R/rtTimeMatrix.R

In hpiR: House Price Indexes

#'
#' Create model matrix for repeat transaction approach
#'
#' Generates the array necessary to estimate a repeat transactions model
#'
#' @param rt_df object of class `rtdata`: repeat transaction data.frame created by
#' rtCreateTrans()
#' @return matrix to be used on the right hand side of a repeat sales regression model
#' @section Further Details:
#' Time periods are calculated from the data provided.
#' @examples
#'
#'   # Load data
#'   data(ex_sales)
#'
#'   # With a raw transaction data.frame
#'   rt_data <- rtCreateTrans(trans_df = ex_sales,
#'                            prop_id = 'pinx',
#'                            trans_id = 'sale_id',
#'                            price = 'sale_price',
#'                            periodicity = 'monthly',
#'                            date = 'sale_date')
#'  # Create Matrix
#'  rt_matrix <- rtTimeMatrix(rt_data)
#'
#' @export

rtTimeMatrix <- function(rt_df
                         ){

  # Check for proper class
  if (!"rtdata" %in% class(rt_df)){
    message('The rt_df object you have supplied is not of class "rtdata". You can create ',
            'an rtdata object with the rtCreateTrans() function.')
    stop()
  }

  # Extract start/end/diff
  time_start <- min(rt_df$period_1)
  time_end <- max(rt_df$period_2)
  time_diff <- time_end - time_start

  # Set up emply matrix
  time_matrix <- array(0, dim = c(nrow(rt_df), time_diff))

  # Fill in time matrix
  for (tm in seq(time_start + 1, time_end)) {
    time_matrix[rt_df$period_1 == tm, tm - time_start] <- -1
    time_matrix[rt_df$period_2 == tm, tm - time_start] <- 1
  }

  # Name Time matrix
  colnames(time_matrix) <- paste0("time_", seq(time_start + 1, time_end))
  class(time_matrix) <- 'timematrix'

  # Return Value
  time_matrix

}