xDewma.arl: Compute ARLs of EWMA control charts under drift

In spc: Statistical Process Control -- Calculation of ARL and Other Control Chart Performance Measures

Compute ARLs of EWMA control charts under drift

Description

Computation of the (zero-state and other) Average Run Length (ARL) under drift for different types of EWMA control charts monitoring normal mean.

Usage

xDewma.arl(l, c, delta, zr = 0, hs = 0, sided = "one", limits = "fix",
    mode = "Gan", m = NULL, q = 1, r = 40, with0 = FALSE)

Arguments

l	smoothing parameter lambda of the EWMA control chart.
c	critical value (similar to alarm limit) of the EWMA control chart.
delta	true drift parameter.
zr	reflection border for the one-sided chart.
hs	so-called headstart (enables fast initial response).
sided	distinguish between one- and two-sided EWMA control chart by choosing "one" and "two", respectively.
limits	distinguishes between different control limits behavior.
mode	decide whether Gan's or Knoth's approach is used. Use "Gan" and "Knoth", respectively.
m	parameter used if mode="Gan". m is design parameter of Gan's approach. If m=NULL, then m will increased until the resulting ARL does not change anymore.
q	change point position. For q=1 and μ=μ_0 and μ=μ_1, the usual zero-state ARLs for the in-control and out-of-control case, respectively, are calculated. For q>1 and μ!=0 conditional delays, that is, E_q(L-q+1|L≥q), will be determined. Note that mu0=0 is implicitely fixed. Deploy large q to mimic steady-state. It works only for mode="Knoth".
r	number of quadrature nodes, dimension of the resulting linear equation system is equal to r+1 (one-sided) or r (two-sided).
with0	defines whether the first observation used for the RL calculation follows already 1*delta or still 0*delta. With q additional flexibility is given.

Details

Based on Gan (1991) or Knoth (2003), the ARL is calculated for EWMA control charts under drift. In case of Gan's framework, the usual ARL function with mu=m*delta is determined and recursively via m-1, m-2, ... 1 (or 0) the drift ARL determined. The framework of Knoth allows to calculate ARLs for varying parameters, such as control limits and distributional parameters. For details see the cited papers.

Value

Returns a single value which resembles the ARL.

Author(s)

Sven Knoth

References

F. F. Gan (1991), EWMA control chart under linear drift, J. Stat. Comput. Simulation 38, 181-200.

L. A. Aerne, C. W. Champ and S. E. Rigdon (1991), Evaluation of control charts under linear trend, Commun. Stat., Theory Methods 20, 3341-3349.

S. Knoth (2003), EWMA schemes with non-homogeneous transition kernels, Sequential Analysis 22, 241-255.

H. M. Fahmy and E. A. Elsayed (2006), Detection of linear trends in process mean, International Journal of Production Research 44, 487-504.

S. Knoth (2012), More on Control Charting under Drift, in: Frontiers in Statistical Quality Control 10, H.-J. Lenz, W. Schmid and P.-T. Wilrich (Eds.), Physica Verlag, Heidelberg, Germany, 53-68.

C. Zou, Y. Liu and Z. Wang (2009), Comparisons of control schemes for monitoring the means of processes subject to drifts, Metrika 70, 141-163.

See Also

xewma.arl and xewma.ad for zero-state and steady-state ARL computation of EWMA control charts for the classical step change model.

