Estimate log Transformation Parameter

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

logtrans(object, ...)

# S3 method for default
logtrans(object, ..., alpha = seq(0.5, 6, by = 0.25) - min(y),
         plotit = TRUE, interp =, xlab = "alpha",
         ylab = "log Likelihood")

# S3 method for formula
logtrans(object, data, ...)

# S3 method for lm
logtrans(object, ...)

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?
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`.
data	optional `data` argument for `lm` fit.

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, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

Examples

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