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Last data update: 2014.03.03

R: Process capability analysis

process.capability

R Documentation

Process capability analysis

Description

Computes process capability indices for a 'qcc' object of type "xbar" and plot the histogram.

Usage

process.capability(object,spec.limits, target, std.dev, nsigmas, 
                   confidence.level = 0.95, breaks = "scott", 
                   add.stats = TRUE, print = TRUE, 
                   digits = getOption("digits"), restore.par = TRUE)

Arguments

`object`	a 'qcc' object of type `"xbar"`
`spec.limits`	a two-values vector specifying the lower and upper specification limits. For one-sided specification limits, the value of the missing limit must be set to `NA`.
`target`	a value specifying the target of the process. If missing the value from the 'qcc' object is used if not `NULL`, otherwise the target is set at the middle value bewteen specification limits.
`std.dev`	a value specifying the within-group standard deviation. If not provided is taken from the 'qcc' object.
`nsigmas`	a numeric value specifying the number of sigmas to use. If not provided is taken from the 'qcc' object.
`confidence.level`	a numeric value between 0 and 1 specifying the level to use for computing confidence intervals.
`breaks`	a value or string used to draw the histogram. See the help for `hist` for more details.
`add.stats`	a logical value indicating whether statistics and capability indices should be added at the bottom of the chart.
`print`	a logical value indicating whether statistics and capability indices should be printed.
`digits`	the number of significant digits to use.
`restore.par`	a logical value indicating whether the previous `par` settings must be restored. If you need to add points, lines, etc. to a chart set this to `FALSE`.

Details

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 that the target is midway between the specification limits.

Value

Invisibly returns a list with components:

`nobs`	number of obserations
`center`	center
`std.dev`	standard deviation
`target`	target
`spec.limits`	a vector of values giving the lower specification limit (LSL) and the upper specification limit (USL)
`indices`	a matrix of capability indices (C_p, C_pl, C_pu, C_pk, C_pm) and the corresponding confindence limits.
`exp`	a vector of values giving the expected fraction, based on a normal approximation, of the observations less than LSL and greater than USL.
`obs`	a vector of values giving the fraction of observations less than LSL and greater than USL.

Author(s)

Luca Scrucca luca@stat.unipg.it

References

Bissell, A.F. (1990) How reliable is your capability index?, Applied Statistics, 39, 331-340.
Boyles, R.A. (1991) The Taguchi capability index, Journal of Quality Technology, 23, 107-126.
Chou, Y., Owen D.B. and Borrego S.A. (1990) Lower Confidence Limits on Process Capability Indices, Journal of Quality Technology, 22, 223-229.
Montgomery, D.C. (2005) Introduction to Statistical Quality Control, 5th ed. New York: John Wiley & Sons.
Wetherill, G.B. and Brown, D.W. (1991) Statistical Process Control. New York: Chapman & Hall.

Examples

data(pistonrings)
attach(pistonrings)
diameter <- qcc.groups(diameter, sample)
q <- qcc(diameter[1:25,], type="xbar", nsigmas=3, plot=FALSE)
process.capability(q, spec.limits=c(73.95,74.05))
process.capability(q, spec.limits=c(73.95,74.05), target=74.02)
process.capability(q, spec.limits=c(73.99,74.01))
process.capability(q, spec.limits = c(73.99, 74.1))