calcInSampleError: Calculate index errors in sample

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:

prop_id: Property Identification number
pred_price: Predicted price
pred_error: (Prediction - Actual) / 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

Run this code

# NOT RUN {
 # 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)

# }

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