Estimate log Transformation Parameter
Usage
logtrans(object, ..., alpha = seq(0.5, 6, by = 0.25) - min(y),
plotit = <<see below>>, interp = <<see below>>,
xlab="alpha", ylab="log Likelihood")
Arguments
object
|
Fitted linear model object, or formula defining the untransformed
model that is y ~ x1 + x2 + ...{} . The function is generic.
|
...
|
If object is a formula, this argument may specify a data frame
as for lm .
|
alpha
|
Set of values for the transformation parameter, alpha.
|
plotit
|
Should plotting be done? (Default is TRUE if a non-null device is
currently active, else FALSE .)
|
interp
|
Should the marginal log-likelihood be interpolated with a spline
approximation? (Default is TRUE if plotting is to be done and
the number of real points is less than 100.)
|
xlab
|
as for plot .
|
ylab
|
as for plot .
|
Description
Find and optionally plot the marginal likelihood for alpha
for a transformation model of the form log(y + alpha) ~ x1 + x2 + ...{}
.Value
List with components x
(for alpha) and y
(for the marginal
log-likelihood values).Side Effects
A plot of the marginal log-likelihood is produced, if requested,
together with an approximate mle and 95% confidence interval.References
Venables & Ripley, Chapter 6.See Also
boxcox
Examples
data(quine)
logtrans(Days ~ Age*Sex*Eth*Lrn, data = quine,
alpha = seq(0.75, 6.5, len=20), singular.ok = TRUE)