SPCShew-class: Shewhart charts.
In spcadjust: Functions for Calibrating Control Charts
Description
Shewhart charts.
Slots
twosidedTRUE if a two-sided chart should be used. Default FALSE.
Examples
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X<-rnorm(100);
##calibrate to ARL 100
chartShew <- new("SPCShew",model=SPCModelNormal(),twosided=TRUE)
## Not run:
SPCproperty(data=X,nrep=500,
property="calARL",chart=chartShew,params=list(target=100),
covprob=c(0.7,0.9))
chartShewOneSided <- new("SPCShew",model=SPCModelNormal(),twosided=FALSE)
SPCproperty(data=X,nrep=500,
property="calARL",chart=chartShewOneSided,
params=list(target=100),covprob=c(0.7,0.9))
##calibrate to a hitting probability of 0.01 in 100 steps
SPCproperty(data=X,nrep=500,
property="calhitprob",
chart=chartShew,params=list(target=0.01,nsteps=100))
SPCproperty(data=X,nrep=500,
property="calhitprob",chart=chartShewOneSided,params=list(target=0.01,nsteps=100))
## work out for ARL for a fixed threshold of 4
SPCproperty(data=X,nrep=500,
property="ARL",chart=chartShew,params=list(threshold=4))
SPCproperty(data=X,nrep=500,
property="ARL",chart=chartShewOneSided,
params=list(threshold=4))
SPCproperty(data=X,nrep=500,
property="hitprob",chart=chartShew,params=list(nsteps=100,threshold=4))
SPCproperty(data=X,nrep=500,
property="hitprob",chart=chartShewOneSided,params=list(nsteps=100,threshold=4))
## End(Not run)
X<-rnorm(100)
chartShew <- new("SPCShew",model=SPCModelNormal())
## Not run:
SPCproperty(data=X,nrep=500,
property="calARL", chart=chartShew,
params=list(target=1000))
SPCproperty(data=X,nrep=500,
property="calhitprob",chart=chartShew,
params=list(target=0.01,nsteps=100))
SPCproperty(data=X,nrep=10,chart=chartShew,
property="ARL",params=list(threshold=3))
SPCproperty(data=X,nrep=500,
property="hitprob",
chart=chartShew,params=list(nsteps=100,threshold=4))
## End(Not run)
Example output
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70 % CI: A threshold of 2.69 gives an in-control ARL of at least 100.
90 % CI: A threshold of 2.846 gives an in-control ARL of at least 100.
Unadjusted result: 2.576
Based on 500 bootstrap repetitions.
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70 % CI: A threshold of 2.441 gives an in-control ARL of at least 100.
90 % CI: A threshold of 2.592 gives an in-control ARL of at least 100.
Unadjusted result: 2.326
Based on 500 bootstrap repetitions.
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90 % CI: A threshold of 4.258 gives an in-control false alarm
probability of at most 0.01 within 100 steps.
Unadjusted result: 3.889
Based on 500 bootstrap repetitions.
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90 % CI: A threshold of 4.147 gives an in-control false alarm
probability of at most 0.01 within 100 steps.
Unadjusted result: 3.718
Based on 500 bootstrap repetitions.
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90 % CI: A threshold of 4 gives an in-control ARL of at least 2988.
Unadjusted result: 15787
Based on 500 bootstrap repetitions.
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90 % CI: A threshold of 4 gives an in-control ARL of at least 6220.
Unadjusted result: 31574
Based on 500 bootstrap repetitions.
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90 % CI: A threshold of 4 gives an in-control false alarm probability
of at most 0.03612 within 100 steps.
Unadjusted result: 0.006314
Based on 500 bootstrap repetitions.
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90 % CI: A threshold of 4 gives an in-control false alarm probability
of at most 0.01453 within 100 steps.
Unadjusted result: 0.003162
Based on 500 bootstrap repetitions.
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90 % CI: A threshold of 3.441 gives an in-control ARL of at least 1000.
Unadjusted result: 3.09
Based on 500 bootstrap repetitions.
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90 % CI: A threshold of 4.113 gives an in-control false alarm
probability of at most 0.01 within 100 steps.
Unadjusted result: 3.718
Based on 500 bootstrap repetitions.
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90 % CI: A threshold of 3 gives an in-control ARL of at least 354.1.
Unadjusted result: 740.8
Based on 10 bootstrap repetitions.
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90 % CI: A threshold of 4 gives an in-control false alarm probability
of at most 0.0161 within 100 steps.
Unadjusted result: 0.003162
Based on 500 bootstrap repetitions.
spcadjust documentation built on May 1, 2019, 7:49 p.m.
