If you examine the code for the function "violating.runs" in the qcc
package (try "violating.runs") you can deconstruct it to find that the code
flags runs where there are .qcc.options$run.length or more points in a row on
one side of the center (process mean).
However, the classic Shewhart rules dictate that a run of monotonically
increasing or decreasing points of <run length> is also a run violation. I
include here my modified version of the function "violating.runs" which also
checks for these violations:
##
## Correct some typos in violating.runs from qcc package
## Added test for run.length of points monotonically increasing or decreasing
## The simplest way is to re-run the code but with "diffs"
## representing the sign of the difference from one point to the next
##
violating.runs<-function (object, run.length = qcc.options("run.length"))
{
center <- object$center
statistics <- c(object$statistics, object$new.statistics)
cl <- object$limits
violators <- numeric()
for(i in 1:2){
diffs <- statistics - center
if(i==2) {
diffs <- c(0,diff(statistics))
## need to decrement run.length since we're looking at differences
between points
run.length<-run.length-1
}
diffs[diffs > 0] <- 1
diffs[diffs < 0] <- -1
runs <- rle(diffs)
index.lengths <- (1:length(runs$lengths))[runs$lengths >= run.length]
index.stats <- 1:length(statistics)
vruns <- rep(runs$lengths >= run.length, runs$lengths)
vruns.above <- (vruns & (diffs > 0))
vruns.below <- (vruns & (diffs < 0))
rvruns.above <- rle(vruns.above)
rvruns.below <- rle(vruns.below)
vbeg.above <-
cumsum(rvruns.above$lengths)[rvruns.above$values]-(rvruns.above$lengths -
run.length)[rvruns.above$values]
vend.above <- cumsum(rvruns.above$lengths)[rvruns.above$values]
vbeg.below <-
cumsum(rvruns.below$lengths)[rvruns.below$values]-(rvruns.below$lengths -
run.length)[rvruns.below$values]
vend.below <- cumsum(rvruns.below$lengths)[rvruns.below$values]
if (length(vbeg.above)) {
for (i in 1:length(vbeg.above)) violators <- c(violators,
vbeg.above[i]:vend.above[i])
}
if (length(vbeg.below)) {
for (i in 1:length(vbeg.below)) violators <- c(violators,
vbeg.below[i]:vend.below[i])
}
} ## ENDOF for i in 1:2
return(violators)
}
> 3)Is there any more criterias made somewhere ?
The other criteria is found by the function "beyond.limits" which
checks to see if any points are beyond the upper or lower control limits.
I believe that Prof. Scrucca wrote the qcc package as a tool to
demonstrate process control to his students taking his statistics classes.
Hence the statistics are excellent. For example note the use of log-gamma and
exponential functions in the function "sd.xbar" to calculate the constant "c4",
which avoids the divison of extremely large [~ 1E20] numbers:
c4 <- function(n) sqrt(2/(n-1)) * exp(lgamma(n/2) - lgamma((n-1)/2)))
where a textbook might write it as:
c4 <- function(n) sqrt(2/(n-1)) * (gamma(n/2) / gamma((n-1)/2)))
Thus I suggest that although qcc provides an excellent basis for
constructing statistical control reports, that you should be prepared to modify
it for your purposes (as I am heavily doing).
P.S. I change .qcc.options using syntax such as
".qcc.options$violating.runs<-7".
Leif S. Kirschenbaum, Ph.D.
Senior Yield Engineer
Reflectivity, Inc.
408-737-8100 x307
408-737-8153 (Fax)
> Date: Tue, 29 Nov 2005 02:08:57 +0200
> From: Tommi Viitanen <[EMAIL PROTECTED]>
> Subject: [R] qcc
>
> violating.runs
[deleted]
>
> that the criteria for the violating is 5 but
> 1)I cannot find "5" in the code of the function. Where is the "5" ?
> 2)What is the easiest way to change it ?
> 3)Is there any more criterias made somewhere ?
>
> Yours sincerelly, Tommi Viitanen
>
[deleted]
> ------------------------------
>
> Date: Tue, 29 Nov 2005 08:58:24 +0100
> From: Uwe Ligges <[EMAIL PROTECTED]>
> Subject: Re: [R] qcc
> To: Tommi Viitanen <[EMAIL PROTECTED]>
>
>
> See ?violating.runs:
> violating.runs(object, run.length = qcc.options("run.length"))
> ^^^^^ ^^^ ^^^ ^^ ^^^ ^^^
>
[deleted]
>
> Uwe Ligges
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