Estimate log Transformation Parameter

DESCRIPTION:

Find and optionally plot the marginal likelihood for alpha for a transformation model of the form log(y + alpha) ~ x1 + x2 + ....

USAGE:

logtrans(object, ..., alpha = seq(0.5, 6, by = 0.25) - min(y),
         plotit = <<see below>>, interp = <<see below>>,
         xlab = "alpha", ylab = "log Likelihood")

REQUIRED ARGUMENTS:

object
Fitted linear model object, or formula defining the untransformed model that is y ~ x1 + x2 + .... The function is generic.

OPTIONAL ARGUMENTS:

...
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 T if a non-null device is currently active, else F.)
interp
Should the marginal log-likelihood be interpolated with a spline approximation? (Default is T if plotting is to be done and the number of real points is less than 100.)
xlab
as for plot.
ylab
as for plot.

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.

REFERENCE:

Venables & Ripley, Chapter 6.

SEE ALSO:

EXAMPLES:

logtrans(Days ~ Age*Sex*Eth*Lrn, data = quine,
         alpha = seq(0.75, 6.5, len=20), singular.ok = T)