Code Block 4. Closing the loop.

cumulative-impact-part-ii-close-the-loop.R

get_eta_hat <- function(t_seq) {
  spline_vars_grid <- predict(my_spline, newx = t_seq)
  spline_vars_grid <- cbind(1, spline_vars_grid)
  eta_hat <- spline_vars_grid %*% coef(spline_reg)[-1]
  as.numeric(eta_hat)
}

convolve_with_fn <- function(training, n, impulse_fn) {
  sum(training[1:(n - 1)] * impulse_fn((n - 1):1))
}

cumulative_impact_eta <- sapply(1:nrow(train_df),
                                function(n) convolve_with_fn(train_df$w, n,
                                                             get_eta_hat))
cumulative_impact_phi <- sapply(1:nrow(train_df),
                                function(n) convolve_with_fn(train_df$w, n,
                                                             get_true_phi))

train_aug_df$perf_hat_convo_eta <- coef(spline_reg)[1] + cumulative_impact_eta
train_aug_df$perf_hat_fitness_fatigue <- 496 + cumulative_impact_phi

# Removing end effects, convolving with spline is same as regression fitted vals
all(abs(train_aug_df$perf_hat_convo_eta  - train_aug_df$perf_hat)[-1] < .001)

days_grid <- 1:259
eta_hat <- get_eta_hat(days_grid)
phi <- get_true_phi(days_grid)