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Process capability indices for a given dataset and distribution

Source: R/qcs.pcr.r
qcs.pcr.Rd

Calculates the process capability indices cp, cpk, cpkL and cpkU for a given dataset and distribution. A histogram with a density curve is displayed along with the specification limits and a Quantile-Quantile Plot for the specified distribution.

Usage

qcs.pcr(
  object,
  distribution = c("normal", "beta", "chi-squared", "exponential", "f", "geometric",
    "lognormal", "log-normal", "logistic", "t", "negative binomial", "poisson",
    "weibull", "gamma"),
  limits = c(lsl = -3, usl = 3),
  target = NULL,
  std.dev = NULL,
  boxcox = FALSE,
  lambda = c(-5, 5),
  confidence = 0.9973,
  plot = TRUE,
  main = NULL,
  ...
)

Arguments

object

qcs object of type "qcs.xbar" or "qcs.one".

distribution

Character string that represent the probability distribution of the data, such as: "normal", "beta", "chi-squared", "exponential", "f", "geometric", "lognormal", "log-normal", "logistic","t", "negative binomial", "poisson", "weibull", "gamma".

limits

A vector specifying the lower and upper specification limits.

target

A value specifying the target of the process. If it is NULL, the target is set at the middle value between specification limits.

std.dev

A value specifying the within-group standard deviation.

boxcox

Logical value (by default FALSE). If TRUE, perform a Box-Cox transformation.

lambda

A vector specifying or numeric value indicating lambda for the transformation.

confidence

A numeric value between 0 and 1 specifying the nivel for computing the specification limits.

plot

Logical value indicating whether graph should be plotted.

main

Title of the plot.

...

Arguments to be passed to or from methods.

References

Montgomery, D.C. (1991) Introduction to Statistical Quality Control, 2nd ed, New York, John Wiley & Sons.

Examples

library(qcr)
data(pistonrings) 
xbar <- qcs.xbar(pistonrings[1:125,],plot = TRUE)

limits = c(lsl = 73.99, usl = 74.01)
qcs.pcr(xbar, "normal", limits = limits) 
#> 
#> Process Capability Analysis
#> 
#> Call:
#> qcs.pcr(object = xbar, distribution = "normal", limits = limits)
#> 
#> Number of obs = 125          Target = 74
#>        Center =  74               LSL =  73.99
#>        StdDev =  0.009785         USL =  74.01
#> 
#> Capability indices:
#> 
#>        Value
#> Cp    0.3407
#> Cp_l  0.3807
#> Cp_u  0.3006
#> Cp_k  0.3006
#> 
#> 
#> PPM:
#> 
#>          Exp<LSL 1.267e+05       Obs<LSL 0
#>          Exp>USL 1.836e+05       Obs>USL 1e+12
#>        Exp Total 3.103e+05     Obs Total 1e+12
#> 
#> Test:
#> 
#> 
#> 	Anderson Darling Test for normal distribution
#> 
#> data:  x 
#> Error in round(x$statistic, 4): non-numeric argument to mathematical function

qcs.pcr(xbar, "weibull", limits = limits)
#> 
#> Process Capability Analysis
#> 
#> Call:
#> qcs.pcr(object = xbar, distribution = "weibull", limits = limits)
#> 
#> Number of obs = 125          Target = 74
#>        Center =  74               LSL =  73.99
#>        StdDev =  0.009785         USL =  74.01
#> 
#> Capability indices:
#> 
#>        Value
#> Cp    0.5801
#> Cp_l  0.4861
#> Cp_u  0.8402
#> Cp_k  0.4861
#> 
#> 
#> PPM:
#> 
#>          Exp<LSL 3.283e+04       Obs<LSL 0
#>          Exp>USL 9965            Obs>USL 1e+12
#>        Exp Total 4.279e+04     Obs Total 1e+12
#> 
#> Test:
#> 
#> 
#> 	Anderson Darling Test for weibull distribution
#> 
#> data:  x 
#> Error in round(x$statistic, 4): non-numeric argument to mathematical function