Description Usage Arguments Value See Also Examples
View source: R/bootstrap.nlsfit.R
NLS fit with without bootstrap
1 2 3 4 5 6  simple.nlsfit(fn, par.guess, y, x, errormodel, priors = list(param = c(), p =
c(), psamples = c()), ..., lower = rep(x = Inf, times =
length(par.guess)), upper = rep(x = +Inf, times = length(par.guess)), dy,
dx, CovMatrix, boot.R = 0, gr, dfn, mask, use.minpack.lm = TRUE,
error = sd, maxiter = 500, success.infos = 1:3,
relative.weights = FALSE, na.rm = FALSE)

fn 

par.guess 
initial guess values for the fit parameters. 
y 
the data as a onedimensional numerical vector to be described by the fit function. 
x 
values of the explaining variable in form of a onedimensional numerical vector. 
errormodel 
Either "yerror" or "xyerror", depending on the xvalues having errors or not. 
priors 
List possessing the elements 
... 
Additional parameters passed to 
lower 
Numeric vector of length 
upper 
Numeric vector of length 
dy 
Numeric vector. Errors of the dependent and independent variable, respectively. These do not need to be specified as they can be computed from the bootstrap samples. In the case of parametric bootstrap it might would lead to a loss of information if they were computed from the pseudobootstrap samples. They must not be specified if a covariance matrix is given. 
dx 
Numeric vector. Errors of the dependent and independent variable, respectively. These do not need to be specified as they can be computed from the bootstrap samples. In the case of parametric bootstrap it might would lead to a loss of information if they were computed from the pseudobootstrap samples. They must not be specified if a covariance matrix is given. 
CovMatrix 
complete variancecovariance matrix of dimensions

boot.R 
If larger than 0, 
gr 

dfn 

mask 
logical or integer index vector. The mask is applied to select the observations from the data that are to be used in the fit. It is applied to 
use.minpack.lm 
use the 
error 
Function that takes a sample vector and returns the error estimate. This is a parameter in order to support different resampling methods like jackknife. 
maxiter 
integer. Maximum number of iterations that can be used in the optimization process. 
success.infos 
integer vector. When using 
relative.weights 
are the errors on y (and x) to be interpreted as relative weights instead of absolute ones? If TRUE, the covariance martix of the fit parameter results is multiplied by chi^2/dof. This is the default in many fit programs, e.g. gnuplot. 
na.rm 
logical. If set to 
Returns an object of class bootstrapfit
, see bootstrap.nlsfit.
Other NLS fit functions:
bootstrap.nlsfit()
,
parametric.bootstrap.cov()
,
parametric.bootstrap()
,
parametric.nlsfit.cov()
,
parametric.nlsfit()
,
plot.bootstrapfit()
,
predict.bootstrapfit()
,
print.bootstrapfit()
,
summary.bootstrapfit()
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