The nlrwr Package - NexTag Supports Open Source Initiatives

8 boxcox.nlsboxcox.nlsTransform-both-sides Box-Cox transformationDescriptionFinds the optimal Box-Cox transformation for non-linear regression models.Usageboxcox.nls(object, lambda = seq(-2, 2, 1/10), plotit = TRUE, start, eps = 1/50, bxlab = expression(lambda), ylab = "log-likelihood", ...)bcSummary(object)Argumentsobjectobject of class nls. For bcSummary the nls fit should have been obtainedusing boxcox.nlslambda numeric vector of lambda values; the default is (-2, 2) in steps of 0.1.plotitstartlogical which controls whether the result should be plotted.a list of starting values (optional).eps numeric value: the tolerance for lambda = 0; defaults to 0.02.bcAddlevelxlabylabnumeric value specifying the constant to be added on both sides prior to Box-Cox transformation. The default is 0.numeric value: the confidence level required.character string: the label on the x axis, defaults to "lambda".character string: the label on the y axis, defaults to "log-likelihood".... additional graphical parameters.Detailsboxcox.nls is very similar to the boxcox in its arguments.The optimal lambda value is determined using a profile likelihood approach: For each lambda valuethe non-linear regression model is fitted and the lambda value resulting in thre largest value of thelog likelihood function is picked.If a self starter model was used in the model fit, then gradient information will be used in theprofiling.ValueAn object of class nls (returned invisibly). If plotit = TRUE a plot of loglik vs lambda is shownindicating a confidence interval (by default 95 the optimal lambda value.