# logtrans: Estimate log Transformation Parameter In MASS: Support Functions and Datasets for Venables and Ripley's MASS

## Description

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

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```logtrans(object, ...) ## Default S3 method: logtrans(object, ..., alpha = seq(0.5, 6, by = 0.25) - min(y), plotit = TRUE, interp =, xlab = "alpha", ylab = "log Likelihood") ## S3 method for class 'formula' logtrans(object, data, ...) ## S3 method for class '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.

`boxcox`
 ```1 2``` ```logtrans(Days ~ Age*Sex*Eth*Lrn, data = quine, alpha = seq(0.75, 6.5, len=20)) ```