PH1XBAR (version 0.11.3)

getCC.ARMA: get Phase I corrected charting constant with an ARMA model

Description

get Phase I corrected charting constant with an ARMA model

Usage

getCC.ARMA(
  fap0 = 0.05,
  interval = c(1, 4),
  m = 50,
  order = c(1, 0),
  phi.vec = 0.5,
  theta.vec = NULL,
  case = "U",
  method = "MLE+MOM",
  nsim.coefs = 100,
  nsim.process = 1000,
  burn.in = 50,
  sim.type = "Recursive",
  verbose = FALSE
)

Value

Object type double. The corrected charting constant.

Arguments

fap0

nominal false Alarm Probabilty in Phase 1

interval

searching range of charting constants for the exact method

m

number of observations

order

order for ARMA(p, q) model

phi.vec

a vector of length p containing autoregressive coefficient(s). When case = 'K', the vector must have a length equal to the first value in the order. If no autoregressive coefficent presents, set phi.vec = NULL

theta.vec

a vector of length q containing moving-average coefficient(s). When case = 'K', the vector must have a length equal to the first value in the order. If no moving-average coefficent presents, set theta.vec = NULL

case

known or unknown case. When case = 'U', the parameters are estimated, when case = 'K', the parameters need to be input

method

estimation method for the control chart. When method = 'Method 3' is maximum likehood estimations plus method of moments. Other options are 'Method 1' which is pure MLE and 'Method 2' which is pure CSS.

nsim.coefs

number of simulation for coeficients. It is functional when double.sim = TRUE.

nsim.process

number of simulation for ARMA processes

burn.in

number of burn-ins. When burn.in = 0, the ECM gets involved. When burn.in is large enough, the ACM gets involved.

sim.type

type of simulation. When sim.type = 'Matrix', the simulation is generated using matrix computation. When sim.type = 'Recursive', the simulation is based on a recursion.

verbose

print diagnostic information about fap0 and the charting constant during the simulations for the exact method

Examples

# load the data in the package as an example
set.seed(12345)

# Calculate the charting constant using fap0 of 0.05, and 50 observations
getCC.ARMA(fap0=0.05, m=50, nsim.coefs=10, nsim.process=10)