Menu.Analyze: Analyze experimental designs
Description
This is a brief explanation on the analysis faciliteis for experimental designs
in the Analyze Design menu.Available analysis options
Generally applicable analysis options
The topmost menu entry Default linear model is of interest for all design types
and is usable whenever the active dataset is a design with response.
However, the default linear model analysis does not work
for long version designs with repeated measurements or for parameter designs in long format,
as it usually does not make sense in such situations. Rather, such designs should be brought into
wide format by using the
Design --> Combine or Modify Designs --> Change from long to wide format
menu.
Note that the default linear model analysis is a quick
first shot that should often be tuned and sometimes (e.g. in many cases splitplot designs) replaced
by a different analysis strategy for serious modelling. Tuning can be
done by using the built-in linear model functions from the R-Commander Analyze menu.
The R-Commander Models menu also offers interesting options for subsequent
model diagnostics and graphics.
Analysis options specific to 2-level designs
There are two types of orthogonal 2-level factorial designs,
regular fractional factorial designs and screening designs.
The latter has more interesting analysis options than the former.
Effects plots and main effects plots are of interest for both types of 2-level
designs alike, while interaction plots are usually of interest for the regular designs only.
Note that the interpretation of all effects plots, main effects and interaction plots
is useful only in connection with knowledge about the alias structure of a design.
For regular designs, this can e.g. be obtained from the Summarize design
menu item within the Inspect design sub menu of the Design menu.
For screening designs, if it can be assumed that interactions are likely to be
much less important than main effects, the main effect plots may be interpretable
without such thoughts. Considerations involving the interactions become quite complicated
with screening designs because of partial aliasing.
Advanced users might also want to try the Bayesian methods
offered in package BsMD-package, which are currently not implemented
in RcmdrPlugin.DoE.
Analysis options specific to designs with quantitative factors
have not been implemented for the GUI version yet. They are available within R-package
rsm for the classical response surface designs.References
Box G. E. P, Hunter, W. C. and Hunter, J. S. (2005)
Statistics for Experimenters, 2nd edition.
New York: Wiley.See Also
See also summary.design for
how designs are summarized, formula.design
for the function that creates the default linear model formula, or
DanielPlot, MEPlot and IAPlot
for the functions behind the graphical analysis tools for 2-level factors.