beezdemand (version 0.2.0)

get_k: Calculate K Scaling Parameter for Demand Curve Fitting

Description

Calculates the k scaling parameter used in demand curve equations to normalize consumption across different units or ranges. The k value is derived from the logarithmic range of consumption values.

This is the modern replacement for GetK(), with explicit parameters for the adjustment value and optional verbose output.

Usage

get_k(
  data,
  use_means = TRUE,
  adjustment = 0.5,
  x_var = "x",
  y_var = "y",
  verbose = FALSE
)

Value

A single numeric value representing the k scaling parameter

Arguments

data

A data frame in long format with columns for price and consumption

use_means

Logical indicating whether to calculate k from mean consumption by price (TRUE, default) or from individual consumption values (FALSE)

adjustment

Numeric adjustment added to the log range (default: 0.5). This value ensures k is slightly larger than the observed range.

x_var

Character string specifying the column name for price (default: "x")

y_var

Character string specifying the column name for consumption (default: "y")

verbose

Logical indicating whether to print calculation details (default: FALSE)

Details

The k parameter is calculated as:

$$k = \log_{10}(\text{max}) - \log_{10}(\text{min}) + \text{adjustment}$$

where max and min are the maximum and minimum non-zero consumption values.

Use in Demand Equations

The k parameter appears in several demand curve equations:

• Hursh & Silberberg (2008): Scales the exponential term

• Koffarnus et al. (2015): Normalizes the exponentiated model

• Ensures numerical stability during model fitting

Calculation Modes

• use_means = TRUE (default): Calculates k from mean consumption at each price point. Recommended when data has multiple subjects, as it reduces influence of individual outliers.

• use_means = FALSE: Calculates k from the full range of individual consumption values. May be preferable for single-subject data or when individual variability is theoretically important.

See Also

• GetK() - Legacy function (superseded)

• FitCurves() - Uses k parameter in demand curve fitting

Examples

# \donttest{
data(apt, package = "beezdemand")

# Calculate k using default settings (mean range + 0.5)
k_val <- get_k(apt)

# Calculate k from individual values
k_ind <- get_k(apt, use_means = FALSE)

# Calculate with custom adjustment
k_custom <- get_k(apt, adjustment = 1.0)

# Show calculation details
k_verbose <- get_k(apt, verbose = TRUE)
# }