GWLP(design, ...)
## S3 method for class 'design':
GWLP(design, kmax=design.info(design)$nfactors,
attrib.out=FALSE, with.blocks = FALSE, digits = NULL, ...)
## S3 method for class 'default':
GWLP(design, kmax=ncol(design), attrib.out=FALSE, digits = NULL, ...)
Choose(n, k)
Kraw(k,x,n,q)
ham(c1, c2)
levels.no(xx)
levelmix(xx)
distDistmix(code, levm)
Bprime(dists, nmax=5)
dualDistmix(Bprime, nmax=5)
design
;
class design properties are exploited by using only factor columns
(or factor and block columns, if with.blocks
is TRUE
)TRUE
, the block column contributes to
the GWLP, otherwise it does notNULL
prevents roundingdesign
design
levelmix
distDistmix
,
analogous to the B_j1_j2 of p.1072 of Xu and Wu 2001kmax
in calls by other functionsBprime
, the MacWilliams transform
of the distance distributionGWLP
is intended for direct use.
The GWLP
methods output a named vector with the numbers of generalized
words of lengths zero to kmax
. If attrib.out
is TRUE
,
this vector comes with the attributes B
and levels.info
,
the latter documenting the level situation of the design, the former
the distance distribution B (Xu and Wu 2001).GWLP
is intended for direct use, the others are not.
Function GWLP
is much faster but also more inaccurate than the
function lengths
, which calculates numbers of words
for lengths 2 to 5 only. Note, however, that function lengths
can be faster for designs with very many rows.
Function ham
calculates the Hamming distance, function Kraw
the Krawtchouk polynomials, function Choose
differs from the base
function choose
by treatment of negative values n
,
functions levels.no
and levelmix
are utilities providing the
level information on the design xx
.
The functions distDistmix
, Bprime
and dualDistmix
implement formulae from Xu and Wu (2001) for the distance distribution,
its MacWilliams transform and the calculation of GWLP from the latter.lengths
GWLP(L18)
GWLP(L18, attrib.out=TRUE)
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