rfModel {hpiR} | R Documentation |
Estimate random forest model for index creation
Description
Estimate coefficients for an index via the random forest approach (generic method)
Usage
rfModel(estimator, rf_df, rf_spec, ntrees = 200, seed = 1, ...)
Arguments
estimator |
Type of model to estimates (pdp) |
rf_df |
Transactions dataset from hedCreateSales() |
rf_spec |
Model specification ('formula' object) |
ntrees |
[200] Set number of trees to use |
seed |
[1] Random seed for reproducibility |
... |
Additional arguments |
Value
'rfmodel' object: model object of the estimator (ex.: 'lm')
Further Details
‘estimator' argument must be in a class of ’pdp' This function is not generally called directly, but rather from 'hpiModel()'
Examples
# Load example data
data(ex_sales)
# Create hedonic data
hed_data <- hedCreateTrans(trans_df = ex_sales,
prop_id = 'pinx',
trans_id = 'sale_id',
price = 'sale_price',
date = 'sale_date',
periodicity = 'monthly')
# Estimate Model
rf_model <- rfModel(estimator = structure('pdp', class = 'pdp'),
rf_df = hed_data,
rf_spec = as.formula(log(price) ~ baths + tot_sf),
ntrees = 10,
sim_count = 1)
[Package hpiR version 0.3.2 Index]