nlsJack: Jackknife resampling

In nlstools: Tools for nonlinear regression diagnostics

Description

Jackknife resampling

Usage

nlsJack (nls)
## S3 method for class 'nlsJack'
 plot(x, mfr = c(nrow(x$reldif),1), ask = FALSE, ...)
## S3 method for class 'nlsJack'
 print(x, ...)
## S3 method for class 'nlsJack'
 summary(object, ...)

Arguments

nls	an object of class 'nls'
x, object	an object of class 'nlsJack'
mfr	layout definition, default is k rows (k: number of parameters) and 1 column
ask	if TRUE, draw plot interactively
...	further arguments passed to or from other methods

Details

A jackknife resampling procedure is performed. Each observation is sequentially removed from the initial data set using a leave-one-out strategy. A data set with n observations provides thus n resampled data sets of n-1 observations. The jackknife estimates with confidence intervals are calculated as described by Seber and Wild (1989) from the results of n new fits of the model on the n jackknife resampled data sets. The leave-one-out procedure is also employed to assess the influence of each observation on each parameter estimate. An observation is empirically defined as influential for one parameter if the difference between the estimate of this parameter with and without the observation exceeds twice the standard error of the estimate divided by sqrt(n). This empirical method assumes a small curvature of the nonlinear model. For each parameter, the absolute relative difference (in percent of the estimate) of the estimates with and without each observation is plotted. An asterisk is plotted for each influential observation.

Value

nlsJack returns a list with 7 objects:

estijack	a vector with jackknife estimates
coefjack	a dataframe with the parameter estimates for each jackknife sample
reldif	a dataframe with the absolute relative difference (in percent of the estimate) of the estimates with and without each observation
dfb	a dataframe with dfbetas for each parameter and each observation
jackCI	a dataframe with jackknife confidence intervals
rse	a vector with residual standard error for each jackknife sample
rss	residual a vector with residual sum of squares for each jackknife sample

Author(s)

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

References

Seber GAF, Wild CJ (1989) Nonlinear regression. Wiley, New York.

Examples

data(growthcurve4)
nls1 <- nls(gompertzm, growthcurve4,
	list(lag = 10, mumax = 0.1, LOG10N0 = 6, LOG10Nmax = 9))
lj <- nlsJack(nls1)
plot(lj)
summary(lj)

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