Get possible outliers of a training data set of a scalar-on-function regression model.
Get possible outliers of a training data set of a scalar-on-function regression model. It sets the training data set also as tuning data set for the calculation of control chart limits, and as phase II data set to compare monitoring statistics against the limits and identify possible outliers. This is only an empirical approach. It is advised to use methods appropriately designed for phase I monitoring to identify outliers.
get_sof_pc_outliers(y, mfdobj)
y |
A numeric vector containing the observations of the scalar response variable. |
mfdobj |
A multivariate functional data object of class mfd denoting the functional covariates. |
A character vector with the ids of functional observations signaled as possibly anomalous.
library(funcharts)
data("air")
air <- lapply(air, function(x) x[1:10, , drop = FALSE])
fun_covariates <- c("CO", "temperature")
mfdobj_x <- get_mfd_list(air[fun_covariates], lambda = 1e-2)
y <- rowMeans(air$NO2)
get_sof_pc_outliers(y, mfdobj_x)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.