Compute the Probability of False Alarm, PFA, and the Conditional Expected Delay, CED, for the Normal, Binomial and Poisson distributions
cusumPfaCedBinom(size0 = 0, prob0 = 1, size1 = 0, prob1 = 1,
tau = 10, N = 100, limit = 10000, seed = NA,
kp = 1, km = -1, hp = 3, hm = -3, side = "both",
printSummary = TRUE)cusumPfaCedNorm(mean0 = 0, sd0=1, mean1=0, sd1=1,
tau=10, N=100, limit=10000, seed=NA,
kp=1, km=-1, hp=3, hm=-3, side="both",
printSummary = TRUE)
cusumPfaCedPois(lambda0 = 0, lambda1=1,
tau=10, N=100, limit=10000, seed=NA,
kp=1, km=-1, hp=3, hm=-3, side="both",
printSummary = TRUE)
a list with elements:
a numeric vector representing the Run Length of the simulation
a numeric vector with summary statistics
a list of length N
elements each of which
has single numeric elements violationLower
, violationUpper
and rl
number of trials (zero or more)
probability of success on each trial
number of trials (zero or more) after a process level change
probability of success on each trial after a process level change
distribution mean
distribution standard deviation
distribution mean after a process level change
distribution standard deviation after a process level change
(non-negative) mean
(non-negative) mean after a process level change
time on which the process level change occurs
the number of replicates
safety parameter, stop rule for procedures with very long ARL
a single value, interpreted as an integer. If specified make the simulation replicable.
a character string specifying the side of the control scheme, must be one of "both" (default), "upper" or "lower"
logical, if TRUE
print a summary of the cusum PFA and CED
Daniele Amberti
Kenett, R., Zacks, S. with contributions by Amberti, D. Modern Industrial Statistics: with applications in R, MINITAB and JMP. Wiley.
cusumPfaCedNorm(mean1=1.5,
tau=100,
N=100,
limit=1000,
seed=123)
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