identifyNormPars

qLow

qUpp

The confidence level; i.e. the desired coverage is 1-alpha. Defaults to 0.05.

alpha

A vector of length 2 giving the initial parameter values to start the optimisation; defaults to c(50,50).

initPars

Maximum number of iterations for <code>optim</code>. Defaults to 1e3. Set to higher values if convergence problems are reported.

maxiter

Finds the best-fit normal distribution parameters for a given confidence interval and returns the mean and sd parameters.

Propagate uncertainty from several estimates when combining these estimates via a function.
This is done by using the parametric bootstrap to simulate values from the distribution of each estimate to build up an empirical distribution of the combined parameter.
Finally either the percentile method is used or the highest density interval is chosen to derive a confidence interval for the combined parameter with the desired coverage.
Gaussian copulas are used for when parameters are assumed to be dependent / correlated.
References: Davison and Hinkley (1997,ISBN:0-521-57471-4) for the parametric bootstrap and percentile method, Gelman et al. (2014,ISBN:978-1-4398-4095-5) for the highest density interval, Stockdale et al. (2020)<doi:10.1016/j.jhep.2020.04.008> for an example of combining conditional prevalences.

identifyNormPars: Determine the parameters of the best-fit normal / Gaussian distribution for a given confidence interval.

Description

Usage

Arguments

Value

See Also