calcSeriesAccuracy | R Documentation |
Estimate the index accuracy for a (progressive) series of indexes
calcSeriesAccuracy(
series_obj,
test_method = "insample",
test_type = "rt",
pred_df = NULL,
smooth = FALSE,
summarize = FALSE,
in_place = FALSE,
in_place_name = "accuracy",
...
)
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 |
‘seriesaccuracy' object (unless calculated ’in_place')
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.
# 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')
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