View source: R/bootstrap.nlsfit.R
bootstrap.nlsfit  R Documentation 
Performs and bootstraps a nonlinear leastsquares fit to data with y and x errors.
bootstrap.nlsfit(fn, par.guess, y, x, bsamples, 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, gr, dfn, mask, use.minpack.lm = TRUE, parallel = FALSE, error = sd, cov_fn = cov, 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. 
bsamples 
bootstrap samples of 
priors 
List possessing the elements 
... 
Additional parameters passed to 
lower 
Numeric vector of length 
upper 
Numeric vector of length 
dy, 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

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 
parallel 
parallelise over bootstrap samples. The package

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. 
cov_fn 
function. Function to compute the covariance (matrix). Default is cov. 
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 a list of class 'bootstrapfit'. It returns all input parameters and adds in addition the following:
t0 
the one dimensional numerical vector of length

t 
an array of dimensions 
bsamples 
the bootstrap samples used as an array of dimensions

Qval 
the pvalue of the fit on the original data 
chisqr 
the residual chisqr value. 
dof 
the residual degrees of freedom of the fit. 
nx 
the number of xvalues. 
tofn 
the original 
mask 
original 
Other NLS fit functions:
parametric.bootstrap.cov()
,
parametric.bootstrap()
,
parametric.nlsfit.cov()
,
parametric.nlsfit()
,
plot.bootstrapfit()
,
predict.bootstrapfit()
,
print.bootstrapfit()
,
simple.nlsfit()
,
summary.bootstrapfit()
## Declare some data. value < c(0.1, 0.2, 0.31) dvalue < c(0.01, 0.01, 0.015) x < c(1, 2, 3) dx < c(0.1, 0.1, 0.1) boot.R < 1500 fn < function (par, x, boot.r, ...) par[1] + par[2] * x ## Before we can use the fit with this data, we need to create bootstrap ## samples. We do not want to use the correlation matrix here. Note that you ## can simply use the parametric.nlsfit function as a convenient wrapper of ## the two steps. bsamples < parametric.bootstrap(boot.R, c(value, x), c(dvalue, dx)) head(bsamples) fit.result < bootstrap.nlsfit(fn, c(1, 1), value, x, bsamples) summary(fit.result) plot(fit.result, main = 'Ribbon on top') plot(fit.result, ribbon.on.top = FALSE, main = 'Ribbon below') residual_plot(fit.result, main = 'Residual Plot')
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