projs.2canon {dae} | R Documentation |
A canonical analysis of the relationships between two sets of projectors
Description
Computes the canonical efficiency factors for the joint
decomposition of two structures or sets of mutually orthogonally
projectors (Brien and Bailey, 2009), orthogonalizing projectors in the
Q2 list
to those earlier in the list
of projectors with
which they are partially aliased. The results can be summarized in the
form of a skeleton ANOVA table.
Usage
projs.2canon(Q1, Q2)
Arguments
Q1 |
A |
Q2 |
A |
Details
Two loops, one nested within the other, are performed. The first cycles
over the components of Q1
and the nested loop cycles over the
components of Q2
. The joint decomposition of the two projectors
in each cycle, one from Q1
(say Q1[[i]]
) and the other
from Q2
(say Q2[[j]]
) is obtained using
proj2.combine
.
In particular, the nonzero canonical efficiency factors for the joint
decomposition of the two projectors is obtained. The nonzero canonical
efficiency factors are the nonzero eigenvalues of
Q1[[i]] %*% Q2[[j]] %*% Q1[[i]]
(James and Wilkinson, 1971).
An eigenvalue is regarded as zero if it is less than
daeTolerance
, which is initially set to
.Machine$double.eps ^ 0.5
(about 1.5E-08). The function
set.daeTolerance
can be used to change
daeTolerance
.
However, a warning occurs if any pair of Q2 projectors (say
Q2[[j]]
and Q2[[k]]
) do not have adjusted orthgonality
with respect to any Q1 projector (say Q1[[i]]
), because they are
partially aliased. That is, if Q2[[j]] %*% Q1[[i]] %*% Q2[[k]]
is nonzero for any pair of different Q2 projectors and any
Q1 projector. When it is nonzero, the projector for the later term in
the list of projectors is orthogonalized to the projector that is
earlier in the list. A list of such projectors is returned in the
aliasing
component of the p2canon.object
. The
entries in the aliasing
component gives the amount of information
that is aliased with previous terms.
Value
Author(s)
Chris Brien
References
Brien, C. J. and R. A. Bailey (2009). Decomposition tables for multitiered experiments. I. A chain of randomizations. The Annals of Statistics, 36, 4184 - 4213.
James, A. T. and Wilkinson, G. N. (1971) Factorization of the residual operator and canonical decomposition of nonorthogonal factors in the analysis of variance. Biometrika, 58, 279-294.
See Also
summary.p2canon
, efficiencies.p2canon
,
projs.combine.p2canon
, pstructure
,
proj2.efficiency
, proj2.combine
,
proj2.eigen
, efficiency.criteria
in package dae, eigen
.
projector
for further information about this class.
Examples
## PBIBD(2) from p. 379 of Cochran and Cox (1957) Experimental Designs.
## 2nd edn Wiley, New York
PBIBD2.unit <- list(Block = 6, Unit = 4)
PBIBD2.nest <- list(Unit = "Block")
trt <- factor(c(1,4,2,5, 2,5,3,6, 3,6,1,4, 4,1,5,2, 5,2,6,3, 6,3,4,1))
PBIBD2.lay <- designRandomize(allocated = trt,
recipient = PBIBD2.unit,
nested.recipients = PBIBD2.nest)
##obtain projectors using pstructure
unit.struct <- pstructure(~ Block/Unit, data = PBIBD2.lay)
trt.struct <- pstructure(~ trt, data = PBIBD2.lay)
##obtain combined decomposition and summarize
unit.trt.p2canon <- projs.2canon(unit.struct$Q, trt.struct$Q)
summary(unit.trt.p2canon)