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Monitor New Data Against Profile Chart (Phase II)

Source: R/spm.R
spm.profile.monitor.Rd

Projects new functional data and scalar predictors through a trained profile monitoring chart. Computes sliding-window FOSR betas, projects onto the reference beta FPCA, and evaluates T-squared statistics.

Usage

spm.profile.monitor(chart, newdata, new.predictors)

Arguments

chart

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

newdata

An object of class fdata with new functional response.

new.predictors

A matrix of new scalar predictors.

Value

An object of class spm.monitor with components:

betas

Matrix of estimated beta coefficients per window

t2

T-squared values for each window

t2.alarm

Logical: TRUE where T-squared exceeds UCL

beta.scores

Beta FPC scores for each window

t2.ucl

T-squared control limit

See also

spm.profile.phase1 for building the chart

Examples

# \donttest{
set.seed(1)
n <- 80; m <- 20
argvals <- seq(0, 1, length.out = m)
X_pred <- cbind(rnorm(n), rnorm(n))
Y <- matrix(rnorm(n * m), n, m)
fd <- fdata(Y, argvals = argvals)
chart <- spm.profile.phase1(fd, X_pred, ncomp = 2, window.size = 15)

# Monitor new data
X_new <- cbind(rnorm(30), rnorm(30))
Y_new <- matrix(rnorm(30 * m) + 1, 30, m)
fd_new <- fdata(Y_new, argvals = argvals)
mon <- spm.profile.monitor(chart, fd_new, X_new)
mon
#> SPM Monitoring Result (Phase II)
#>   Observations: 16 
#>   T2 alarms: 0 of 16 (0%) 
#>   SPE alarms: 0 of 16 (0%) 
#>   T2 UCL: 5.991 
#>   SPE UCL: NULL 
# }