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, ...)# S3 method for default
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 kappa4nlsBoot
print(x, ...)
# S3 method for kappa4nlsBoot
summary(object, ...)
# S3 method for summary.kappa4nlsBoot
print(x, ...)
# S3 method for formula
kappa4nlsBoot(formula, data = list(), xin, lower, upper,
tol, maxiter, bootstraps, bootName, ...)
# S3 method for kappa4nlsBoot
predict(object, newdata = NULL, ...)
An LHS ~ RHS formula, specifying the linear model to be estimated.
A data.frame which contains the variables in formula.
Numeric vector of length 3 containing initial values, for \(\sigma\), \(h\), and \(k\).
A vector of lower constraints for the parameters to be estimated; defaults to c(0, -5, -5).
A vector of upper constraints for the parameters to be estimated; defaults to c(10, 1, 1).
Error tolerance level; defaults to 1e-15.
The maximum number of iterations allowed; defaults to 50000.
An integer giving the number of bootstrap samples.
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.
A kappa4nlsBoot object.
A kappa4nlsBoot object.
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.
print.summary.kappa4nlsBoot: The object passed to the function is returned invisibly.
predict.kappa4nlsBoot: A vector of predicted values resulting from the estimated model.
default: default method for kappa4nlsBoot.
kappa4nlsBoot: print method for kappa4nlsBoot.
kappa4nlsBoot: summary method for kappa4nlsBoot.
summary.kappa4nlsBoot: print method for summary.kappa4nlsBoot.
formula: formula method for kappa4nlsBoot.
kappa4nlsBoot: predict method for kappa4nlsBoot.