preprocessings {PTAk} | R Documentation |
Choices of centering or detrending and scaling are important preprocessings for multiway analysis.
Multcent(dat=X,bi=c(1,2),by=3, centre=mean, centrebyBA=c(TRUE,FALSE),scalebyBA=c(TRUE,FALSE)) IterMV(n=10,dat=X,Mm=c(1,3),Vm=c(2,3), fFUN=mean,usetren=FALSE, tren=function(x)smooth.spline(as.vector(x),df=5)$y, rsd=TRUE) Detren(dat,Mm=c(1,3),rsd=TRUE, tren=function(x)smooth.spline(as.vector(x),df=5)$y ) Susan1D(y,x=NULL,sigmak=NULL,sigmat=NULL, ker=list(function(u)return(exp(-0.5*u**2))))
function Multcent
dat |
array |
bi |
vector defining the "centering, bicentering or multi-centering" one wants
to operate crossed with by |
by |
number or vector defining the entries used "with" in the other operations |
centre |
function used as FUN in applying
"multi-centering" |
centrebyBA |
a bolean vector for "centering" with centre Before and After
according to by |
scalebyBA |
idem as centrebyBA, for scaling operation |
n |
number of iterations between "centering" and scaling |
Mm |
margins to performs Detren or fFUN on |
Vm |
margins to scale |
fFUN |
function to use as FUN if usetren is
FALSE |
usetren |
logical, to use Detren |
tren |
function to use in Detren |
rsd |
logical passed into Detren (only) to detrend or not |
y |
vector (length n ) |
x |
vector of same length, if NULL it is 1:n |
sigmak |
parameter related to kernel bandwidth with y
values (default is 1/2*range |
sigmat |
parameter related to kernel bandwidth with x
values (default value is 8*n^{-1/5} , with a minimum number of
neigbours set as one apart) |
ker |
a list of two kernels list("t"=function "k"=function
) for each weightings (if only one given it is used for
both) |
Multcent
performs in order "centering" by by
;
"multicentering" for every bi
with by
; then scale
(standard deviation) to one by by
.
IterMV
performs an iterative "detrending" and scaling
according to te margins defined (see Leibovici(2000) and references
in it).
Detren
detrends (or smooths if rsd
is FALSE
)
the data accoding to th margins given.
Susan1D
performs a non-linear kernel smoothing of y
against x
(both reordered in the function according to orders
of x
) with an usual kernel (t
) as for kernel
regression and a kernel (t
) for the values of y
(the
product of the kernels constitutes the non-linear weightings. This
function is adapted from SUSAN algorithm (see references).
Didier Leibovici c3s2i@free.fr
Smith S.M. and J.M. Brady (1997) SUSAN - a new approach to low level image processing. International Journal of Computer Vision, 23(1):45-78, May 1997.