`R/real_time_00_mfd.R`

`get_mfd_df_real_time.Rd`

This function produces a list functional data objects, each evolving up to an intermediate domain point, that can be used to estimate models that allow real-time predictions of incomplete functions, from the current functional domain up to the end of the observation, and to build control charts for real-time monitoring.

It calls the function `get_mfd_df`

for each domain point.

- dt
See

`get_mfd_df`

.- domain
See

`get_mfd_df`

.- arg
See

`get_mfd_df`

.- id
See

`get_mfd_df`

.- variables
See

`get_mfd_df`

.- n_basis
See

`get_mfd_df`

.- n_order
See

`get_mfd_df`

.- basisobj
See

`get_mfd_df`

.- Lfdobj
See

`get_mfd_df`

.- lambda
See

`get_mfd_df`

.- lambda_grid
See

`get_mfd_df`

.- k_seq
A vector of values between 0 and 1, containing the domain points over which functional data are to be evaluated in real time. If the domain is the interval (a,b), for each instant k in the sequence, functions are evaluated in (a,k(b-a)).

- ncores
If you want parallelization, give the number of cores/threads to be used when creating mfd objects separately for different instants.

A list of `mfd`

objects as produced by
`get_mfd_df`

,
corresponding to a given instant.

```
library(funcharts)
x <- seq(1, 10, length = 25)
y11 <- cos(x)
y21 <- cos(2 * x)
y12 <- sin(x)
y22 <- sin(2 * x)
df <- data.frame(id = factor(rep(1:2, each = length(x))),
x = rep(x, times = 2),
y1 = c(y11, y21),
y2 = c(y12, y22))
mfdobj_list <- get_mfd_df_real_time(dt = df,
domain = c(1, 10),
arg = "x",
id = "id",
variables = c("y1", "y2"),
lambda = 1e-2)
```