Description Usage Arguments Details Value Author(s) References Examples

Performs the multivariate process capability analysis using three component multivariate process capability vector (mpcv).

1 2 | ```
mpcv(x, indepvar = 1, LSL, USL, Target, alpha = 0.0027, distance, n.integr = 100,
coef.up, coef.lo)
``` |

`x` |
a numeric matrix containing the data (quality characteristics). |

`indepvar` |
a number or a name of the independent variable needed for building one-sided models. |

`LSL` |
a vector of lower specification limits defined for each variable. |

`USL` |
a vector of upper specification limits defined for each variable. |

`Target` |
a vector of target of the process defined for each variable. |

`alpha` |
the proportion of nonconforming products. |

`distance` |
the distance measure to be used for removing the nonconforming elements. This must be one of |

`n.integr` |
a number of integration intervals |

`coef.up` |
a vector of minimal values of leading coefficients for "upper" one-sided models. Value given for |

`coef.lo` |
a vector of minimal values of leading coefficients for "lower" one-sided models. Value for |

If the parameter `Target`

is not specified, then `Target <- LSL + (USL - LSL)/2`

.

Using the applied methodology, the shape of a process region is mainly defined by the leading coefficients of the models.
To obtain a certain shape of a process region (e.g. similar to the previous one) there is possible to specify minimal values of the leading coefficients `coef.lo`

and `coef.up`

of the models.
By default all the minimal values of the coefficients equal zero.

Except the `"mahalanobis"`

distance, the available distance measures are listed in dist.

An mpcv object. See `mpcv.object`

for details.

Krzysztof Ciupke, `<krzysztof.ciupke at polsl.pl>`

Ciupke K. (2014) *Multivariate Process Capability Vector Based on One-Sided Model*, Quality and Reliability Engineering International, John Wiley & Sons.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
data(industrial)
x <- industrial$x
LSL <- industrial$LSL
USL <- industrial$USL
Target<- industrial$Target
res.ind <- mpcv(x, LSL=LSL, USL=USL, Target=Target, alpha=0.025)
data(automotive)
x <- automotive$x
LSL <- automotive$LSL
USL <- automotive$USL
Target<- automotive$Target
res.aut <- mpcv(x, indepvar="T", LSL=LSL, USL=USL, Target=Target)
data(sleeves)
x <- sleeves$x
LSL <- sleeves$LSL
USL <- sleeves$USL
Target<- sleeves$Target
res.sle <- mpcv(x, indepvar=3, LSL=LSL, USL=USL, Target=Target, alpha=.02)
``` |

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