boxcox.nls: Transform-both-sides Box-Cox transformation

In OnofriAndreaPG/aomisc: Statistical methods for the agricultural sciences

Transform-both-sides Box-Cox transformation

Description

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

Usage

  boxcox.nls(object, lambda = seq(-2, 2, 1/10), plotit = TRUE, start, eps = 1/50, bcAdd = 0, level = 0.95, 
  xlab = expression(lambda), ylab = "log-likelihood", ...)

  bcSummary(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~c+(d-c)/(1+(conc/e)^b),data=ryegrass, start=list(b=1,c=0,d=8,e=3))
summary(ryegrass.m1)

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

## Fitting the Michaelis-Menten model without self starter
L.minor.m1 <- nls(rate ~ Vm*conc/(K+conc), data = L.minor, start = list(K=20, Vm=120))
L.minor.m2 <- boxcox(L.minor.m1)
bcSummary(L.minor.m2)

## Fitting the Michaelis-Menten model with self starter
L.minor.m3 <- nls(rate ~ SSmicmen(conc, Vm, K), data = L.minor)
L.minor.m4 <- boxcox(L.minor.m3)
bcSummary(L.minor.m4)

OnofriAndreaPG/aomisc documentation built on July 4, 2023, 6:53 p.m.