# logtrans

0th

Percentile

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.

boxcox

##### Aliases
• logtrans
• logtrans.formula
• logtrans.lm
• logtrans.default
##### Examples
library(MASS) logtrans(Days ~ Age*Sex*Eth*Lrn, data = quine, alpha = seq(0.75, 6.5, len=20)) 
Documentation reproduced from package MASS, version 7.3-47, License: GPL-2 | GPL-3

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