boxcox(x, formula, n.effects, sig=.05, method="pse", minlam=-1, maxlam=1, nlam=21)
fac.aov
.
formula
or
n.effects
(see below) may be specified.
"sig"
specifying number of effects to be fitted in the
reduced model.
If
n.effects
is an integer the
n.effects
largest terms
are used in the reduced model, with the rest used to estimate
variance. If
n.effects = "sig"
, significant terms are included in the
model, where significance is determined by the values of
sig
and
method
.
n.effects = "sig"
.
n.effects = "sig"
.
"pse"
uses a pseudo standard error estimate.
"tse"
uses the 60% trimmed standard error of the effects.
"ase"
uses the adaptive standard error estimate.
minlam
and
maxlam
.
"boxcox"
with the following
components:
lambda
.
nlam
rows and one column for the effects given by
n.effects
.
lambda
.
logliklam
.
"largest"
if
length(n.effects)=1
;
"model"
if
length(n.effects) > 1;
"sig"
if
n.effects="sig"
;
"formula"
if
formula
was used instead of
n.effects
.
See Box, Hunter and Hunter (1978) p. 239 or Haaland (1989) Chapter 6 for details.
If the value of
formula
or
n.effects
leads to a reduced model
with all terms of the full model produced from
fac.aov
,
there must either be replication in the design or the
model must not be saturated. Otherwise
boxcox
will not
be able to estimate the residual sums of squares for the
various transformations.
Box, G.E.P (1988) Signal-to-noise ratios, performance
criteria, and transformations. Technometrics, 30:1-17.
Box, G.E.P. and D.R. Cox (1964) An analysis of
transformations (with discussion). J. Royal Statist. Soc. Ser. B, 26:211-246
Box, G.E.P. and C.A. Fung (1983) Some considerations
in estimating data transformations. MRC Report #2609,
University of Wisconsin-Madison.
Box, G.E.P. and C.A. Fung (1986) Studies in quality
improvement: Minimizing transmitted variation by
parameter design. Report 8, University of
Wisconsin-Madison, Center for Quality and Productivity
Improvement (submitted to the Journal of Quality Technology).
Box, G. E. P., W. G. Hunter, and J. S. Hunter (1978)
Statistics for Experimenters,
New York: Wiley.
Haaland, P. D. (1989)
Experimental Design in Biotechnology,
New York: Marcel Dekker.
Weisberg, S. (1985)
Applied Linear Regression,
2nd edition, New York:Wiley.
buffer.fac <- fac.aov(buffer.df) buffer.bc <- boxcox(buffer.fac, ~pH*thimer + pH*gent) plot(buffer.fac) # produces both of the plots below lambda(buffer.bc) loglik(buffer.bc) buffer.bc2 <- boxcox(buffer.fac, n.effects=5) buffer.bc3 <- boxcox(buffer.fac, n.effects = "sig")