(L36.2.11.3.12, c(rep(2,3),rep(3,3)), rela=TRUE, crit="worst")
## optimum GR is 3.59
## subsequent optimization w.r.t. rA3
best3reltot.GR <- oa.min3(L36.2.11.3.12, c(rep(2,3),rep(3,3)), rela=TRUE,
variants=best3GR$column.variants)
## optimum rA3 is 0.5069
## (note: different from first optimizing rA3 (0.3611) and then GR (3.5))
## remaining nine designs: optimize RPFTs
L36 <- oa.design(L36.2.11.3.12, randomize=FALSE)
lapply(1:9, function(obj) P3.3(L36[,best3reltot.GR$column.variants[obj,]]))
## all identical
oa.min34(L36, nlevels=c(rep(2,3),rep(3,3)), min3=best3reltot.GR)
## still all identical
## End(Not run)
## select among column variants with projection frequencies
## here, all variants have identical projection frequencies
## for larger problems, this may sometimes be relevant
variants <- oa.min34(L24.2.13.3.1.4.1, nlevels=c(2,2,3,4))
for (i in 1:nrow(variants$column.variants)){
cat("variant ", i, "\n")
print(P3.3(oa.design(L24.2.13.3.1.4.1, columns=variants$column.variants[i,])))
}
## automatic optimization is possible, but can be time-consuming
## (cf. help for oa.design)
plan <- oa.design(L24.2.13.3.1.4.1, nlevels=c(2,2,3,4), columns="min3")
length3(plan)
length4(plan)
plan <- oa.design(L24.2.13.3.1.4.1, nlevels=c(2,2,3,4), columns="min34")
length3(plan)
length4(plan)
## Not run:
## blocked design from FrF2
## the design is of resolution IV
## there is one (generalized) 4-letter word that does not involve the block factor
## there are four more 4-letter words involving the block factor
## all this and more can also be learnt from design.info(plan)
require(FrF2)
plan <- FrF2(32,6,blocks=4)
length3(plan)
length3(plan, with.blocks=TRUE)
length4(plan)
length4(plan, with.blocks=TRUE)
design.info(plan)
## End(Not run)
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