R/hte_helpers.R

In evalHTE: Evaluating Heterogeneous Treatment Effects

# # function to call Julia and run optimization
# #' @import JuliaCall
# #' @importFrom JuliaCall julia_setup
# #' @importFrom JuliaCall julia_source
# #' @importFrom JuliaCall julia_eval
# run_optimization <- function() {
#   if (!check_julia_installation()) {
#     stop("Julia is not properly installed or configured. Please install the correct version of Julia and try again.")
#   }
  
#   # Set up Julia
#   julia <- julia_setup()
  
#   # Source the Julia file
#   JuliaCall::julia_source("optimization_code.jl")
  
#   # Run the optimization
#   result <- JuliaCall::julia_eval("run_optimization()")
  
#   # Extract and return results as a list
#   return(
#     list(
#       beta_0 = result[[1]],
#       beta_1 = result[[2]],
#       normal_law = result[[3]],
#       alphas = result[[4]]
#     )
#   )
# }

# # helper function to check if Julia is installed and working
# check_julia_installation <- function() {
#   tryCatch({
#     # Check if JuliaCall package is available
#     if (!requireNamespace("JuliaCall", quietly = TRUE)) {
#       stop("JuliaCall package is not installed. Please install it first.")
#     }

#     # Try to set up Julia
#     julia <- JuliaCall::julia_setup(verbose = TRUE)
    
#     # # Check Julia version
#     # version <- JuliaCall::julia_eval("VERSION")
#     # if (package_version(version) <= package_version("1.0.0")) {
#     #   stop("Julia version is too old. Please install Julia 1.0.0 or newer.")
#     # }
    
#     # If Julia is installed and working
#     return(TRUE)
#   }, error = function(e) {
#     message("Julia check failed: ", e$message)
#     return(FALSE)
#   })
# }

# helper function to plot the GATE estimates
gate_ggplot <- function(data) {
    ggplot(data, aes(
      x = .data$group, y = .data$estimate,
      ymin = .data$lower , ymax = .data$upper, color = .data$algorithm)) +
    ggdist::geom_pointinterval(
      width = 0.5,
      position = position_dodge(0.5),
      interval_size_range = c(0.8, 1.5),
      fatten_point = 2.5) +
    theme_bw() +
    # add color theme
    scale_color_manual(values = c(
      "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7", "#999999")) +
    theme(panel.grid = element_blank(),
          panel.background = element_blank()) +
    labs(x = "Group", y = "GATE estimate") +
    geom_hline(yintercept = 0, linetype = "dashed", color = "#4e4e4e")
}


# rename the columns of the data frame with the interaction terms
rename_interaction_terms <- function(interaction_df){
  colnames(interaction_df) <- gsub(":", "_", colnames(interaction_df))
  colnames(interaction_df) <- gsub("\\*", "_", colnames(interaction_df))
  colnames(interaction_df) <- gsub("\\(", "", colnames(interaction_df))
  colnames(interaction_df) <- gsub("\\)", "", colnames(interaction_df))
  colnames(interaction_df) <- gsub("\\+", "_", colnames(interaction_df))
  return(interaction_df)
}



# function to convert formula and create new variables
convert_formula <- function(user_formula, data, treatment){

  # get the outcome variable name from the formula
  outcome <- all.vars(user_formula)[1]

  # get the covariates from the formula
  interaction_df <- model.matrix(user_formula, data)
  interaction_df <- rename_interaction_terms(interaction_df)

  # remove variable Intercept from covariates list by name
  covariates_vec <- colnames(interaction_df)
  covariates_vec <- covariates_vec[!covariates_vec %in% c("Intercept", paste0(treatment))]
  # combine the interaction_df with the original data frame
  new_data = data %>% dplyr::select(all_of(outcome))
  combined_data <- cbind(new_data, interaction_df)

  return(list(data = combined_data, covariates = covariates_vec, outcome = outcome))
}