Simulation_study_monitoring_scheme_run_length_plot_results.qmd

---
title: "Covariate-Adjusted Functional Data Analysis for Structural Health Monitoring"
subtitle: "Simulations results monitoring scheme"
        
# Freeze computed outputs
freeze: true

# Enable banner style title blocks
title-block-banner: true

# Enable CC licence appendix
license: "CC BY"

# Default for table of contents
toc: true
toc-title: Table of contents
toc-location: left

# Default knitr options
execute:
  echo: true
  message: true
  warning: true
cache: false

date: "10/07/2024" 
format:
  html:
    embed-resources: false
    code-fold: false
    code-summary: "Code"
    code-tools:
      source: true
      toggle: true
      caption: none

knitr:
  opts_chunk:
    dev:
      - png
      - pdf
      
editor: source

author:
  - name: Philipp Wittenberg
    corresponding: true
    id: pw
    orcid: 0000-0001-7151-8243
    email: pwitten@hsu-hh.de
    affiliation: 
      - name: Helmut Schmidt University
        city: Hamburg
        country: Germany
        url: www.hsu-hh.de
  - name: Lizzie Neumann
    id: ln
    orcid: 0000-0003-2256-1127
    email: neumannl@hsu-hh.de
    affiliation: 
      - name: Helmut Schmidt University
        city: Hamburg
        country: Germany
        url: www.hsu-hh.de
  - name: Alexander Mendler
    id: ln
    orcid: 0000-0002-7492-6194
    email: alexander.mendler@tum.de
    affiliation: 
      - name: Technical University of Munich
        city: Hamburg
        country: Germany
        url: www.hsu-hh.de
  - name: Jan Gertheiss
    id: jg
    orcid: 0000-0001-6777-4746
    email: gertheij@hsu-hh.de
    affiliation: 
      - name: Helmut Schmidt University
        city: Hamburg
        country: Germany
        url: www.hsu-hh.de        
        
        

citation: 
  container-title: arXiv:2408.02106
  doi: 10.48550/arXiv.2408.02106
  
bibliography:
  - literature.bib
link-citations: true

output: github_document
---

```{r}
start_time <- Sys.time()
```

Load libraries

```{r load_libraries_and_data, message=FALSE}
# Package names
packages <- c("benchmarkme", "dplyr", "forcats", "funData", "ggplot2", 
              "patchwork", "tidyr")

# Install packages not yet installed
installed_packages <- packages %in% rownames(installed.packages())
if (any(installed_packages == FALSE)) {
  install.packages(packages[!installed_packages])
}

# Packages loading
invisible(lapply(packages, library, character.only = TRUE))

theme_set(theme_bw() +
            theme(panel.grid.major=element_blank(),
                  panel.grid.minor=element_blank(),
                  strip.background=element_rect(fill="white"),
                  strip.text=element_text(size=10, face="bold"),
                  plot.title=element_text(hjust=.5),
                  axis.text.x = element_text(color="black"),
                  axis.text.y = element_text(color="black"),
                  axis.ticks = element_line(color = "black")
            )
          )

mcb <- c("#D55E00", "#56B4E9", "#E69F00", "#009E73", "#CC79A7")
LABS <- lapply(c(0.1, 0.3, 1.0), function(i) bquote(lambda==.(i)))
std_shift <- function(i) sqrt(exp(-(i+1)/2))
```

```{r}
RUNS <- 1:100
DIR <- "data/simulations/"
df1 <- purrr::map_df(paste0(DIR,"MEWMA_ARL_r_", 0.1 , 
                            "_N_300_RUN_", RUNS, ".rds"), readRDS) |>
  group_by(EFI, delta_vec) |>
  mutate(AARL=mean(ARL), stdARL=sd(ARL), EFI=factor(EFI)) |>
  ungroup()

df2 <- purrr::map_df(paste0(DIR,"MEWMA_ARL_r_", 0.3 , 
                            "_N_300_RUN_", RUNS, ".rds"), readRDS) |>
  group_by(EFI, delta_vec) |>
  mutate(AARL=mean(ARL), stdARL=sd(ARL), EFI=factor(EFI)) |>
  ungroup()

df3 <- purrr::map_df(paste0(DIR,"MEWMA_ARL_r_", 1 , 
                            "_N_300_RUN_", RUNS, ".rds"), readRDS) |>
  group_by(EFI, delta_vec) |>
  mutate(AARL=mean(ARL), stdARL=sd(ARL), EFI=factor(EFI)) |>
  ungroup()
```

```{r}
bind_rows(df1, df2, df3, .id="KAPPA") |> 
  select(ARLSE) |>
  filter(ARLSE==max(ARLSE))
```

```{r MEWMA_simulation_plots_normal_shift, fig.width=8.5, fig.height=3}
p1 <- bind_rows(df1, df2, df3, .id="LAMBDA") |>
  mutate(LAMBDA=factor(LAMBDA, labels=LABS)) |>
  ggplot(aes(x=delta_vec, y=AARL, group=LAMBDA, colour=LAMBDA, fill=LAMBDA)) +
    geom_line() +
    geom_ribbon(aes(ymin=AARL-stdARL, ymax=AARL+stdARL), alpha=.2, colour=NA) +
    labs(y="ARL approximation", x=bquote(Shift~size~delta)) +
    scale_y_log10(breaks=c(1, 10, 50, 100)) +
    facet_wrap(~EFI, labeller=label_bquote(cols=FPC~.(EFI))) +
    scale_colour_manual(values = mcb[c(2,3,5)], labels=LABS) +
    scale_fill_manual(values = mcb[c(2,3,5)], labels=LABS) +
    theme(legend.position="inside", legend.position.inside=c(.9,.7), legend.title=element_blank())
```

```{r MEWMA_simulation_plots_standardized_shift, fig.width=8.5, fig.height=3}
p2 <- bind_rows(df1, df2, df3, .id="LAMBDA") |>
  mutate(LAMBDA=factor(LAMBDA, labels=LABS), 
         std_shift=delta_vec/std_shift(as.numeric(EFI))) |>
  ggplot(aes(x=std_shift, y=AARL, group=LAMBDA, colour=LAMBDA, fill=LAMBDA)) +
    geom_line() +
    geom_ribbon(aes(ymin=AARL-stdARL, ymax=AARL+stdARL), alpha=.2, colour=NA) +
    labs(y="ARL approximation", 
         x=bquote(Standardized~shift~size~frac(delta, sqrt(nu[r])))) +
    scale_y_log10(breaks=c(1, 10, 50, 100)) +
    scale_x_continuous(breaks=c(1:11)) +
    facet_wrap(~EFI, labeller=label_bquote(cols=FPC~.(EFI))) +
    scale_colour_manual(values = mcb[c(2,3,5)], labels=LABS) +
    scale_fill_manual(values = mcb[c(2,3,5)], labels=LABS) +
    theme(legend.position="none", legend.title=element_blank())
```

```{r Figure_5_MEWMA_simulation_plots_combined, fig.width=8.5, fig.height=6}
p1 + p2 + plot_layout(ncol=1)
```

## Computational and session information

Hardware information:

```{r extract_hardware_specification}
get_cpu()
get_ram()
```

Computation time:

```{r computational_time}
print(paste("Computation time:", round(Sys.time() - start_time, 2)[[1]], "seconds"))
```
Session information:

```{r session_info}
sessionInfo()
```