Examples

## Not run: 
DxDewma.arl <- Vectorize(xDewma.arl, "delta")
## Gan (1991)
## Table 1
## original values are
#  delta   arlE1  arlE2  arlE3
#  0       500    500    500
#  0.0001  482    460    424
#  0.0010  289    231    185
#  0.0020  210    162    129
#  0.0050  126     94.6   77.9
#  0.0100   81.7   61.3   52.7
#  0.0500   27.5   21.8   21.9
#  0.1000   17.0   14.2   15.3
#  1.0000    4.09   4.28   5.25
#  3.0000    2.60   2.90   3.43
#
lambda1 <- 0.676
lambda2 <- 0.242
lambda3 <- 0.047
h1 <- 2.204/sqrt(lambda1/(2-lambda1))
h2 <- 1.111/sqrt(lambda2/(2-lambda2))
h3 <- 0.403/sqrt(lambda3/(2-lambda3))
deltas <- c(.0001, .001, .002, .005, .01, .05, .1, 1, 3)
arlE10 <- round(xewma.arl(lambda1, h1, 0, sided="two"), digits=2)
arlE1 <- c(arlE10, round(DxDewma.arl(lambda1, h1, deltas, sided="two", with0=TRUE),
                         digits=2))
arlE20 <- round(xewma.arl(lambda2, h2, 0, sided="two"), digits=2)
arlE2 <- c(arlE20, round(DxDewma.arl(lambda2, h2, deltas, sided="two", with0=TRUE),
                         digits=2))
arlE30 <- round(xewma.arl(lambda3, h3, 0, sided="two"), digits=2)
arlE3 <- c(arlE30, round(DxDewma.arl(lambda3, h3, deltas, sided="two", with0=TRUE),
                         digits=2))
data.frame(delta=c(0, deltas), arlE1, arlE2, arlE3)

## do the same with more digits for the alarm threshold
L0 <- 500
h1 <- xewma.crit(lambda1, L0, sided="two")
h2 <- xewma.crit(lambda2, L0, sided="two")
h3 <- xewma.crit(lambda3, L0, sided="two")
lambdas <- c(lambda1, lambda2, lambda3)
hs <- c(h1, h2, h3) * sqrt(lambdas/(2-lambdas))
hs
# compare with Gan's values 2.204, 1.111, 0.403
round(hs, digits=3)

arlE10 <- round(xewma.arl(lambda1, h1, 0, sided="two"), digits=2)
arlE1 <- c(arlE10, round(DxDewma.arl(lambda1, h1, deltas, sided="two", with0=TRUE),
                         digits=2))
arlE20 <- round(xewma.arl(lambda2, h2, 0, sided="two"), digits=2)
arlE2 <- c(arlE20, round(DxDewma.arl(lambda2, h2, deltas, sided="two", with0=TRUE),
                         digits=2))
arlE30 <- round(xewma.arl(lambda3, h3, 0, sided="two"), digits=2)
arlE3 <- c(arlE30, round(DxDewma.arl(lambda3, h3, deltas, sided="two", with0=TRUE),
                         digits=2))
data.frame(delta=c(0, deltas), arlE1, arlE2, arlE3)

## Aerne et al. (1991) -- two-sided EWMA
## Table I (continued)
## original numbers are
#     delta  arlE1  arlE2  arlE3
#  0.000000  465.0  465.0  465.0
#  0.005623  77.01  85.93  102.68
#  0.007499  64.61  71.78  85.74
#  0.010000  54.20  59.74  71.22
#  0.013335  45.20  49.58  58.90
#  0.017783  37.76  41.06  48.54
#  0.023714  31.54  33.95  39.87
#  0.031623  26.36  28.06  32.68
#  0.042170  22.06  23.19  26.73
#  0.056234  18.49  19.17  21.84
#  0.074989  15.53  15.87  17.83
#  0.100000  13.07  13.16  14.55
#  0.133352  11.03  10.94  11.88
#  0.177828   9.33   9.12   9.71
#  0.237137   7.91   7.62   7.95
#  0.316228   6.72   6.39   6.52
#  0.421697   5.72   5.38   5.37
#  0.562341   4.88   4.54   4.44
#  0.749894   4.18   3.84   3.68
#  1.000000   3.58   3.27   3.07
#
lambda1 <- .133
lambda2 <- .25
lambda3 <- .5
cE1 <- 2.856
cE2 <- 2.974
cE3 <- 3.049
deltas <- 10^(-(18:0)/8)
arlE10 <- round(xewma.arl(lambda1, cE1, 0, sided="two"), digits=2)
arlE1 <- c(arlE10, round(DxDewma.arl(lambda1, cE1, deltas, sided="two"), digits=2))
arlE20 <- round(xewma.arl(lambda2, cE2, 0, sided="two"), digits=2)
arlE2 <- c(arlE20, round(DxDewma.arl(lambda2, cE2, deltas, sided="two"), digits=2))
arlE30 <- round(xewma.arl(lambda3, cE3, 0, sided="two"), digits=2)
arlE3 <- c(arlE30, round(DxDewma.arl(lambda3, cE3, deltas, sided="two"), digits=2))
data.frame(delta=c(0, round(deltas, digits=6)), arlE1, arlE2, arlE3)


