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]