logtrans {MASS} | R Documentation

## 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")

### 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`.

### 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)