nlsBoot: Bootstrap resampling

In nlstools: Tools for nonlinear regression diagnostics

Description

Bootstrap resampling

Usage

nlsBoot (nls, niter = 999)
## S3 method for class 'nlsBoot'
 plot(x, type = c("pairs", "boxplot"), 
    mfr = c(ceiling(sqrt(ncol(x$coefboot))), 
    ceiling(sqrt(ncol(x$coefboot)))), 
    ask = FALSE, ...)
## S3 method for class 'nlsBoot'
 print(x, ...)
## S3 method for class 'nlsBoot'
 summary(object, ...)

Arguments

nls	an object of class 'nls'
niter	number of iterations
x, object	an object of class 'nlsBoot'
type	type of representation (options are "pairs" or "boxplot")
mfr	layout definition (number of rows and columns in the graphics device)
ask	if TRUE, draw plot interactively
...	further arguments passed to or from other methods

Details

Non-parametric bootstrapping is used. Mean centered residuals are bootstrapped. By default, 999 resampled data sets are created from which bootstrap estimates are obtained by fitting the model on each of these data sets. Whenever the fit fails to converge, a flag reports the number of non-convergences. If the fitting procedure fails to converge in more than 50% of the cases, the procedure is interrupted with a flag and no result is given. The function summary returns the bootstrap estimates and the 95 percent confidence intervals which are simply defined by the median and the 2.5 and 97.5 percentiles of the bootstrap sample of estimates. The bootstrap estimates distributions can be visualized using the function plot.nlsBoot either by plotting the bootstrap sample for each pair of parameters or by displaying the boxplot representation of the bootstrap sample for each parameter. Notice that nlsBoot does not currently handle transformed dependent variables specified in the left side of the nls formula.

Value

nlsBoot returns a list of three objects:

coefboot	contains the bootstrap parameter estimates
bootCI	contains the bootstrap medians and the bootstrap 95% confidence intervals
rse	is the vector of bootstrap residual errors

Author(s)

Florent Baty [e-mail]
Marie-Laure Delignette-Muller [e-mail]

References

Bates DM and Watts DG (1988) Nonlinear regression analysis and its applications. Wiley, Chichester, UK.

Huet S, Bouvier A, Poursat M-A, Jolivet E (2003) Statistical tools for nonlinear regression: a practical guide with S-PLUS and R examples. Springer, Berlin, Heidelberg, New York.

Examples

data(growthcurve4)
nls1 <- nls(gompertzm, growthcurve4, 
    list(lag = 10, mumax = 0.1, LOG10N0 = 6, LOG10Nmax = 9))
boo <- nlsBoot(nls1,niter=200)
plot(boo)
plot(boo, type = "boxplot", ask=FALSE)
summary(boo)
  

nlstools documentation built on May 2, 2019, 5:49 p.m.