Design of experiments
GenStat has a comprehensive set of facilities for design of experiments. Collectively, these are
known as the GenStat Design System. Many different design types are covered, each with a
procedure that allows you to view and choose from the available possibilities. Other procedure
allow designs and data forms to be displayed. There is also a general procedure
DESIGN that can be used interactively to provide a single point of access to all the
design types. DESIGN and the AG... procedures that it calls provide the Select Design
facilities in GenStat for Windows, while the alternative Standard Design menu uses
AGHIERARCHICAL, AGLATIN and AGSQLATTICE to generate
completely randomized designs, randomized blocks, Latin and Graeco-Latin squares, split-plots,
strip-plots (or criss-cross designs) and lattices.
provides a menu-driven interface for selecting and generating
experimental designs
forms alpha designs for up to 100 treatments
generates balanced-incomplete-block designs
generates Box-Behnken designs
generates central composite designs
generates Latin squares balanced for carry-over
effects
generates cyclic designs from standard generators
generates generally balanced designs - factorial designs with
blocking, fractional factorial designs, Lattice squares etc.
generates minimum aberration complete and fractional
factorial designs
generates fractional factorial designs
generates orthogonal hierarchical designs
generates mutually orthogonal Latin squares
generates loop designs e.g. for time-course microarray
experiments
generates designs to estimate main effects of two-level
factors
generates neighbour-balanced designs
generates complete and quasi-complete Latin squares
generates reference-level designs e.g. for microarray
experiments
generates semi-Latin squares
generates square lattice designs
produces experimental designs efficient under analysis of
covariance
prints treatment combinations tabulated by the block factors
plots the plan of a design
prints data forms for a design
There are also procedures that you can use to determine the sample size (i.e. replication)
required for experiments that are to be analysed by analysis of variance, t-test or various non-parametric tests. You can also calculate the power (or probability of detection) for terms in
analysis of variance or regression analyses.
calculates the power (probability of detection) for terms in an
analysis of variance
finds the replication (sample size) to detect a treatment
effect or contrast
calculates the power (probability of detection) for regression
models
calculates the minimum size of effect or contrast detectable
in an analysis of variance
calculates the sample size for binomial tests
calculates the sample size to detect specified
correlations
calculates the sample size for Lin's concordance
coefficient
calculates the sample size for the Mann-Whitney test
calculates the sample size for McNemar's test
calculates the sample size to obtain a specified precision
calculates the sample size for a sign test
calculates the sample size for t-tests, including equivalence tests
and tests for non-inferiority
The Design System is based on a range of standard generators. Some of these, such as the
Galois fields used to generate Latin squares, can be formed when required - and so there is no
limitation on the available designs. Repertoires of others, such as design keys, are stored in
backing-store files which are scanned by the design generation procedures to form menus listing
the available possibilities. Algorithms are available to form generators for new designs, and these
can then be added to the design files to become an integral part of the system. Other design
utilities include procedures for combining simple designs into more complicated arrangements,
and for determining how many replicates are needed. There is also a directive for constructing
response-surface designs. The relevant commands include the directives
uses the BLKL algorithm to construct designs for
estimating response surfaces
generates values of factors in systematic order or as defined
by a design key, or forms values of pseudo-factors
puts units of vectors into random order, or randomizes units
of an experimental design
forms design keys for multi-stratum experimental designs, allowing
for confounding and aliasing of treatments
determines patterns of confounding and aliasing from
design keys, and extends the treatment formula to incorporate the necessary pseudo-factors
forms a model formula using structures supplied in a
pointer
and the procedures
forms a variate of unit labels for a design
forms a factor to index the units of the final stratum of a
design
generates values for treatment factors using the design key
method
merges extra units into an experimental design
forms a new experimental design from the product of two
designs
randomizes and prints an experimental design
produces experimental designs efficient under analysis of
covariance
represents a factor by factorial combinations of a set of
factors
forms a factor with a level for every combination of other
factors
forms the basic contrasts of a model term
forms the complement of an incomplete block design
forms a backing-store file of information for
AGDESIGN
forms Hadamard matrices
forms a "concurrence" matrix recording how often each
pair of treatments occurs in the same block of a design
forms a projection matrix for a set of model terms
calculates the efficiency for estimating effects in cross-over
designs
estimates the power of contrasts in cross-over designs