sof_pc_real_time: Get a list of scalar-on-function linear regression models estimated on functional data each evolving up to an intermediate domain point.

Description

This function produces a list of objects, each of them contains the result of applying sof_pc to a scalar response variable and multivariate functional covariates evolved up to an intermediate domain point. See Capezza et al. (2020) for additional details on real-time monitoring.

Usage

sof_pc_real_time(
  y,
  mfd_real_time_list,
  single_min_variance_explained = 0,
  tot_variance_explained = 0.9,
  selection = "PRESS",
  components = NULL,
  ncores = 1
)

Value

A list of lists each produced by sof_pc, corresponding to a given instant.

Arguments

y: A numeric vector containing the observations of the scalar response variable.
mfd_real_time_list: A list created using get_mfd_df_real_time or get_mfd_list_real_time, denoting a list of functional data objects, each evolving up to an intermediate domain point, with observations of the multivariate functional covariates.
single_min_variance_explained: See sof_pc.
tot_variance_explained: See sof_pc.
selection: See sof_pc.
components: See sof_pc.
ncores: If you want parallelization, give the number of cores/threads to be used when creating objects separately for different instants.

Author

C. Capezza

References

Capezza C, Lepore A, Menafoglio A, Palumbo B, Vantini S. (2020) Control charts for monitoring ship operating conditions and CO2 emissions based on scalar-on-function regression. Applied Stochastic Models in Business and Industry, 36(3):477--500. doi:10.1002/asmb.2507

Examples

Run this code

library(funcharts)
data("air")
air <- lapply(air, function(x) x[1:10, , drop = FALSE])
mfdobj_list <- get_mfd_list_real_time(air[c("CO", "temperature")],
                                      n_basis = 15,
                                      lambda = 1e-2,
                                      k_seq = c(0.5, 0.75, 1))
y <- rowMeans(air$NO2)
mod_list <- sof_pc_real_time(y, mfdobj_list)

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