R: Transform-both-sides Box-Cox transformation

boxcox.drc {drc} | R Documentation

## 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.

### Details

The optimal lambda value is determined using a profile likelihood approach: For each lambda value the dose-response regression model is fitted and the lambda value (and corresponding model fit) resulting in the largest value of the log likelihood function is chosen.

### Value

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

### Author(s)

Christian Ritz

### 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 <- drm(ryegrass, fct = LL.4())
    summary(ryegrass.m1)

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

[Package drc version 3.0-1 Index]