View source: R/sewma.sf.prerun.R
sewma.sf.prerun | R Documentation |
Computation of the survival function of the Run Length (RL) for EWMA control charts monitoring normal variance.
sewma.sf.prerun(n, l, cl, cu, sigma, df1, df2, hs=1, sided="upper",
qm=30, qm.sigma=30, truncate=1e-10, tail_approx=TRUE)
n |
calculate sf up to value |
l |
smoothing parameter lambda of the EWMA control chart. |
cl |
lower control limit of the EWMA control chart. |
cu |
upper control limit of the EWMA control chart. |
sigma |
true standard deviation. |
df1 |
actual degrees of freedom, corresponds to subgroup size (for known mean it is equal to the subgroup size, for unknown mean it is equal to subgroup size minus one. |
df2 |
degrees of freedom of the pre-run variance estimator. |
hs |
so-called headstart (enables fast initial response). |
sided |
distinguishes between one- and two-sided two-sided
EWMA- |
qm |
number of quadrature nodes for calculating the collocation definite integrals. |
qm.sigma |
number of quadrature nodes for convoluting the standard deviation uncertainty. |
truncate |
size of truncated tail. |
tail_approx |
Controls whether the geometric tail approximation is used (is faster) or not. |
The survival function P(L>n) and derived from it also the cdf P(L<=n) and the pmf P(L=n) illustrate the distribution of the EWMA run length. For large n the geometric tail could be exploited. That is, with reasonable large n the complete distribution is characterized. The algorithm is based on Waldmann's survival function iteration procedure and on results in Knoth (2007)...
Returns a vector which resembles the survival function up to a certain point.
Sven Knoth
S. Knoth (2007), Accurate ARL calculation for EWMA control charts monitoring simultaneously normal mean and variance, Sequential Analysis 26, 251-264.
K.-H. Waldmann (1986), Bounds for the distribution of the run length of geometric moving average charts, Appl. Statist. 35, 151-158.
sewma.sf
for the RL survival function of EWMA control charts w/o pre-run uncertainty.
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