Convenience functions to quickly access and modify attributes of data frames of the class design; methods for the class are described in a separate help topic
undesign(design)
redesign(design, undesigned)
desnum(design)
desnum(design) <- value
run.order(design)
run.order(design) <- value
design.info(design)
design.info(design) <- value
factor.names(design)
factor.names(design, contr.modify = TRUE, levordold = FALSE) <- value
response.names(design)
response.names(design, remove=FALSE) <- value
col.remove(design, colnames)
ord(matrix, decreasing=FALSE)data frame of S3 class design. For the structures of design
objects, refer to the details section and to the value sections of the
functions that create them.
an object that is currently not a design but could be (e.g. obtained by applying function undesign
an appropriate replacement value:
a numeric version of the design matrix for function desnum (usage not encouraged for non-experts!)
a run order data frame for function run.order (usage not encouraged for non-experts!)
a list with appropriate design information for function design.info (usage not encouraged for non-experts!)
for function `factor.names<-` a character vector of new factor names (levels remain unchanged)
or a named list of level combinations for the factors,
like factor.names in function fac.design
for function `response.names<-` a character vector of response names referring to variables
which are already available in design
logical to indicate whether contrasts are to be modified to match the new levels;
relevant for R factors only, not for numeric design variables;
if TRUE, factors with 2 levels get -1/+1 contrasts, factors with more than two quantitative levels
get polynomial contrasts with scores identical to the factor levels, and factors with more than two
character levels get treatment contrasts; if FALSE, the contrasts remain unchanged from their previous state.
If solely the contrasts are to be changed, function change.contr is preferrable.
logical to indicate whether the level ordering should follow the old function behavior;
the new behavior (from version 0.27) is more plausible, in that the level ordering in the
new factor.names corresponds to the factor.names entry of the design.info
attribute; previously, the automatic level ordering of factor levels deviated from that order
which even led to a changed level order when reassigning exactly the factor.names element
of the design.info attribute
logical to indicate whether responses not indicated in value are to be removed from
the design altogether.
If TRUE, the respective columns are deleted from the design.
Otherwise, the columns remain in the data frame but loose their status as a response variable.
character vector of names of columns to be removed from the design;
design factors or the block factor cannot be removed; with non-numeric variables,
the desnum attribute of the design may have to be manually modified for
removing the respective columns in some cases.
matrix, data frame or also object of class design that is to be ordered column by column
logical, indicates whether decreasing order or not (increasing is default)
returns a numeric matrix, the corresponding replacement function modifies a class design object
returns a 3-column data frame with standard and actual run order as well as a run order with replication identifiers attached; the corresponding replacement function modifies a class design object
returns the design.info attribute of the design;
the corresponding replacement function modifies a class design object
returns a named list the names of which are the names of the treatment factors of the design while the list elements are the vectors of levels for each factor
returns a class design object with modified
factor names information (renamed factors and/or changed factor levels);
returns a character vector of response names that
(names of numeric variables within the data frame design
that are to be treated as response variables ) ;
the corresponding replacement function modifies the design
returns a class design object with modified
response names information (add or remove numeric columns of the design
to or from set of response variables), and potentially response columns
removed from the design.
returns a class design object with some columns
removed from both the design itself and the desnum attribute.
Response columns may be removed, but factor or block columns may not.
returns an index vector that orders the matrix or data frame;
for example, design[ord(design),] orders the design in increasing order with respect to the first,
then the second etc. factor.
Function sample is used for the randomization
functionality of this package. With R version 3.6.0, the behavior of this
function has changed. Since the R version is not stored with a class design
object, please check carefully if a design you want to reproduce based on a given
creator or seed element of the design.info attribute
has the expected randomization order.
The randomization order of a design that was created with the default settings
under R version 3.6.0 or newer can only be reproduced with such a new R version.
If an R version 3.6.0 or newer is used for reproducing the randomization order
of a randomized design that was created with an R version before 3.6.0,
the RNGkind setting has to be modified:
RNGkind(sample.kind="Rounding")
activates the old behavior,
RNGkind(sample.kind="default")
switches back to the recommended new behavior.
For an example, see the documentation of the example data set VSGFS.
Items of class design are data frames with attributes. They are generated
by various functions that create experimental designs (cf. see also section), and
by various utility functions for designs like
the above extractor function for class design.
