kappa4nlsBoot: Sigmoidal curve fitting.
In alR: Arc Lengths of Statistical Functions

Description Usage Arguments Value Methods (by class)

Bootstrap estimates, along with standard errors and confidence intervals, of a nonlinear model, resulting from nonlinear least squares fitting of the four-parameter kappa sigmoidal function.

kappa4nlsBoot(formula, data = list(), xin, lower, upper, tol, maxiter,
  bootstraps, bootName, ...)

## Default S3 method:
kappa4nlsBoot(formula, data = list(), xin, lower = c(0,
  -5, -5), upper = c(10, 1, 1), tol = 1e-15, maxiter = 50000, bootstraps,
  bootName, ...)

## S3 method for class 'kappa4nlsBoot'
print(x, ...)

## S3 method for class 'kappa4nlsBoot'
summary(object, ...)

## S3 method for class 'summary.kappa4nlsBoot'
print(x, ...)

## S3 method for class 'formula'
kappa4nlsBoot(formula, data = list(), xin, lower, upper,
  tol, maxiter, bootstraps, bootName, ...)

## S3 method for class 'kappa4nlsBoot'
predict(object, newdata = NULL, ...)

`formula`	An LHS ~ RHS formula, specifying the linear model to be estimated.
`data`	A data.frame which contains the variables in `formula`.
`xin`	Numeric vector of length 3 containing initial values, for σ, h, and k.
`lower`	A vector of lower constraints for the parameters to be estimated; defaults to c(0, -5, -5).
`upper`	A vector of upper constraints for the parameters to be estimated; defaults to c(10, 1, 1).
`tol`	Error tolerance level; defaults to 1e-15.
`maxiter`	The maximum number of iterations allowed; defaults to 50000.
`bootstraps`	An integer giving the number of bootstrap samples.
`bootName`	The name of the .rds file to store the kappa4nlsBoot object. May include a path.
`...`	Arguments to be passed on to the differential evolution function `JDEoptim`.
`x`	A kappa4nlsBoot object.
`object`	A kappa4nlsBoot object.
`newdata`	The data on which the estimated model is to be fitted.

A generic S3 object with class kappa4nlsBoot.

kappa4nlsBoot.default: A list object (saved using saveRDS in the specified location) with the following components:

intercept: Did the model contain an intercept TRUE/FALSE?
coefficients: A vector of estimated coefficients.
bcoefficients: A vector of bootstrap coefficients, resulting from bootstrap estimation.
se: The standard errors for the estimates resulting from bootstrap estimation.
error: The value of the objective function.
errorList: A vector of values of the objective function for each bootstrap sample.
fitted.values: A vector of estimated values.
residuals: The residuals resulting from the fitted model.
call: The call to the function.
time: Min, mean and max time incurred by the computation, as obtained from comm.timer.

summary.kappa4nlsBoot: A list of class summary.kappa4nlsBoot with the following components:

call: Original call to the kappa4nlsBoot function.
coefficients: A matrix with estimates, estimated errors, and 95% parameter confidence intervals (based on the inverse empirical distribution function).
r.squared: The r^{2} coefficient.
sigma: The residual standard error.
error: Value of the objective function.
time: Min, mean and max time incurred by the computation, as obtained from comm.timer.
residSum: Summary statistics for the distribution of the residuals.
errorSum: Summary statistics for the distribution of the value of the objective function.