calcInSampleError: Calculate index errors in sample

In hpiR: House Price Indexes

Description

Estimate the predictive error of an index via an in-sample approach.

Usage

calcInSampleError(pred_df, index, ...)

Arguments

pred_df	Set of sales against which to test predictions
index	Index (of class 'ts') to be tested for accuracy
...	Additional Arguments

Value

object of class 'hpiaccuracy' inheriting from class 'data.frame' containing the following fields:

pair_id

Uniq Pair ID number

price

Transaction Price

pred_price

Predicted price

error

(Prediction - Actual) / Actual

log_error

log(prediction) - log(actual)

pred_period

Period of the prediction

Further Details

In addition to being a stand-alone function, it is also used by 'calcForecastError' and 'calcKFoldError“

Examples

 # Load example data
 data(ex_sales)

 # Create index with raw transaction data
 rt_index <- rtIndex(trans_df = ex_sales,
                     periodicity = 'monthly',
                     min_date = '2010-06-01',
                     max_date = '2015-11-30',
                     adj_type = 'clip',
                     date = 'sale_date',
                     price = 'sale_price',
                     trans_id = 'sale_id',
                     prop_id = 'pinx',
                     estimator = 'robust',
                     log_dep = TRUE,
                     trim_model = TRUE,
                     max_period = 48,
                     smooth = FALSE)

 # Calculate accuracy
 in_accr <- calcInSampleError(pred_df = rt_index$data,
                              index = rt_index$index$value)

hpiR documentation built on April 1, 2020, 5:09 p.m.