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MEWMA Monitoring for Functional Data

Source: R/spm.R
spm.mewma.Rd

Applies Multivariate Exponentially Weighted Moving Average (MEWMA) monitoring to sequential functional data. MEWMA smooths the FPC score vectors over time, then computes a Hotelling-type statistic on the smoothed scores.

Usage

spm.mewma(chart, newdata, lambda = 0.2, ncomp = NULL, alpha = 0.05)

Arguments

chart

An object of class spm.chart from spm.phase1.

newdata

An object of class fdata with sequential observations.

lambda

MEWMA smoothing parameter in (0, 1] (default 0.2).

ncomp

Number of components (default: same as chart).

alpha

Significance level (default 0.05).

Value

An object of class spm.mewma with components:

smoothed.scores

Matrix of MEWMA-smoothed score vectors

mewma.statistic

Numeric vector of MEWMA statistics

ucl

Control limit

alarm

Logical vector: TRUE where MEWMA exceeds UCL

spe

SPE values

spe.limit

SPE control limit

spe.alarm

Logical: TRUE where SPE exceeds limit

See also

spm.amewma for adaptive MEWMA, spm.ewma for univariate EWMA

Examples

# \donttest{
set.seed(1)
n <- 50; m <- 30
argvals <- seq(0, 1, length.out = m)
X <- matrix(rnorm(n * m), n, m)
fd <- fdata(X, argvals = argvals)
chart <- spm.phase1(fd, ncomp = 3)

X_new <- matrix(rnorm(20 * m) + 0.5, 20, m)
fd_new <- fdata(X_new, argvals = argvals)
mewma <- spm.mewma(chart, fd_new, lambda = 0.2)
mewma
#> SPM MEWMA Monitoring
#>   Observations: 20 
#>   Lambda: 0.2 
#>   UCL: 7.815 
#>   MEWMA alarms: 1 of 20 (5%) 
#>   SPE alarms: 1 of 20 (5%) 
# }