# logtrans

##### 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 + …`

.

- Keywords
- models, hplot, regression

##### Usage

`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.

##### See Also

##### Examples

```
# NOT RUN {
logtrans(Days ~ Age*Sex*Eth*Lrn, data = quine,
alpha = seq(0.75, 6.5, len=20))
# }
```

*Documentation reproduced from package MASS, version 7.3-51.6, License: GPL-2 | GPL-3*