calcSeriesAccuracy {hpiR}

Calculate the accuracy of a series of indexes

Description

Estimate the index accuracy for a (progressive) series of indexes

Usage

calcSeriesAccuracy(series_obj, test_method = "insample",
  test_type = "rt", pred_df = NULL, smooth = FALSE,
  summarize = FALSE, in_place = FALSE, in_place_name = "accuracy",
  ...)

Arguments

series_obj

Serieshpi object to be analyzed

test_method

default = 'insample'; Also 'kfold' or 'forecast'

test_type

default = 'rt'; Type of data to use for test. See details.

pred_df

default = NULL; Extra data if the test_type doesn't match data in hpi_obj

smooth

default = FALSE; Analyze the smoothed indexes

summarize

default = FALSE; When multiple accuracy measurements for single observation take the mean of them all.

in_place

default = FALSE; Should the result be returned into an existing 'hpi' object

in_place_name

default = 'accuracy'; Name for returning in place

...

Additional Arguments

Value

‘seriesaccuracy' object (unless calculated ’in_place')

Further Details

Unless using 'test_method = "forecast"“ with a "forecast_length" of 1, the results will have more than one accuracy estimate per observations. Setting 'summarize = TRUE' will take the mean accuracy for each observation across all indexes.

Examples

 # Load data
 data(ex_sales)

 # Create index
 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)

  #  Create Series (Suppressing messages do to small sample size of this example)
  suppressMessages(
    hpi_series <- createSeries(hpi_obj = rt_index,
                               train_period = 12))

  # Calculate insample accuracy
  hpi_series_accr <- calcSeriesAccuracy(series_obj = hpi_series,
                                        test_type = 'rt',
                                        test_method = 'insample')