Description Usage Arguments Value Methods (by class) Examples
A framework for 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 | kappa4nls(formula, data = list(), lower, upper, tol, maxiter, ...)
## Default S3 method:
kappa4nls(formula, data = list(), lower = c(0, -5, -5),
upper = c(10, 1, 1), tol = 1e-15, maxiter = 50000, ...)
## S3 method for class 'kappa4nls'
print(x, ...)
## S3 method for class 'kappa4nls'
summary(object, ...)
## S3 method for class 'summary.kappa4nls'
print(x, ...)
## S3 method for class 'formula'
kappa4nls(formula, data = list(), lower = c(0, -5, -5),
upper = c(10, 1, 1), tol = 1e-15, maxiter = 50000, ...)
## S3 method for class 'kappa4nls'
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 |
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. |
... |
Arguments to be passed on to the differential evolution function |
x |
A kappa4nls object. |
object |
A kappa4nls object. |
newdata |
The data on which the estimated model is to be fitted. |
A generic S3 object with class kappa4nls.
kappa4nls.default: A list with all components from JDEoptim
, as well as:
intercept: Did the model contain an intercept TRUE/FALSE?
coefficients: A vector of estimated coefficients.
error: The value of the objective function.
fitted.values: A vector of estimated values.
residuals: The residuals resulting from the fitted model.
call: The call to the function.
summary.kappa4nls: A list of class summary.kappa4nls with the following components:
call: Original call to kappa4nls
function.
coefficients: A vector with parameter estimates.
r.squared: The r^{2} coefficient.
sigma: The residual standard error.
error: Value of the objective function.
residSum: Summary statistics for the distribution of the residuals.
print.summary.kappa4nls: The object passed to the function is returned invisibly.
predict.kappa4nls: A vector of predicted values resulting from the estimated model.
default
: default method for kappa4nls.
kappa4nls
: print method for kappa4nls.
kappa4nls
: summary method for kappa4nls.
summary.kappa4nls
: print method for summary.kappa4nls.
formula
: formula method for kappa4nls.
kappa4nls
: predict method for kappa4nls.
1 2 3 4 5 6 7 8 9 | k <- kappa4tc(-4, 0, 1)$par
x <- seq(qkappa4(0, 4, 0.4, -4, k), qkappa4(0.7, 4, 0.4, -4, k), length.out=100)
y <- sapply(x, function(i) pkappa4(i, 4, 0.4, -4, k))
kappa4nls.default(y~x, tol=1e-5)
u <- seq(qkappa4(0.1, 4, 0.4, -4, k), qkappa4(0.8, 4, 0.4, -4, k), length.out=100)
v <- sapply(u, function(i) pkappa4(i, 4, 0.4, -4, k))
nls <- kappa4nls(y~x, tol=1e-5)
predict(nls, newdata=data.frame(y=v, x=u))
|
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