hpiModel {hpiR}

Wrapper to estimate model approaches (generic method)

Description

Generic method to estimate modeling approaches for indexes

Usage

hpiModel(model_type, hpi_df, estimator = "base", log_dep = TRUE,
  trim_model = TRUE, mod_spec = NULL, ...)

Arguments

model_type

Type of model to estimate ('rt', 'hed', 'rf')

hpi_df

Dataset created by one of the *CreateTrans() function in this package.

estimator

Type of estimator to be used ('base', 'weighted', 'robust')

log_dep

default TRUE, should the dependent variable (change in price) be logged?

trim_model

default TRUE, should excess be trimmed from model results ('lm' or 'rlm' object)?

mod_spec

Model specification

...

Additional Arguments

Value

hpimodel object consisting of:

estimator

Type of estimator

coefficients

Data.frame of coefficient

model_obj

class 'rtmodel' or 'hedmodel'

mod_spec

Full model specification

log_dep

Binary: is the dependent variable in logged format

base_price

Mean price in the base period

periods

'data.frame' of periods

approach

Type of model used

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 model object
 hpi_model <- hpiModel(model_type = 'rt',
                       hpi_df = rt_data,
                       estimator = 'base',
                       log_dep = TRUE)

 # For custom weighted repeat transaction model

 hpi_model_wgt <- hpiModel(model_type = 'rt',
                           hpi_df = rt_data,
                           estimator = 'weighted',
                           weights = runif(nrow(rt_data), 0, 1))