qcs.cusum | R Documentation |
This function is used to compute statistics required by the cusum chart.
qcs.cusum(x, ...)
## Default S3 method:
qcs.cusum(
x,
var.index = 1,
sample.index = 2,
covar.index = NULL,
covar.names = NULL,
data.name = NULL,
sizes = NULL,
center = NULL,
std.dev = NULL,
decision.interval = 5,
se.shift = 1,
plot = FALSE,
...
)
## S3 method for class 'qcd'
qcs.cusum(
x,
center = NULL,
std.dev = NULL,
decision.interval = 5,
se.shift = 1,
plot = FALSE,
...
)
x |
An object of class "qcd". |
... |
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 fo 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 indicating the 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 |
A value or a vector of values specifying the sample sizes associated with each group. |
center |
A value specifying the center of group statistics or the ”target” value of the process. |
std.dev |
A value or an available method specifying the within-group
standard deviation(s) of the process. |
decision.interval |
A numeric value specifying the number of standard errors of the summary statistics at which the cumulative sum is out of control. |
se.shift |
The amount of shift to detect in the process, measured in standard errors of the summary statistics. |
plot |
Logical value. If |
library(qcr)
data(pistonrings)
attach(pistonrings)
res.qcd <- qcd(pistonrings, type.data = "dependence")
res.qcs <- qcs.cusum(res.qcd, type = "cusum")
summary(res.qcs)
plot(res.qcs)
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