Example: Car seat occupation:
Algorithm must decide whether airbag opens:
- Must open for adult but not for small child or if the seat if empty;
- a few others I missed.
Key questions are:
- What type of design: 32 run regular fractional factorial;
- Response measurement – depends on dummy position, so repeat for 3 different;
- dummy places;
- Precision – are 32 rounds enough.
Use frf2 function in Ulrike’s package to generate experimental design.
Principals of DoE. Initially developed by Fisher. Key principles are blocking, randomization, replication. Repeated measurements are NOT replications.
In this example, there is high measurement error variance. Repeats done directly in sequence. Need to decide between replications and repeats – these are not the same! Balanced factorial experiments provide intrinsic replication.
George Box advocated that when creating experimental designs, you should proceed sequentially. Smaller initial screening. This does not apply to computer experiments.
Experimental Design Task View
Started in Feb 2008 and currently contains 37 packages. Maintainers need help, please point out relevant packages or complain about unhelpful packages. Of the 37 packages, only 18 have a dependency relation to others.
As with many packages, FrF2 and DOE.base were developed (in 2008) because someone needed them.
DoE.base -> FrF2 -> DoE.wrapper -> Gui interface (R commander plugin).
- DoE.base: for full factorial with blocking and orthogonal arrays;
- FrF2 2-level fractional factorials;
- DoE.wrapper – unify syntax.
Call for activities
A lot is available, but there are still gaps in functionality. If you have the expertise, why not write a package? Other possibilities are bug reports, suggestions for improvement, wishes, GUI beta testing, internationalization (not quite ready yet).