The data frame itself always contains the design in uncoded form. For many
design generation functions, these are factors. For designs for quantitative factors
(bbd, ccd, lhs, 2-level designs with center points), the design variables are numeric.
This is always indicated by the design.info element quantitative, for which all components
are TRUE in that case.
Generally, its attributes are desnum,
run.order, and design.info.
Attribute desnum contains
a numeric coded version of the design. For factor design variables, the content of
desnum depends on the contrast information of the factors (cf. change.contr
for modifying this).
Attribute run.order is a data frame
with run order information (standard order, randomized order, order with replication info),
and the details of design.info partly depend on the type of design.
design.info generally is a list with first element type,
further info on the design,
and some options of the design call regarding randomization and replication.
For almost all design types, elements include
number of runs (not adjusted for replications)
number of factors
named list, as can be handed to function oa.design
the integer number of replications (1=unreplicated)
logical indicating whether replications are only repeat runs but not truly replicated
logical indicating whether the experiment was randomized
integer seed for the random number generator note that the randomization behavior has changed with R version 3.6.0; section "Warning" provides information on reproducing randomized designs.
in the presence of response data only; the character vector identifying response columns in the data frame
contains the call or the list of menu settings within package RcmdrPlugin.DoE that led to creation of the design. Note that the randomization behavior has changed with R version 3.6.0; section "Warning" provides information on reproducing randomized designs.
For some design types, notably designs of types starting with “FrF2” and
designs that have been created by combining other designs,
there can be substantial additional information available from the design.info
attribute in specialized situations. Detailed information on the structure of the
design.info attribute
can be found in the value sections of the respective functions. A tabular overview
of the available design.info elements is given on the authors homepage.
Function undesign removes all design-related attributes from a class design
object; this may be necessary for making some independent code work on design objects.
(For example, function reshape from package stats does not
work on a class design object, presumably because of the specific extractor method for class design.)
Occasionally, one may also want
to reconnect a processed undesigned object to its design properties. This is the purpose of
function redesign.
The functions desnum, run.order, and design.info extract
the respective attribute, i.e. e.g. function design.info
extracts the design information for the design. The corresponding assignment
functions should only be used by very experienced users, as they may
mess up things badly if they are used naively .
The functions factor.names and response.names extract the
respective elements of the design.info attribute. The corresponding assignment
functions allow to change factor names and/or factor codes and to exclude or include
a numeric variable from the list of responses that are recognized as such by analysis
procedures. Note that the response.names function can (on request, not by default)
remove response variables from the data frame design. However, it is not directly able to
add new responses from outside the data frame design. This is what the
function add.response is for.
Function col.remove removes columns from the design and returns the
design without these columns and an intact class design structure.
Chambers, J.M. and Hastie, T.J. (1993). Statistical Models in S, Chapman and Hall, London.
See also the following functions known to produce objects of class
design: FrF2, pb,
fac.design, oa.design,
bbd.design, ccd.design,
ccd.augment, lhs.design,
as well as cross.design, param.design, and
utility functions in this package for reshaping designs.
There are also special methods for class design ([.design,
print.design, summary.design, plot.design)
# NOT RUN {
oa12 <- oa.design(nlevels=c(2,2,6))
#### Examples for factor.names and response.names
factor.names(oa12)
## rename factors
factor.names(oa12) <- c("First.Factor", "Second.Factor", "Third.Factor")
## rename factors and relabel levels of first two factors
namen <- c(rep(list(c("current","new")),2),list(""))
names(namen) <- c("First.Factor", "Second.Factor", "Third.Factor")
factor.names(oa12) <- namen
oa12
## add a few variables to oa12
responses <- cbind(temp=sample(23:34),y1=rexp(12),y2=runif(12))
oa12 <- add.response(oa12, responses)
response.names(oa12)
## temp (for temperature) is not meant to be a response
## --> drop it from responselist but not from data
response.names(oa12) <- c("y1","y2")
## looking at attributes of the design
desnum(oa12)
run.order(oa12)
design.info(oa12)
## undesign and redesign
u.oa12 <- undesign(oa12)
str(u.oa12)
u.oa12$new <- rnorm(12)
r.oa12 <- redesign(oa12, u.oa12)
## make known that new is also a response
response.names(r.oa12) <- c(response.names(r.oa12), "new")
## look at design-specific summary
summary(r.oa12)
## look at data frame style summary instead
summary.data.frame(r.oa12)
# }
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