Package 'REPS'

Calculate index based on specified method (Fisher, Laspeyres, Paasche, HMTS, Time Dummy, Rolling Time Dummy)

Description

Central hub function to calculate index figures using different methods. Can also calculate chained indices using the Annual Overlap Method.

Usage

calculate_hedonic_index(
  dataset,
  method,
  period_variable,
  dependent_variable,
  numerical_variables = NULL,
  categorical_variables = NULL,
  reference_period = NULL,
  number_of_observations = TRUE,
  chained = FALSE,
  index_mutation = FALSE,
  parallel = FALSE,
  ...
)

Arguments

dataset	Data frame with input data
method	One of: "fisher", "laspeyres", "paasche", "hmts", "timedummy", "rolling_timedummy", "repricing"
period_variable	A string with the name of the column containing time periods.
dependent_variable	Usually the price
numerical_variables	Vector with numeric quality-determining variables
categorical_variables	Vector with categorical variables (also dummies)
reference_period	Period or group of periods that will be set to 100
number_of_observations	Logical, whether to show number of observations (default = TRUE)
chained	Logical. If TRUE, calculates a chained index using the Annual Overlap Method. Default is FALSE.
index_mutation	Logical. If TRUE, calculates a contribution-to-index-mutation table for one method. Default is FALSE.
parallel	Logical. If TRUE, independent calculations are parallelized where useful. Default is FALSE.
...	Additional method-specific arguments passed to the underlying functions: periods_in_year: (Required for Repricing) Number of periods per year (e.g. 12 for months, 4 for quarters) number_preliminary_periods: (Optional for HMTS) Number of preliminary periods. Default = 3 production_since: (Optional for HMTS) Start period for production simulation. Default = NULL resting_points: (Optional for HMTS) Whether to return detailed outputs. Default = FALSE imputation: (Optional for Laspeyres/Paasche) Include imputation values? Default = FALSE window_length: (Optional for Rolling Time Dummy) Window size in number of periods. Default = 5 unit_variable: (Optional for index mutation) Unit column to exclude as groups. Default = NULL index_mutation_period: (Optional for index mutation) Period to analyze. Default = latest period

Value

A data.frame (or list for HMTS with resting_points = TRUE; named list if multiple methods are used; or list with Index and Index_mutation when index_mutation = TRUE)

Author(s)

Vivek Gajadhar

Examples

## Not run: 
data("hedonic_data")

Tbl_indices <- REPS::calculate_hedonic_index(
  method = c("fisher", "hmts", "laspeyres", "paasche",
 "repricing", "timedummy", "rolling_timedummy"),
  dataset = hedonic_data,
  period_variable = "period",
  dependent_variable = "price",
  numerical_variables = c("floor_area", "dist_trainstation"),
  categorical_variables = c("neighbourhood_code", "dummy_large_city"),
  reference_period = "2015",
  number_of_observations = FALSE,
  periods_in_year = 4,
  number_preliminary_periods = 1,
  window_length = 4,
  production_since = NULL,
  resting_points = FALSE,
  imputation = FALSE
)

## End(Not run)

---

Calculate Price Index (Deprecated)

Description

This function has been renamed. Please use calculate_hedonic_index instead.

Usage

calculate_price_index(...)

Arguments

...	Arguments passed to calculate_hedonic_index.

---

Calculate regression diagnostics by period

Description

For each period in the data, fits a log-linear model and computes diagnostics:

• Normality test (Shapiro-Wilk)

• Adjusted R-squared

• Breusch-Pagan test for heteroscedasticity

• Durbin-Watson test for autocorrelation

Usage

calculate_regression_diagnostics(
  dataset,
  period_variable,
  dependent_variable,
  numerical_variables = NULL,
  categorical_variables = NULL,
  parallel = FALSE
)

Arguments

dataset	A data.frame with input data
period_variable	Name of the period variable (string)
dependent_variable	Name of the dependent variable (string)
numerical_variables	Vector of numerical independent variables (default = NULL)
categorical_variables	Vector of categorical independent variables (default = NULL)
parallel	Logical; whether independent period-level diagnostics are parallelized.

Value

A data.frame with diagnostics by period

Author(s)

Mohammad Kardal, Vivek Gajadhar

Examples

diagnostics <- calculate_regression_diagnostics(
  dataset = hedonic_data,
  period_variable = "period",
  dependent_variable = "price",
  numerical_variables = c("floor_area", "dist_trainstation"),
  categorical_variables = c("dummy_large_city", "neighbourhood_code")
)
head(diagnostics)

---

A real estate example dataframe

Description

A subset of data from a fictitious real estate data frame containing transaction prices and some categorical and numerical characteristics of each dwelling.

Usage

hedonic_data

Format

A data frame with 7,800 rows and 6 columns:

period

A (string) vector indicating a time period

price

A (string) vector indicating the transaction price of the dwelling

floor_area

A real-valued vector of (the logarithm of) the floor area of the dwelling

dist_trainstation

A real-valued vector of (the logarithm of) the distance of the dwelling to the nearest train station

neighbourhood_code

A categorical code/string referring to the neighbourhood the dwelling belongs to

dummy_large_city

A vector indicating whether the dwelling belongs to a large city or not

Source

A fictitious dataset for illustration purposes

Examples

data(hedonic_data)
head(hedonic_data)

---

Plot index output from calculate_hedonic_index

Description

Static price index plot using base R graphics with grid lines and external legend.

Usage

plot_price_index(index_output, title = NULL)

Arguments

index_output	A data.frame or named list of data.frames (from calculate_hedonic_index())
title	Optional plot title

Details

Supports both single index data.frame and named list of multiple methods. X-axis shows only first period of each year with rotated labels to avoid clutter.

Value

None. Draws plots in the active graphics device.

Author(s)

Vivek Gajadhar

---

Plot diagnostics output from calculate_regression_diagnostics as a multi-panel grid (base R)

Description

Creates a static 3x2 grid of base R plots showing regression diagnostics:

• Normality (Shapiro-Wilk)

• Linearity (Adjusted R-squared)

• Heteroscedasticity (Breusch-Pagan)

• Autocorrelation (Durbin-Watson)

• Autocorrelation (p-value DW)

Usage

plot_regression_diagnostics(diagnostics, title = "Regression Diagnostics")

Arguments

diagnostics	A data.frame as returned by calculate_regression_diagnostics()
title	Optional overall title for the entire plot grid (default: "Regression Diagnostics")

Value

None. Produces plots in the active graphics device.

Author(s)

Vivek Gajadhar

Examples

plot_regression_diagnostics(
  calculate_regression_diagnostics(
    dataset = hedonic_data,
    period_variable = "period",
    dependent_variable = "price",
    numerical_variables = c("floor_area", "dist_trainstation"),
    categorical_variables = c("dummy_large_city", "neighbourhood_code")
  )
)

---