This function is used to compute statistics required by the p chart.

1 2 3 4 5 6 7 8 9 10 11 | ```
qcs.p(x, ...)
## Default S3 method:
qcs.p(x, var.index = 1, sample.index = 2,
covar.index = NULL, covar.names = NULL, data.name = NULL,
sizes = NULL, center = NULL, conf.nsigma = 3, limits = NULL,
plot = FALSE, ...)
## S3 method for class 'qcd'
qcs.p(x, center = NULL, conf.nsigma = 3, limits = NULL,
plot = FALSE, ...)
``` |

`x` |
an R object (used to select the method). See details. |

`...` |
arguments passed to or from methods. |

`var.index` |
a scalar with the column number corresponding to the observed data for the variable (the variable quality). Alternativelly can be a string with the name of the quality variable. |

`sample.index` |
a scalar with the column number corresponding to the index each group (sample). |

`covar.index` |
optional. A scalar or numeric vector with the column number(s) corresponding to the covariate(s). Alternativelly it can be a character vector with the names of the covariates. |

`covar.names` |
optional. A string or vector of strings with names for the covariate columns. Only valid if there is more than one column of data. By default, takes the names from the original object. |

`data.name` |
a string specifying the name of the variable which appears on the plots. If not provided it is taken from the object given as data. |

`sizes` |
optional. A value or a vector of values specifying the sample sizes
associated with each group. For continuous data the sample sizes are obtained counting the non- |

`center` |
a value specifying the center of group statistics or the ”target” value of the process. |

`conf.nsigma` |
a numeric value used to compute control limits, specifying the
number of standard deviations (if |

`limits` |
a two-values vector specifying control limits. |

`plot` |
a logical value indicating should be plotted. |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
library(qcr)
data(orangejuice)
str(orangejuice)
attach(orangejuice)
datos.qcd <- qcd(data = orangejuice, var.index = 1, sample.index = 2,
sizes = size, type.data = "atributte")
res.qcs <- qcs.p(datos.qcd)
summary(res.qcs)
plot(res.qcs)
datos.qcs <- qcs.p(orangejuice[trial,c(1,2)], sizes = orangejuice[trial,3])
plot(datos.qcs)
``` |

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