parametric.bootstrap.cov: Parametric bootstrap with covariance

In hadron: Analysis Framework for Monte Carlo Simulation Data in Physics

Parametric bootstrap with covariance

Description

Parametric bootstrap with covariance

Usage

parametric.bootstrap.cov(boot.R, x, cov, seed)

Arguments

boot.R	numeric. Number of bootstrap samples to generate.
x	numeric vector. Actual values for the data.
cov	numeric matrix, square, length of x or missing. Covariance between the various variables in the vector x.
seed	integer. Seed to use for the random number generation. If it is missing, the seed will not be set to any particular value. If there was a default value, all results would be exactly correlated. So if you want reproducability by fixing the seeds, make sure you choose different seeds for independent variables.

Value

A matrix with as many columns as there are variables in x and as many rows as boot.R.

See Also

Other NLS fit functions: bootstrap.nlsfit(), parametric.bootstrap(), parametric.nlsfit.cov(), parametric.nlsfit(), plot.bootstrapfit(), predict.bootstrapfit(), print.bootstrapfit(), simple.nlsfit(), summary.bootstrapfit()

Examples

x <- 1:3
cov <- matrix(c(0.1, 0, 0.01,
                0, 0.15, 0.02,
                0.01, 0.02, 0.2), nrow = 3)
parametric.bootstrap.cov(5, x, cov)

hadron documentation built on Sept. 9, 2022, 5:06 p.m.