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Function to plot mewma chart

Source: R/mqcs.mewma.r
mqcs.mewma.Rd

This function is used to compute statistics required by the Multivariate Exponentially Weighted Moving Average (MEWMA) chart.

Usage

mqcs.mewma(x, ...)

# S3 method for default
mqcs.mewma(
  x,
  data.name = NULL,
  limits = NULL,
  Xmv = NULL,
  S = NULL,
  method = "sw",
  plot = FALSE,
  ...
)

# S3 method for mqcd
mqcs.mewma(
  x,
  limits = NULL,
  Xmv = NULL,
  S = NULL,
  lambda = 0.1,
  method = "sw",
  plot = FALSE,
  ...
)

Arguments

x

An object of class 'mqcd'

...

Arguments passed to or from methods.

data.name

A string that specifies the title displayed on the plots. If not provided it is taken from the name of the object x.

limits

A two-values vector specifying the control limits.

Xmv

The mean vector. It is only specified for Phase II or when the parameters of the distribution are known.

S

The sample covariance matrix. It is only used for Phase II or when the parameters of the distribution are known.

method

The method employed to compute the covariance matrix in the individual observation case. Two methods are used "sw" for compute according to (Sullivan,Woodall 1996a) and "hm" by (Holmes,Mergen 1993)

plot

Logical value. If TRUE a mewma chart should be plotted.

lambda

The smoothing constant. Only values of 0.1, 0.2,...,0.9 are allowed.

See also

mqcd, mqcs

Author

Edgar Santos-Fernandez

Examples


##
##  Continuous data 
##
library(qcr)
data(dowel1)
str(dowel1)
#> 'data.frame':	40 obs. of  2 variables:
#>  $ diameter: num  0.492 0.501 0.491 0.492 0.505 0.5 0.497 0.509 0.49 0.499 ...
#>  $ length  : num  0.988 1.011 1.008 0.97 1.003 ...
data.mqcd <- mqcd(dowel1)
res.mqcs <- mqcs.mewma(data.mqcd)
summary(res.mqcs)
#> 
#> Summary of group statistics:
#>        V1         
#>  Min.   :0.04165  
#>  1st Qu.:0.95586  
#>  Median :1.69276  
#>  Mean   :1.62204  
#>  3rd Qu.:1.89606  
#>  Max.   :4.17595  
#> 
#> Number of quality characteristics:  2
#> Number of samples or observations:  40
#> Number of observations or sample size:  1
#> 
#> Mean Vector: 
#>  0.500875 1.001825
#> Covariance Matrix:
#>          diameter       length
#> [1,] 4.908654e-05 8.584936e-05
#> [2,] 8.584936e-05 4.199429e-04
#> 
#> Control limits: 
#>    lcl    ucl 
#> 0.0000 8.6336 
#> 
#> Number beyond limits: 0 
plot(res.mqcs, title =" MEWMA Control Chart for dowel1")