## Fahmy/Elsayed (2006) -- two-sided EWMA
## Table 4 (Monte Carlo results, 10^4 replicates, change point at t=51!)
## original numbers are
#   delta     arl  s.e.
#   0.00  365.749  3.598
#   0.10   12.971  0.029
#   0.25    7.738  0.015
#   0.50    5.312  0.009
#   0.75    4.279  0.007
#   1.00    3.680  0.006
#   1.25    3.271  0.006
#   1.50    2.979  0.005
#   1.75    2.782  0.004
#   2.00    2.598  0.005
#
lambda <- 0.1
cE <- 2.7
deltas <- c(.1, (1:8)/4)
arlE1 <- c(round(xewma.arl(lambda, cE, 0, sided="two"), digits=3),
           round(DxDewma.arl(lambda, cE, deltas, sided="two"), digits=3))
arlE51 <- c(round(xewma.arl(lambda, cE, 0, sided="two", q=51)[51], digits=3),
     round(DxDewma.arl(lambda, cE, deltas, sided="two", mode="Knoth", q=51),
           digits=3))
data.frame(delta=c(0, deltas), arlE1, arlE51)

## additional Monte Carlo results with 10^8 replicates
#   delta   arl.q=1   s.e.    arl.q=51  s.e.
#   0.00    368.910   0.036   361.714   0.038
#   0.10     12.986   0.000    12.781   0.000
#   0.25      7.758   0.000     7.637   0.000
#   0.50      5.318   0.000     5.235   0.000
#   0.75      4.285   0.000     4.218   0.000
#   1.00      3.688   0.000     3.628   0.000
#   1.25      3.274   0.000     3.233   0.000
#   1.50      2.993   0.000     2.942   0.000
#   1.75      2.808   0.000     2.723   0.000
#   2.00      2.616   0.000     2.554   0.000

## Zou et al. (2009) -- one-sided EWMA
## Table 1
## original values are
#  delta   arl1  arl2  arl3
#  0           ~ 1730
#  0.0005  317   377   440
#  0.001   215   253   297
#  0.005   83.6  92.6  106
#  0.01    55.6  58.8  66.1
#  0.05    22.6  21.1  22.0
#  0.1     15.5  13.9  13.8
#  0.5     6.65  5.56  5.09
#  1.0     4.67  3.83  3.43
#  2.0     3.21  2.74  2.32
#  3.0     2.86  2.06  1.98
#  4.0     2.14  2.00  1.83
l1 <- 0.03479
l2 <- 0.11125
l3 <- 0.23052
c1 <- 2.711
c2 <- 3.033
c3 <- 3.161
zr <- -6
r  <- 50
deltas <- c(0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1:4)
arl1 <- c(round(xewma.arl(l1, c1, 0, zr=zr, r=r), digits=2),
          round(DxDewma.arl(l1, c1, deltas, zr=zr, r=r), digits=2))
arl2 <- c(round(xewma.arl(l2, c2, 0, zr=zr), digits=2),
          round(DxDewma.arl(l2, c2, deltas, zr=zr, r=r), digits=2))
arl3 <- c(round(xewma.arl(l3, c3, 0, zr=zr, r=r), digits=2),
          round(DxDewma.arl(l3, c3, deltas, zr=zr, r=r), digits=2))
data.frame(delta=c(0, deltas), arl1, arl2, arl3)

## End(Not run)

spc documentation built on Oct. 24, 2022, 5:07 p.m.