boxcox.nls: Transform-both-sides (TBS) method for nonlinear regression

In statforbiology: Data Analyses in Agriculture and Biology

Transform-both-sides (TBS) method for nonlinear regression

Description

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

Usage

  ## S3 method for class 'nls'
boxcox(object, lambda = seq(-2, 2, 1/10), plotit = TRUE, 
             start, eps = 1/50, bcAdd = 0, level = 0.95, 
             xlab = expression(lambda), ylab = "log-likelihood", ...)
  ## S3 method for class 'nlsbc'
summary(object, ...)

Arguments

object	object of class nls. For bcSummary the nls fit should have been obtained using boxcox.nls
lambda	numeric vector of lambda values; the default is (-2, 2) in steps of 0.1.
plotit	logical which controls whether the result should be plotted.
start	a list of starting values (optional).
eps	numeric value: the tolerance for lambda = 0; defaults to 0.02.
bcAdd	numeric value specifying the constant to be added on both sides prior to Box-Cox transformation. The default is 0.
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.

Details

boxcox.nls is very similar to the boxcox in its arguments. 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. If a self starter model was used in the model fit, then gradient information will be used in the profiling.

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%) about the optimal lambda value.

Author(s)

Christian Ritz, modified by Andrea Onofri

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
ryegrass.m1 <- nls(rootl ~ NLS.L4(conc, b, c, d, e), 
                 data=ryegrass)
summary(ryegrass.m1)

## Fitting the same model with optimal Box-Cox transformation
ryegrass.m2 <- boxcox(ryegrass.m1, plotit = TRUE)
summary(ryegrass.m2)

statforbiology documentation built on Sept. 9, 2025, 5:42 p.m.