% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/guerrero.R
\name{BoxCox.lambda}
\alias{BoxCox.lambda}
\title{Automatic selection of Box Cox transformation parameter}
\usage{
BoxCox.lambda(x, method = c("guerrero", "loglik"), lower = -1, upper = 2)
}
\arguments{
\item{x}{a numeric vector or time series of class \code{ts}}

\item{method}{Choose method to be used in calculating lambda.}

\item{lower}{Lower limit for possible lambda values.}

\item{upper}{Upper limit for possible lambda values.}
}
\value{
a number indicating the Box-Cox transformation parameter.
}
\description{
If \code{method=="guerrero"}, Guerrero's (1993) method is used, where lambda
minimizes the coefficient of variation for subseries of \code{x}.
}
\details{
If \code{method=="loglik"}, the value of lambda is chosen to maximize the
profile log likelihood of a linear model fitted to \code{x}. For
non-seasonal data, a linear time trend is fitted while for seasonal data, a
linear time trend with seasonal dummy variables is used.
}
\examples{

lambda <- BoxCox.lambda(AirPassengers,lower=0)
air.fit <- Arima(AirPassengers, order=c(0,1,1),
                 seasonal=list(order=c(0,1,1),period=12), lambda=lambda)
plot(forecast(air.fit))

}
\references{
Box, G. E. P. and Cox, D. R. (1964) An analysis of
transformations. \emph{JRSS B} \bold{26} 211--246.

Guerrero, V.M. (1993) Time-series analysis supported by power
transformations. \emph{Journal of Forecasting}, \bold{12}, 37--48.
}
\seealso{
\code{\link{BoxCox}}
}
\author{
Leanne Chhay and Rob J Hyndman
}
\keyword{ts}
