CautiousLearning: simulation – R documentation

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simulation

Estimation errors and conditional run-length simulation

Description

Function ruv simulates the standardized estimation errors at the starting of the monitoring phase (see Section 2.3 of Capizzi and Masarotto (2019)).

Function rcrl simulates, under different in-control or out-control scenarios, the conditional run-length given the standardized estimation errors. When openMP is supported, computation can be distribuited on multiple cores. See omp.

Usage

ruv(n, m)

rcrl(n, chart, u, v, tau, delta, omega, maxrl = 1000000L)

Arguments

`n`	number of simulated values.
`m`	number of in-control observations available at the starting of the monitoring phase.
`chart`	list with the same elements as those returned by `x.cl`, `ewma.cl` and `cusum.cl`.
`u, v`	values of the estimation errors (scalars).
`tau, delta, omega`	when i<tau, observations are distributed as N(mu,sigma^2) random variables; when i>=tau, observations are distributed as N(mu+deltasigma, (omegasigma)^2) random variables.
`maxrl`	run-length are truncated at i=maxrl.

Value

`ruv`	numeric matrix of dimension nx2.
`rcrl`	integer vector of length n.

Author(s)

Giovanna Capizzi and Guido Masarotto

References

Capizzi, G. and Masarotto, G. (2019) "Guaranteed In-Control Control Chart Performance with Cautious Parameter Learning", accepted for publication in Journal of Quality Technology, a copy of the paper can be obtained from the authors.

Examples

ruv(5, 100)
## EWMA control chart (nominal ARL=500, 
## initial estimates based on 100 in-control observations)
chart <- list(chart = "EWMA",
              lambda = 0.2,
              limit = c(Linf=3.187, Delta=0.427, A=1.5, B=50, m=100))
rcrl(10, chart, 2, 1, 50, 2, 1)

CautiousLearning

Control Charts with Guaranteed In-Control Performance and Cautious Parameters Learning

v1.0.1

MIT + file LICENSE

Authors