Basic information for 2-level screening designs

Share:

Description

Basic information for 2-level screening design Menu

Brief statistical background

Most of the screening designs are so-called Plackett-Burman designs. Exceptions occur, where Plackett and Burman did not suggest a design or where the Plackett-Burman designs coincide with the regular fractional factorial designs which are not so suitable for screening purposes. For more information, look at the help file for function pb from package FrF2.

Inputs on Tab Base Settings

name of design

must be a valid name. The design itself is created under this name in the R workspace.

number of runs

must be provided and must be a multiple of 4.

the check box

below the number of runs is relevant for 12 run designs only: if checked, the 12 run design is in the arrangement as provided by Taguchi, otherwise it is a Plackett-Burman design. (The two are equivalent to each other, but comparison is tedious.)

number of factors

must always be specified. It must be smaller than the number of runs. The function always creates a design with the number of factors one less than the number of runs. Those factors not used in the experiment are named e1, e2 etc. and serve for creating half-normal plots of the effects. The number of factors must match the number of entries on the Factor Details tab.

replications

is the number of times each experimental run is conducted. If larger than 1, each run is conducted several times. If the checkbox next to the number of replications is checked, it is assumed that the experiment involves repeated measurements for one setup of the experimental run; if it is not checked, the experimental run itself is replicated with everything relevant newly set up (much more valuable than repeated measurements, unless the key driver of variability is in the measuring step). If the check box is not checked, the experiment will be randomized separately for each round of replications (first all first runs, then all second runs etc.).

randomization settings

should normally not be changed; you can provide a seed if you want to exactly reproduce a randomized design created in the past. Unchecking the randomization box will produce a non-randomized experiment. This is usually NOT recommended.

Author(s)

Ulrike Groemping

References

~put references to the literature/web site here ~

See Also

See Also pb for the function that does the calculations and Menu.2level for overall help on the 2-level design menu.

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.