cumulative-impact-part-ii-graphics.R

library(ggplot2)

# Figure 1
plot_df <- rbind(
  data.frame(day = days_grid, level = phi, type = "true \u03D5(t)"),
  data.frame(day = days_grid, level = eta_star, type = "best fit \u03B7*(t)")
)

ggplot(plot_df, aes(x = day, y = level, color = type, linetype = type)) +
  geom_line(size = 2) +
  ggtitle("Total system impulse response for H.T.'s training") +
  xlab("Day (n)") + ylab("Lag distribution value") +
  theme(text = element_text(size = 16))

# Figure 2
plot_df <- rbind(
  data.frame(day = days_grid, level = phi, type =  "true \u03D5(t)"),
  data.frame(day = days_grid, level = eta_hat, type = "fitted \u03B7(t)")
)

ggplot(plot_df, aes(x = day, y = level, color = type, linetype = type)) +
  geom_line(size = 2) +
  ggtitle("Spline-based estimation of impulse response \u03D5(t)") +
  xlab("Day (n)") + ylab("Lag distribution value") +
  theme(text = element_text(size = 16))

# Figure 3
ggplot(train_aug_df) +
  geom_point(aes(x = day, y = perf)) +
  geom_line(aes(x = day, y = perf_hat_fitness_fatigue, color = "blue"),
            size = .9) +
  geom_line(aes(x = day, y = perf_hat, color = "dark orange"), size = .9) +
  ggtitle("Performance, observed and modeled") +
  xlab("Day (n)") + ylab("Performance") +
  theme(text = element_text(size = 16)) +
  scale_colour_manual(name = "model",
    values =c("blue" = "blue","dark orange" = "dark orange"),
    labels = c("fitness-fatigue", "spline-based"))