mshewhart {dfphase1} | R Documentation |
Multivariate Shewhart-type control charts
Description
mshewhart
computes, and, optionally, plots,
several Shewhart-type Phase I control charts for detecting
location and scale changes in multivariate subgrouped data.
mshewhart.normal.limits
pre-computes
the corresponding control limits when the in-control distribution is
multivariate normal.
Usage
mshewhart(x, subset, stat = c("T2Var", "T2", "Var", "Depth Ranks"), score = c("Identity",
"Signed Ranks", "Spatial Signs", "Spatial Ranks", "Marginal Ranks"),
loc.scatter = c("Classic", "Robust"), plot = TRUE, FAP = 0.05,
seed = 11642257, L = 1000, limits = NA)
mshewhart.normal.limits(p, n, m, stat = c("T2Var", "T2", "Var", "Depth Ranks"),
score = c("Identity", "Signed Ranks", "Spatial Signs", "Spatial Ranks",
"Marginal Ranks"), loc.scatter = c("Classic", "Robust"),
FAP = 0.05, seed = 11642257, L = 100000)
Arguments
x |
a pxnxm data numeric array (n observations gathered at m time points on p variables). |
p |
integer: number of monitored variables. |
n |
integer: size of each subgroup (number of observations gathered at each time point). |
m |
integer: number of subgroups (time points). |
subset |
an optional vector specifying a subset of subgroups/time points to be used |
stat |
character: control statistic[s] to use; see Details. |
score |
character: transformation to use; unused when
|
loc.scatter |
character: estimates of the multivariate location and scatter
to use when no preliminary rank transformation is applied.
Unused when |
plot |
logical; if |
FAP |
numeric (between 0 and 1): desired false alarm probability. |
seed |
positive integer; if not |
L |
positive integer: number of Monte Carlo replications used to
compute the control limits. Unused by |
limits |
numeric: pre-computed vector of control limits.
This vector should contain |
Details
The implemented control statistics are
T2Var
: combination of theT2
andVar
statistics described below.T2
: Hotelling'sT^2
control statistics (see Montgomery, 2009, equation 11.19, or Qiu, 2013, equation 7.7) with control limit equal toA
.Var
: normal likelihood ratio control statistics for detecting changes in the multivariate dispersion (see Montgomery, 2009, equation 11.34), with control limit equal toB
.Depth Ranks
: control statistics based on the rank of the Mahalanobis depths, proposed by Bell et. al.. As suggested Bell et al., the Mahalanobis depths are computed using the BACON estimates of the multivariate mean vector and the mean of the subgroups sample covariance matrices. An alarm is signalled if any of the statistics is greater than a positive control limitC
.
The T2
and Var
control statistics are computed
score=Identical:
from the original data standardized using either the classical pooled estimates of the mean vector and dispersion matrix (Montgomery, 2009, equations 11.14–11.18; Qiu, 2013, equations at page 269) or the highly robust minimum covariance determinant (MCD) estimate when argumentloc.scatter
is equal toClassic
orRobust
, respectively.score=Signed Ranks, Spatial Signs, Spatial Ranks, Marginal Ranks
: from a “rank” transformation of the original data. In particular, see Hallin and Paindaveine (2005) for the definition of the multivariate signed ranks and Oja (2010) for those of the spatial signs, spatial ranks, and marginal ranks. Multivariate signed ranks, spatial signs and ranks are “inner” standardized while marginal ranks are “outer” standardized (see Oja (2010) for the definition of “inner” and “outer” standardization). Whenloc.scatter
is equal toClassic
, inner standardization takes into account the subgroup structure of the data imposing that the average of the within-group covariances of the transformed data is proportional to the identity matrix. Otherwise, i.e., whenloc.scatter
is equal toRobust
, it is based on a standard Hettmansperger-Randles-like scatter estimate. Note that theT^2
control statistics based on the spatial signs corresponds to the control charts suggested by Cheng and Shiau (2015) whenloc.scatter
is equal toRobust
.
Value
mshewhart
returns an invisible list with elements:
T2 |
|
Var |
|
DepthRanks |
control statistic based on the rank of
the Mahalanobis depths; this element is present only if
|
center , scatter |
estimates of the multivariate location and scatter used to standardized the observations. |
limits |
control limits. |
stat , score , loc.scatter , FAP , L , seed |
input arguments. |
mshewhart.normal.limits
returns a numeric vector
containing the control limits.
Note
-
When
limits
isNA
,mshewhart
computes the control limits by permutation. Then, the resulting control chart is distribution-free. -
Pre-computed limits, such as those computed by using
mshewhart.normal.limits
, are not recommended. Indeed, the resulting control chart will not be distribution-free. -
However, when
score
isSigned Ranks
,Spatial Signs
,Spatial Ranks
orstat
isDepth Ranks
, the computed control limits are distribution-free in the class of all multivariate elliptical distributions.
Author(s)
Giovanna Capizzi and Guido Masarotto.
References
R. C. Bell, L. A. Jones-Farmer, N. Billor (2014) “A distribution-free multivariate Phase I location control chart for subgrouped data from elliptical distributions”. Technometrics, 56, pp. 528–538, doi:10.1080/00401706.2013.879264.
C. R. Cheng, J. J. H. Shiau JJH (2015) “A distribution-free multivariate control chart for Phase I applications”. Quality and Reliability Engineering International, 31, pp. 97–111, doi:10.1002/qre.1751.
M. Hallin and D. Paindaveine (2005) “Affine-Invariant Aligned Rank Tests for the Multivariate General Linear Model with VARMA Errors”. Journal of Multivariate Analysis, 93, pp. 122–163, doi:10.1016/j.jmva.2004.01.005.
D. C. Montgomery (2009) Introduction to Statistical Quality Control, 6th edn. Wiley.
H. Oja (2010) Multivariate Nonparametric Methods with R. An Approach Based on Spatial Signs and Ranks. Springer.
P. Qiu (2013) Introduction to Statistical Process Control. Chapman & Hall/CRC Press.
Examples
data(ryan)
mshewhart(ryan)
mshewhart(ryan,subset=-10)
mshewhart(ryan,subset=-c(10,20))
mshewhart(ryan,score="Signed Ranks")
mshewhart(ryan,subset=-10,score="Signed Ranks")
mshewhart(ryan,subset=-c(10,20),score="Signed Ranks")