Bias corrected jackknife 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.
kappa4nlsJack(formula, data = list(), xin, lower, upper, tol, maxiter,
jackName, ...)# S3 method for default
kappa4nlsJack(formula, data = list(), xin, lower = c(0,
-5, -5), upper = c(10, 1, 1), tol = 1e-15, maxiter = 50000, jackName,
...)
# S3 method for kappa4nlsJack
print(x, ...)
# S3 method for kappa4nlsJack
summary(object, ...)
# S3 method for summary.kappa4nlsJack
print(x, ...)
# S3 method for formula
kappa4nlsJack(formula, data = list(), xin, lower, upper,
tol, maxiter, jackName, ...)
# S3 method for kappa4nlsJack
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.
The name of the .rds file to store the kappa4nlsJack object. May include a path.
Arguments to be passed on to the differential evolution function JDEoptim.
A kappa4nlsJack object.
A kappa4nlsJack object.
The data on which the estimated model is to be fitted.
A generic S3 object with class kappa4nlsJack.
kappa4nlsJack.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.
coefDist The jackknife parameter distribution.
jcoefficients: A vector of bias-corrected coefficients, resulting from jackknife estimation.
bias: The corrections applied in obtaining the bias-corrected estimates.
se: The standard errors for the estimates resulting from jackknife estimation.
error: The value of the objective function.
errorList: A vector of values of the objective function at jackknife points.
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.kappa4nlsJack: A list of class summary.kappa4nlsJack with the following components:
call: Original call to the kappa4nlsJack 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.kappa4nlsJack: The object passed to the function is returned invisibly.
predict.kappa4nlsJack: A vector of predicted values resulting from the estimated model.
default: default method for kappa4nlsJack.
kappa4nlsJack: print method for kappa4nlsJack.
kappa4nlsJack: summary method for kappa4nlsJack.
summary.kappa4nlsJack: print method for summary.kappa4nlsJack.
formula: formula method for kappa4nlsJack.
kappa4nlsJack: predict method for kappa4nlsJack.