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, …)
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
.
Set of values for the transformation parameter, alpha.
Should plotting be done?
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.)
as for plot
.
as for plot
.
optional data
argument for lm
fit.
List with components x
(for alpha) and y
(for the marginal
log-likelihood values).
A plot of the marginal log-likelihood is produced, if requested, together with an approximate mle and 95% confidence interval.
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
# NOT RUN {
logtrans(Days ~ Age*Sex*Eth*Lrn, data = quine,
alpha = seq(0.75, 6.5, len=20))
# }
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