boxcox.drc {drc} | R Documentation |
Finds the optimal Box-Cox transformation for non-linear regression.
boxcox.drc(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", ...)
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. |
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.
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.
Christian Ritz
Carroll, R. J. and Ruppert, D. (1988) Transformation and Weighting in Regression, New York: Chapman and Hall (Chapter 4).
For linear regression the analogue is boxcox
.
## 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)