plot_mon {funcharts} | R Documentation |
Plot multivariate functional object over the training data set
Description
This function plots selected functions in a phase_II monitoring data set against the corresponding training data set to be compared.
Usage
plot_mon(cclist, fd_train, fd_test, plot_title = FALSE, print_id = FALSE)
Arguments
cclist |
A |
fd_train |
An object of class |
fd_test |
An object of class |
plot_title |
A logical value. If |
print_id |
A logical value. If |
Value
A ggplot of the multivariate functional data.
In particular, the multivariate functional data given in
fd_train
are plotted on
the background in gray, while the multivariate functional data given in
fd_test
are
plotted on the foreground, the colour
of each curve is black or red depending on if that curve
was signal as anomalous by at least a contribution plot.
Examples
library(funcharts)
data("air")
air <- lapply(air, function(x) x[201:300, , drop = FALSE])
fun_covariates <- c("CO", "temperature")
mfdobj_x <- get_mfd_list(air[fun_covariates],
n_basis = 15,
lambda = 1e-2)
y <- rowMeans(air$NO2)
y1 <- y[1:60]
y_tuning <- y[61:90]
y2 <- y[91:100]
mfdobj_x1 <- mfdobj_x[1:60]
mfdobj_x_tuning <- mfdobj_x[61:90]
mfdobj_x2 <- mfdobj_x[91:100]
mod <- sof_pc(y1, mfdobj_x1)
cclist <- regr_cc_sof(object = mod,
y_new = y2,
mfdobj_x_new = mfdobj_x2,
y_tuning = y_tuning,
mfdobj_x_tuning = mfdobj_x_tuning,
include_covariates = TRUE)
get_ooc(cclist)
cont_plot(cclist, 3)
plot_mon(cclist, fd_train = mfdobj_x1, fd_test = mfdobj_x2[3])