NLS fit with parametric bootstrap and covariance
parametric.nlsfit.cov(fn, par.guess, boot.R, y, x, cov, lower = rep(x = -Inf,
times = length(par.guess)), upper = rep(x = +Inf, times =
length(par.guess)), ..., bootstrap = TRUE, na.rm = FALSE)fn(par, x, ...). The (non-linear) function to be fitted to the
data. Its first argument must be the fit parameters named par. The
second must be x, the explaining variable. Additional parameters
might be passed to the function. Currently we pass boot.r which is 0
for the original data and the ID (1, ...) of the bootstrap sample otherwise.
As more parameters might be added in the future it is recommended that the
fit function accepts ... as the last parameter to be forward compatible.
initial guess values for the fit parameters.
numeric. Number of bootstrap samples to generate.
the data as a one-dimensional numerical vector to be described by the fit function.
values of the explaining variable in form of a one-dimensional numerical vector.
numeric matrix, square, length of x or missing. Covariance
between the various variables in the vector x.
Numeric vector of length length(par.guess)
of lower bounds on the fit parameters. If missing, -Inf
will be set for all.
Numeric vector of length length(par.guess)
of upper bounds on the fit parameters. If missing, +Inf
will be set for all.
Additional parameters passed to fn, gr and dfn.
boolean. If TRUE, bootstrap is used.
logical. If set to true, NAs in y and dy will be ignored.
If x-errors are taken into account, NAs in x and dx will be ignored, too.
See simple.nlsfit.
Other NLS fit functions:
bootstrap.nlsfit(),
parametric.bootstrap.cov(),
parametric.bootstrap(),
parametric.nlsfit(),
plot.bootstrapfit(),
predict.bootstrapfit(),
print.bootstrapfit(),
simple.nlsfit(),
summary.bootstrapfit()