The functions create parameter designs for robustness experiments and signal-to-noise investigations with inner and outer arrays and facilitate their formatting and data aggregation.
an experimental design for the inner array,
data frame of class
an experimental design for the outer array,
data frame of class
character taking the values
parameter design in long format (created by function
character vector giving names of variables in addition to the experimental factors of the inner array that are constant over outer array runs for each inner array run
currently not used
A parameter design is an experimental plan for setting the so-called “control parameters” such that they achieve the intended function and at the same time minimize the effects of the so-called “noise parameters”. Note that the word parameters is used here in an engineering sense rather than in the typical sense it is used in statistics. The experiment crosses the control factors in the “inner array” with the noise factors in the “outer array”.
param.design uses function
creating an inner/outer array crossed design. There will be data aggregation
functions for such designs in the near future.
Note that designs created by
param.design are not properly randomized,
as they are conducted in the Taguchi inner / outer array sense with the runs of
the inner array as whole plots and the factors of the outer array as split-plot
factors. With analysis methods that work on data aggregated over the outer array
this is appropriate. If analysis of control and noise factor designs is to be conducted
in a combined approach, the experiment should be fully randomized. This can be
done using function
cross.design directly (cf. example there).
A data frame of class
design with type “param” or
“FrF2.param” for long version inner/outer array designs, and
type of the inner array suffixed with “.paramwide” for wide version
inner/outer array designs. The
design.info attribute of such designs has
the following extraordinary elements:
In long format, there are the same elements as for type
cross.design, and the additional elements
outer that give the names of the inner and outer array
In wide format, the
design.info information refers to the inner array,
cross... something are no longer available (except for
and the element
outer contains the outer array design the rows of which
correspond to the response columns. The additional element
format with value
“innerouterWide” indicates the wide format (introduced for analogy to wide
repeated measures designs), and
responselist shows the responses and their
respective columns in support of subsequent aggregation. Finally,
if there are variables that are neither experimental factors nor responses and
change within one run of the inner array, these are listed in
This function is still experimental.
NIST/SEMATECH e-Handbook of Statistical Methods, Section 5.5.6 (What are Taguchi Designs?), accessed August 11, 2009. http://www.itl.nist.gov/div898/handbook/pri/section5/pri56.htm
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## It is recommended to use param.design particularly with FrF2 designs. ## For the examples to run without package FrF2 loaded, ## oa.design designs are used here. ## quick preliminary checks to try out possibilities control <- oa.design(L18, columns=1:4, factor.names=paste("C",1:4,sep="")) noise <- oa.design(L4.2.3, columns=1:3, factor.names=paste("N",1:3,sep="")) ## long long <- param.design(control,noise) ## wide wide <- param.design(control,noise,direction="wide") wide long ## use proper labelled factors ## should of course be as meaningful as possible for your data fnc <- c(list(c("current","new")),rep(list(c("type1", "type2","type3")),3)) names(fnc) <- paste("C", 1:4, sep="") control <- oa.design(L18, factor.names=fnc) fnn <- rep(list(c("low","high")),3) names(fnn) <- paste("N",1:3,sep="") noise <- oa.design(L4.2.3, factor.names = fnn) ex.inner.outer <- param.design(control,noise,direction="wide",responses=c("force","yield")) ex.inner.outer ## export e.g. to Excel or other program with which editing is more convenient ## Not run: ### design written to default path as html and rda by export.design ### html can be opened with Excel ### data can be typed in ### for preparation of loading back into R, ### remove all legend-like comment that does not belong to the data table itself ### and store as csv ### reimport into R using add.response ### (InDec and OutDec are for working with German settings csv ### in an R with standard OutDec, i.e. wrong default option) getwd() ## look at default path, works on most systems export.design(ex.inner.outer, OutDec=",") add.response("ex.inner.outer", "ex.inner.outer.csv", "ex.inner.outer.rda", InDec=",") ## End(Not run)
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