R/psm_functions.R

In pricesensitivitymeter: Van Westendorp Price Sensitivity Meter Analysis

#---------------------
# Implementing van Westendorp's PSM in R
#---------------------

psm_analysis <- function(toocheap, cheap, expensive, tooexpensive, data = NA,
                         validate = TRUE,
                         interpolate = FALSE, interpolation_steps = get_psm_constant("DEFAULT_INTERPOLATION_STEPS"),
                         intersection_method = "min",
                         acceptable_range = "original",
                         pi_cheap = NA, pi_expensive = NA,
                         pi_scale = get_psm_constant("NMS_DEFAULTS.PI_SCALE"),
                         pi_calibrated = get_psm_constant("NMS_DEFAULTS.PI_CALIBRATED"),
                         pi_calibrated_toocheap = get_psm_constant("NMS_DEFAULTS.PI_CALIBRATED_TOOCHEAP"),
                         pi_calibrated_tooexpensive = get_psm_constant("NMS_DEFAULTS.PI_CALIBRATED_TOOEXPENSIVE")) {

  # Step 1: Validate all inputs (centralized, consistent validation)
  validation_info <- validate_psm_inputs(
    toocheap = toocheap, cheap = cheap, expensive = expensive, tooexpensive = tooexpensive,
    data = data, validate = validate, interpolate = interpolate,
    interpolation_steps = interpolation_steps, intersection_method = intersection_method,
    acceptable_range = acceptable_range, pi_cheap = pi_cheap, pi_expensive = pi_expensive,
    pi_scale = pi_scale, pi_calibrated = pi_calibrated,
    pi_calibrated_toocheap = pi_calibrated_toocheap,
    pi_calibrated_tooexpensive = pi_calibrated_tooexpensive
  )
  
  # Step 2: Prepare and clean data (handles all data formats consistently)
  prepared_data <- prepare_psm_data(
    toocheap = toocheap, cheap = cheap, expensive = expensive, tooexpensive = tooexpensive,
    data = data, pi_cheap = pi_cheap, pi_expensive = pi_expensive,
    pi_scale = pi_scale, pi_calibrated = pi_calibrated, validate = validate
  )
  
  # Step 3: Calculate empirical cumulative distribution functions
  ecdf_data <- calculate_ecdf_data(
    psmdata = prepared_data$data,
    weighted = prepared_data$weighted,
    survey_design = prepared_data$survey_design,
    interpolate = interpolate,
    interpolation_steps = interpolation_steps
  )
  
  # Step 4: Identify key price points (IDP, OPP, price range)
  price_points <- identify_price_points(
    data_ecdf = ecdf_data,
    intersection_method = intersection_method,
    acceptable_range = acceptable_range
  )
  
  # Step 5: Calculate NMS extension if requested
  nms_results <- NULL
  if (prepared_data$nms_requested) {
    nms_results <- calculate_nms_analysis(
      prepared_data = prepared_data,
      data_ecdf = ecdf_data,
      pi_calibrated_toocheap = pi_calibrated_toocheap,
      pi_calibrated_tooexpensive = pi_calibrated_tooexpensive
    )
  }
  
  # Step 6: Construct final result object
  construct_psm_result(
    prepared_data = prepared_data,
    ecdf_data = ecdf_data,
    price_points = price_points,
    nms_results = nms_results,
    acceptable_range = acceptable_range,
    pi_scale = pi_scale,
    pi_calibrated = pi_calibrated
  )
}