qua.design {DoE.base}

Function to switch between qualitative and quantitative factors and different contrast settings

Description

The function allows to switch between qualitative and quantitative factors and different contrast settings.

Usage

qua.design(design, quantitative = NA, contrasts = character(0), ...)
change.contr(design, contrasts=contr.treatment)

Arguments

design

an experimental design, data frame of class design

quantitative

can be

EITHER

one of the single entries
NA for setting all factors to the default coding for class design (cf. details),
“all” for making all factors quantitative (=numeric),
“none” for making all factors qualitative (=factor)

OR

an unnamed vector of length nfactors with an entry TRUE, NA or FALSE for each factor, where TRUE makes a factor into a numeric variable, and FALSE makes it into a factor with treatment contrasts, and NA reinstates the default factor settings;

OR

a named vector (names from the factor names of the design) with an entry TRUE, NA or FALSE for each named factor (implying no change for the omitted factors)

contrasts

only takes effect for factors for which quantitative is FALSE;
the default character(0) does not change any contrasts vs.~the previous or default contrasts.

For customizing, a

character string
OR a character vector with a contrast name entry for each factor OR a named character vector of arbitrary length from 1 to number of factors

can be given; the names must correspond to names of factors to be modified, and entries must be names of contrast functions. The contrast functions are then applied to the respective factors with the correct number of levels.
Possible contrast function names include (at least) contr.FrF2 (for number of levels a power of 2 only), contr.helmert, contr.treatment, contr.SAS, contr.sum, contr.poly. CAUTION: Function qua.design checks whether the contrast names actually define a function, but it is not checked whether this function is a valid contrast function.

...

currently not used

Details

With function qua.design, option quantitative has the following implications:
An experimental factor for which quantitative is TRUE is recoded into a numeric variable.
An experimental factor for which quantitative is NA is recoded into an R-factor with the default contrasts given below.
An experimental factor for which quantitative is FALSE is recoded into an R-factor with treatment contrasts (default) or with custom contrasts as indicated by the contrasts parameter.
If the intention is to change contrasts only, function change.contr is a convenience interface to function qua.design.

The default contrasts for factors in class design objects (exception: purely quantitative design types like lhs or rsm designs) depend on the number and content of levels:
2-level experimental factors are coded as R-factors with -1/1 contrasts,
experimental factors with more than two quantitative (=can be coerced to numeric) levels are coded as R factors with polynomial contrasts (with scores the numerical levels of the factor),
and qualitatitve experimental factors with more than two levels are coded as R factors with treatment contrasts.

Note that, for 2-level factors, the default contrasts from function qua.design differ from the default contrasts with which the factors were generated in case of functions fac.design or oa.design. Thus, for recreating the original state, it may be necessary to explicity specify the desired contrasts.

Function change.contr makes all factors qualitative. Per default, treatment contrasts (cf. contr.treatment) are assigned to all factors. The default contrasts can of course be modified.

Warning: It is possible to misuse these functions especially for designs that have been combined from several designs. For example, while setting factors in an lhs design (cf. lhs.design) to qualitative is prevented, if the lhs design has been crossed with another design of a different type, it would be possible to make such a nonsensical modification.

Value

A data frame of class design; the element quantitative of attribute design.info, the data frame itself and the desnum attribute are modified as appropriate.

Author(s)

Ulrike Groemping

Examples

## usage with all factors treated alike
y <- rnorm(12)
plan <- oa.design(nlevels=c(2,6,2))
lm(y~.,plan)
lm(y~., change.contr(plan))   ## with treatment contrasts instead
plan <- qua.design(plan, quantitative = "none")
lm(y~.,plan)
plan <- qua.design(plan, quantitative = "none", contrasts=c(B="contr.treatment"))
lm(y~.,plan)
plan <- qua.design(plan, quantitative = "none")
lm(y~.,plan)

plan <- qua.design(plan, quantitative = "all")
lm(y~.,plan)
plan <- qua.design(plan)  ## NA resets to default state
lm(y~.,plan)

## usage with individual factors treated differently
plan <- oa.design(factor.names = list(liquid=c("type1","type2"), 
     dose=c(0,10,50,100,200,500), temperature=c(10,15)))
str(undesign(plan))
## Not run: 
## would cause an error, since liquid is character and cannot be reasonably coerced to numeric
plan <- qua.design(plan, quantitative = "all")

## End(Not run)
plan <- qua.design(plan, quantitative = "none")
str(undesign(plan))

plan <- qua.design(plan, quantitative = c(dose=TRUE,temperature=TRUE))
str(undesign(plan))
## reset all factors to default
plan <- qua.design(plan, quantitative = NA)
str(undesign(plan))
desnum(plan)
## add a response
y <- rnorm(12)
plan <- add.response(plan,y)
## set dose to treatment contrasts
plan <- qua.design(plan, quantitative = c(dose=FALSE), contrasts=c(dose="contr.treatment"))
str(undesign(plan))
desnum(plan)