Description Usage Arguments Details 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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | 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 |
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 |
x |
A kappa4nlsBoot object. |
object |
A kappa4nlsBoot object. |
newdata |
The data on which the estimated model is to be fitted. |
On systems where the pbMPI package is available, this code will run in parallel.
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
, or that from system.time
.
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
, or that from system.time
.
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.
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