R Package Documentation
Browse R Packages
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description
Shewhart charts.
Slots
twosidedTRUE if a two-sided chart should be used. Default FALSE.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | X<-rnorm(100);
##calibrate to ARL 100
chartShew <- new("SPCShew",model=SPCModelNormal(),twosided=TRUE)
## Not run:
SPCproperty(data=X,nrep=500,
property="calARL",chart=chartShew,params=list(target=100),
covprob=c(0.7,0.9))
chartShewOneSided <- new("SPCShew",model=SPCModelNormal(),twosided=FALSE)
SPCproperty(data=X,nrep=500,
property="calARL",chart=chartShewOneSided,
params=list(target=100),covprob=c(0.7,0.9))
##calibrate to a hitting probability of 0.01 in 100 steps
SPCproperty(data=X,nrep=500,
property="calhitprob",
chart=chartShew,params=list(target=0.01,nsteps=100))
SPCproperty(data=X,nrep=500,
property="calhitprob",chart=chartShewOneSided,params=list(target=0.01,nsteps=100))
## work out for ARL for a fixed threshold of 4
SPCproperty(data=X,nrep=500,
property="ARL",chart=chartShew,params=list(threshold=4))
SPCproperty(data=X,nrep=500,
property="ARL",chart=chartShewOneSided,
params=list(threshold=4))
SPCproperty(data=X,nrep=500,
property="hitprob",chart=chartShew,params=list(nsteps=100,threshold=4))
SPCproperty(data=X,nrep=500,
property="hitprob",chart=chartShewOneSided,params=list(nsteps=100,threshold=4))
## End(Not run)
X<-rnorm(100)
chartShew <- new("SPCShew",model=SPCModelNormal())
## Not run:
SPCproperty(data=X,nrep=500,
property="calARL", chart=chartShew,
params=list(target=1000))
SPCproperty(data=X,nrep=500,
property="calhitprob",chart=chartShew,
params=list(target=0.01,nsteps=100))
SPCproperty(data=X,nrep=10,chart=chartShew,
property="ARL",params=list(threshold=3))
SPCproperty(data=X,nrep=500,
property="hitprob",
chart=chartShew,params=list(nsteps=100,threshold=4))
## End(Not run)
|
Example output
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70 % CI: A threshold of 2.69 gives an in-control ARL of at least 100.
90 % CI: A threshold of 2.846 gives an in-control ARL of at least 100.
Unadjusted result: 2.576
Based on 500 bootstrap repetitions.
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70 % CI: A threshold of 2.441 gives an in-control ARL of at least 100.
90 % CI: A threshold of 2.592 gives an in-control ARL of at least 100.
Unadjusted result: 2.326
Based on 500 bootstrap repetitions.
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90 % CI: A threshold of 4.258 gives an in-control false alarm
probability of at most 0.01 within 100 steps.
Unadjusted result: 3.889
Based on 500 bootstrap repetitions.
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90 % CI: A threshold of 4.147 gives an in-control false alarm
probability of at most 0.01 within 100 steps.
Unadjusted result: 3.718
Based on 500 bootstrap repetitions.
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90 % CI: A threshold of 4 gives an in-control ARL of at least 2988.
Unadjusted result: 15787
Based on 500 bootstrap repetitions.
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90 % CI: A threshold of 4 gives an in-control ARL of at least 6220.
Unadjusted result: 31574
Based on 500 bootstrap repetitions.
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90 % CI: A threshold of 4 gives an in-control false alarm probability
of at most 0.03612 within 100 steps.
Unadjusted result: 0.006314
Based on 500 bootstrap repetitions.
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90 % CI: A threshold of 4 gives an in-control false alarm probability
of at most 0.01453 within 100 steps.
Unadjusted result: 0.003162
Based on 500 bootstrap repetitions.
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90 % CI: A threshold of 3.441 gives an in-control ARL of at least 1000.
Unadjusted result: 3.09
Based on 500 bootstrap repetitions.
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90 % CI: A threshold of 4.113 gives an in-control false alarm
probability of at most 0.01 within 100 steps.
Unadjusted result: 3.718
Based on 500 bootstrap repetitions.
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90 % CI: A threshold of 3 gives an in-control ARL of at least 354.1.
Unadjusted result: 740.8
Based on 10 bootstrap repetitions.
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90 % CI: A threshold of 4 gives an in-control false alarm probability
of at most 0.0161 within 100 steps.
Unadjusted result: 0.003162
Based on 500 bootstrap repetitions.
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