Transform-both-sides Box-Cox transformation
Finds the optimal Box-Cox transformation for non-linear regression.
## 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", ...)object |
object of class |
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 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.
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.
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 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)
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