Plot multivariate functional object over the training data set

This function plots selected functions in a phase_II monitoring data set against the corresponding training data set to be compared.

plot_mon(cclist, fd_train, fd_test, plot_title = FALSE, print_id = FALSE)

Arguments

cclist

A data.frame produced by control_charts_pca, control_charts_sof_pc regr_cc_fof, or regr_cc_sof.

fd_train

An object of class mfd containing the training data set of the functional variables. They are plotted in gray in the background.

fd_test

An object of class mfd containing the phase II data set of the functional variables to be monitored. They are coloured in black or red on the foreground.

plot_title

A logical value. If TRUE, it prints the title with the observation name. Default is FALSE.

print_id

A logical value. If TRUE, and also plot_title is TRUE, it prints also the id of the observation in the title of the ggplot. Default is FALSE

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)
#>      id    T2   spe     y contribution_T2_CO contribution_T2_temperature
#> 91   91 FALSE FALSE FALSE              FALSE                       FALSE
#> 92   92 FALSE FALSE FALSE              FALSE                       FALSE
#> 93   93  TRUE FALSE FALSE               TRUE                       FALSE
#> 94   94 FALSE FALSE FALSE              FALSE                       FALSE
#> 95   95 FALSE FALSE FALSE              FALSE                       FALSE
#> 96   96 FALSE FALSE FALSE              FALSE                       FALSE
#> 97   97 FALSE FALSE FALSE              FALSE                       FALSE
#> 98   98 FALSE FALSE FALSE              FALSE                       FALSE
#> 99   99 FALSE FALSE  TRUE              FALSE                       FALSE
#> 100 100 FALSE FALSE  TRUE              FALSE                       FALSE
#>     contribution_spe_CO contribution_spe_temperature
#> 91                FALSE                        FALSE
#> 92                FALSE                        FALSE
#> 93                FALSE                        FALSE
#> 94                FALSE                        FALSE
#> 95                FALSE                        FALSE
#> 96                FALSE                        FALSE
#> 97                FALSE                        FALSE
#> 98                FALSE                        FALSE
#> 99                FALSE                        FALSE
#> 100               FALSE                        FALSE
cont_plot(cclist, 3)

plot_mon(cclist, fd_train = mfdobj_x1, fd_test = mfdobj_x2[3])