Description Usage Arguments Details Value Note Author(s) References See Also Examples
View source: R/cg._g.r View source: R/cg._g.r
Function visualize the given values of measurement in a run chart and in a histogram. Furthermore the “centralized Gage potential index” Cg and the “noncentralized Gage Capability index” Cgk are calculated and displayed.
1 2 3 4 5 6 7 8 9 10 11 12 13  cg (x, target, tolerance, ref.interval, facCg, facCgk, n = 0.2,
type, col, pch, xlim, ylim, conf.level = 0.95, cex.val = 1.5)
cgToleranceView(x, target, tolerance, ref.interval, facCg, facCgk, n = 0.2,
type, col, pch, xlim, ylim, main, conf.level = 0.95, cex.val = 1,
cgOut = TRUE)
cgHist (x, target, tolerance, ref.interval, facCg, facCgk, n = 0.2, col,
xlim, ylim, main, conf.level = 0.95, cex.val = 1, cgOut = TRUE)
cgRunChart (x, target, tolerance, ref.interval, facCg, facCgk, n = 0.2,
type, col, pch, xlim, ylim,main, conf.level = 0.95, cex.val = 1,
cgOut = TRUE)

x 
a vector containing the measured values. 
target 
a numeric value giving the expected target value for the xvalues. 
tolerance 
vector of length 2 giving the lower and upper specification limits. 
ref.interval 
numeric value giving the confidence intervall on which the calculation is based.
By default it is based on 6 sigma methodology. Regarding the normal distribution
this relates to 
facCg 
numeric value as a factor for the calculation of the gage potential index. The default Value for facCg is ‘0.2’. 
facCgk 
numeric value as a factor for the calulation of the gage capability index. The default value for facCgk is ‘0.1’. 
n 
numeric value between ‘0’ and ‘1’ giving the percentage of the tolerance field (values between the upper and lower specification limits given by tolerance) where the values of x should be positioned. Limit lines will be drawn. Default value is ‘0.2’. 
type 
what type of plot should be drawn in the run chart. Possible types see

col 
color of the curve in the run chart. 
pch 
variable specifies the symbols of the run chart. Details see 
xlim 
vector of length 2 giving the limits for the x axis of the run chart. 
ylim 
vector of length 2 giving the limits for the y axis of the run chart. 
main 
an overall title for the plot: see 
conf.level 
confidence level for internal t.test checking the significance of the bias between target and mean of x. The default value is ‘0.95’. The result of the t.test is shown in the histogram on the left side. 
cex.val 
numeric value giving the size of the text in the legend. See also 
cgOut 
logical value deciding wether the Cg and Cgk values should be plotted in a legend. Only
available for the function 
The calculation of the potential and actual gage capability are based on the
following formulae:
Cg = (facCg * tolerance[2]tolerance[1])/ref.interval
Cgk = (facCgk * abs(targetmean(x))/(ref.interval/2)
If the usage of the historical process variation is preferred the values for
the tolerance tolerance
must be adjusted manually. That means in case of the
6 sigma methodolgy for example, that tolerance = 6 * sigma[process].
Function returns a list of numeric values. The first element contains the calculated centralized gage potential index Cg and the second contains the noncentralized gage capability index Cgk.
Support for other distributions than normal might be included later in an update.
For a more detailed example which shows the usage cg()
please read the vignette for the package
qualityTools
at http://www.rqualitytools.org/html/Measure.html.
Thomas Roth: [email protected]
Etienne Stockhausen: [email protected]
DIETRICH, Edgar; SCHULZE, Alfred: Pruefprozesseignung,
3rd ed. Munich: Carl Hanser, 2007.
DIETRICH, Edgar et al: Eignungsnachweis von Messsystemen, 3rd ed. Munich: Carl Hanser, 2008.
gageLin
gageRR
plot
par
http://www.rqualitytools.org/html/Measure.html
1 2 3 4 5 6 7 
#simple example with default values
cg(rnorm(125,mean = 10.01 ,sd = 0.1), target = 10, tolerance = c(8,12))
#example with larger n and adjusted ref. interval
cg(rnorm(25,mean = 1.01 ,sd = 0.5), ref.interval=pnorm(5.5/2)pnorm(5.5/2), n=0.3)
#example with changed factors for Cg and Cgk
cg(rnorm(75, sd = 0.1), facCg = 0.15, facCgk = 0.075, tolerance = c(10,10)/6)

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