qcs.pcr {qcr} | R Documentation |
Process capability indices for a given dataset and distribution
Description
Calculates the process capability indices cp, cpk, cpkL and cpkU for a given dataset and distribution. A histogramm 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 |
distribution |
character string that representing the probability distribution 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 is |
std.dev |
A value specifying the within-group standard deviation. |
boxcox |
Logical value (default is 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)
qcs.pcr(xbar, "weibull", limits = limits)