drc (version 2.5-12)

boxcox.drc: Transform-both-sides Box-Cox transformation

Description

Finds the optimal Box-Cox transformation for non-linear regression.

Usage

## S3 method for class 'drc':
boxcox(object, lambda = seq(-2, 2, by = 0.25), plotit = TRUE, bcAdd = 0, 
method = c("ml", "anova"),  level = 0.95, eps = 1/50, 
xlab = expression(lambda), ylab = "log-Likelihood", ...)

Arguments

object

object of class drc.

lambda

numeric vector of lambda values; the default is (-2, 2) in steps of 0.25.

plotit

logical which controls whether the result should be plotted.

bcAdd

numeric value specifying the constant to be added on both sides prior to Box-Cox transformation. The default is 0.

method

character string specifying the estimation method for lambda: maximum likelihood or ANOVA-based (optimal lambda inherited from more general ANOVA model fit.

eps

numeric value: the tolerance for lambda = 0; defaults to 0.02.

level

numeric value: the confidence level required.

xlab

character string: the label on the x axis, defaults to "lambda".

ylab

character string: the label on the y axis, defaults to "log-likelihood".

...

additional graphical parameters.

Value

• An object of class nls (returned invisibly). If plotit = TRUE a plot of loglik vs lambda is shown indicating a confidence interval (by default 95 the optimal lambda value.

Details

The optimal lambda value is determined using a profile likelihood approach: For each lambda value the non-linear regression model is fitted and the lambda value resulting in thre largest value of the log likelihood function is picked.

References

Carroll, R. J. and Ruppert, D. (1988) Transformation and Weighting in Regression, New York: Chapman and Hall (Chapter 4).

See Also

For linear regression the analogue is boxcox.

Examples

## Fitting log-logistic model without transformation
m1 <- drm(ryegrass, fct = LL.4())
summary(m1)

## Fitting the same model with optimal Box-Cox transformation
m2 <- boxcox(m1)
summary(m2)