Monitors a functional response after adjusting for known scalar covariates using function-on-scalar regression (FOSR). The residuals are then monitored via FPCA-based T-squared and SPE statistics.
Usage
frcc.phase1(
fdataobj,
predictors,
ncomp = 5,
fosr.lambda = 1e-04,
alpha = 0.05,
tuning.fraction = 0.5,
seed = 42
)Arguments
- fdataobj
An object of class
fdata(functional response).- predictors
A matrix of scalar predictors (n x p).
- ncomp
Number of principal components for residual FPCA (default 5).
- fosr.lambda
FOSR smoothing parameter (default 1e-4).
- alpha
Significance level (default 0.05).
- tuning.fraction
Fraction of data for tuning (default 0.5).
- seed
Random seed (default 42).
Value
An object of class frcc.chart with components:
- eigenvalues
Eigenvalues from residual FPCA
- t2.ucl
T-squared control limit
- spe.ucl
SPE control limit
- ncomp
Number of components used
- fdataobj
Original fdata object
- predictors
Original predictor matrix
- .rust
Internal fields for Phase II monitoring
See also
frcc.monitor for Phase II monitoring,
spm.phase1 for monitoring without covariates
Examples
# \donttest{
set.seed(1)
n <- 60; 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 <- frcc.phase1(fd, X_pred, ncomp = 3)
chart
#> Functional Regression Control Chart (Phase I)
#> Components: 3
#> Alpha: 0.05
#> T2 UCL: 7.815
#> SPE UCL: 1.075
#> Observations: 60
#> Predictors: 2
# }