Bootstrap estimates, along with standard errors and confidence intervals, of a nonlinear model, resulting from arc length fitting of the four-parameter kappa sigmoidal function.
kappa4alBoot(formula, data = list(), xin, lower, upper, q1, q2, tol, maxiter,
bootstraps, bootName, ...)# S3 method for default
kappa4alBoot(formula, data = list(), xin, lower = c(0, -5,
-5), upper = c(10, 1, 1), q1, q2, tol = 1e-15, maxiter = 50000,
bootstraps, bootName, ...)
# S3 method for kappa4alBoot
print(x, ...)
# S3 method for kappa4alBoot
summary(object, ...)
# S3 method for summary.kappa4alBoot
print(x, ...)
# S3 method for formula
kappa4alBoot(formula, data = list(), xin, lower, upper, q1,
q2, tol, maxiter, bootstraps, bootName, ...)
# S3 method for kappa4alBoot
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).
Numeric vectors, for the lower and upper bounds of the intervals over which arc lengths are to be computed.
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 kappa4alBoot object. May include a path.
Arguments to be passed on to the differential evolution function JDEoptim.
A kappa4alBoot object.
A kappa4alBoot object.
The data on which the estimated model is to be fitted.
A generic S3 object with class kappa4alBoot.
kappa4alBoot.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.kappa4alBoot: A list of class summary.kappa4alBoot with the following components:
call: Original call to the kappa4alBoot function.
coefficients: A matrix with estimates, estimated errors, and 95% parameter confidence intervals (based on the inverse empirical distribution function).
arclengths: A matrix of the arc length segments that were matched, for the dependent and independent variables.
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.kappa4alBoot: The object passed to the function is returned invisibly.
predict.kappa4alBoot: A vector of predicted values resulting from the estimated model.
default: default method for kappa4alBoot.
kappa4alBoot: print method for kappa4alBoot.
kappa4alBoot: summary method for kappa4alBoot.
summary.kappa4alBoot: print method for summary.kappa4alBoot.
formula: formula method for kappa4alBoot.
kappa4alBoot: predict method for kappa4alBoot.