Menu.oaTab1 {RcmdrPlugin.DoE}R Documentation

Basic information for orthogonal main effects designs

Description

Basic information for orthogonal main effects design Menu

Brief statistical background

The orthogonal main effects designs are of different types. They all work well if there are indeed no interactions between factors. Some of them have complete aliasing between main effects and two-factor interactions at least for some factors. It is therefore advisable to check the design before actually conducting the experiment with respect to its potential analysis options and biases.

Also note that it is usually preferable to create an experiment with solely 2-level factors from the special menu for 2-level situations. If there is just one factor at more than 2 levels, it may also be useful to simply cross this factor with an otherwise 2-level design. Also, 4-level factors can be accomodated in 2-level designs by allocating them to two 2-level factors together with their interaction (i.e. three columns of the model matrix). Both possibilities will be automatically available in the future; for the time being, they require manual work by the user.

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 factors
must always be specified. The number of factors must match the number of entries on the Factor Details tab.
specific array
can be selected from the drop-down list; this implies that this particular array is used for generating the design; the array name indicates its number of runs and the maximum possible numbers of factors with various numbers of levels. For example, the array L12.2.2.6.1 can accomodate 2 factors at 2 levels each and one factor at 6 levels
minimum number of runs
can be selected from dropdown, but is not needed
minimum residual df
is per default 0 and can be set to any positive integer number; it specifies the minium number of extra runs over and above what would be needed for a model with main effects for all factors; for example, when using the design L12.2.2.6.1 for two 2-level factors and one 6-level factor, the model with all main effects requires 1+2*(2-1)+1*(6-1)=8 degrees of freedom, i.e. there are four extra degrees of freedom for pure error or lack of fit
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 oa.design for the function that does the calculations and Menu.General for overall help on the general factorial design menu.


[Package RcmdrPlugin.DoE version 0.6-10 Index]