getCC.ARMA | R Documentation |
get Phase I corrected charting constant with an ARMA model
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
)
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 |
Object type double. The corrected charting constant.
# 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)
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