qcs.ca | R Documentation |
Calculates the process capability indices cp, cpk, cpL cpU, cpm, cpmk for a qcs object and normal distribution. Also, this function calculates confidence limits for C_p using the method described by Chou et al. (1990). Approximate confidence limits for C_pl, C_pu and C_pk are computed using the method in Bissell (1990). Confidence limits for C_pm are based on the method of Boyles (1991); this method is approximate and it assumes the target is midway between the specification limits. Moreover, calculates the process capability indices cnp, cnpk, cnpm, cnpmk for a qcs object. A histogramm with a density curve is displayed along with the specification limits, a Quantile-Quantile Plot for the specified distribution and contour graph is plotted for estimate the indice cpm.
qcs.ca( object, limits = c(lsl = -3, usl = 3), target = NULL, std.dev = NULL, nsigmas = 3, confidence = 0.9973, plot = TRUE, main = NULL, ... )
object |
qcs object of type |
limits |
A vector specifying the lower and upper specification limits. |
target |
A value specifying the target of the process.
If is |
std.dev |
A value specifying the within-group standard deviation. |
nsigmas |
A numeric value specifying the number of sigmas to use. |
confidence |
A numeric value between 0 and 1 specifying the probabilities for computing the quantiles. This values is used only when object values is provided. The default value is 0.9973. |
plot |
Logical value indicating whether graph should be plotted. |
main |
Title of the plot. |
... |
Arguments to be passed to or from methods. |
Montgomery, D.C. (1991) Introduction to Statistical Quality Control, 2nd
ed, New York, John Wiley & Sons.
Tong, L.I. and Chen, J.P. (1998), Lower con???dence limits of process capability
indices for nonnormal process distributions. International Journal of Quality & Reliability Management,
Vol. 15 No. 8/9, pp. 907-19.
Vannman, K (1995) A Unified Approach to Capability Indices. Statitica Sinica,5,805-820.
Vannman, K. (2001). A Graphical Method to Control Process Capability. Frontiers in Statistical Quality Control,
No 6, Editors: H-J Lenz and P-TH Wilrich. Physica-Verlag, Heidelberg, 290-311.
Hubele and Vannman (2004). The E???ect of Pooled and Un-pooled Variance Estimators on Cpm When Using Subsamples.
Journal Quality Technology, 36, 207-222.
library(qcr) data(pistonrings) xbar <- qcs.xbar(pistonrings[1:125,],plot = TRUE) LSL=73.99; USL=74.01 limits = c(lsl = 73.99, usl = 74.01) qcs.ca(xbar, limits = limits)
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