Author: Brent Kaplan
Date: 2026-03-02

# Choosing the Right Demand Model

#### Brent Kaplan

#### 2026-03-02

# Introduction

The `beezdemand` package provides multiple approaches for fitting behavioral economic demand curves. This guide helps you choose the right modeling approach for your data and research questions.

## When to Use Demand Analysis

Demand analysis is appropriate when you want to: - Quantify the value of a reinforcer (e.g., drugs, food, activities) - Compare demand elasticity across conditions or groups - Estimate key parameters like intensity (Q0), elasticity (alpha), and breakpoint - Compute derived metrics like Pmax (price at maximum expenditure) and Omax (maximum expenditure)

## Quick Decision Guide

Your Situation | Recommended Approach | Function
--- | --- | ---
Individual curves, quick exploration | Fixed-effects NLS | `fit_demand_fixed()`
Group comparisons, repeated measures | Mixed-effects | `fit_demand_mixed()`
Many zeros, two-part modeling | Hurdle model | `fit_demand_hurdle()`
Cross-commodity substitution | Cross-price models | `fit_cp_*()`

# Data Quality First

Before fitting any model, always check your data for systematic responding.

    # Check for systematic demand
    systematic_check <- check_systematic_demand(apt)
    head(systematic_check$results)
    #> # A tibble: 6 × 15
    #>   id    type   trend_stat trend_threshold trend_direction trend_pass bounce_stat
    #>   <chr> <chr>       <dbl>           <dbl> <chr>           <lgl>            <dbl>
    #> 1 19    demand      0.211           0.025 down            TRUE                 0
    #> 2 30    demand      0.144           0.025 down            TRUE                 0
    #> 3 38    demand      0.788           0.025 down            TRUE                 0
    #> 4 60    demand      0.909           0.025 down            TRUE                 0
    #> 5 68    demand      0.909           0.025 down            TRUE                 0
    #> 6 106   demand      0.818           0.025 down            TRUE                 0
    #> # ℹ 8 more variables: bounce_threshold <dbl>, bounce_direction <chr>,
    #> #   bounce_pass <lgl>, reversals <int>, reversals_pass <lgl>, returns <int>,
    #> #   n_positive <int>, systematic <lgl>

    # Filter to systematic data only (those that pass all criteria)
    systematic_ids <- systematic_check$results |>
      filter(systematic) |>
      dplyr::pull(id)

    apt_clean <- apt |>
      filter(as.character(id) %in% systematic_ids)

    cat("Systematic participants:", n_distinct(apt_clean$id),
        "of", n_distinct(apt$id), "\n")
    #> Systematic participants: 10 of 10

The `check_systematic_demand()` function applies criteria from Stein et al. (2015) to identify nonsystematic responding patterns including:

- Trend (DeltaQ): Consumption should decrease as price increases
- Bounce: Limited price-to-price increases in consumption
- Reversals: No consumption after sustained zeros

# Tier 1: Fixed-Effects NLS

## When to Use

Use `fit_demand_fixed()` when you want:

- Individual demand curves for each participant
- Quick exploration of your data
- Compatibility with traditional analysis approaches
- Per-subject parameter estimates without pooling information

## Complete Example

    # Fit individual demand curves using the Hursh & Silberberg equation
    fit_fixed <- fit_demand_fixed(
      data = apt,
      equation = "hs",
      k = 2
    )

    # Print summary
    print(fit_fixed)
    #>
    #> Fixed-Effect Demand Model
    #> ==========================
    #>
    #> Call:
    #> fit_demand_fixed(data = apt, equation = "hs", k = 2)
    #>
    #> Equation: hs
    #> k: fixed (2)
    #> Subjects: 10 ( 10 converged, 0 failed)
    #>
    #> Use summary() for parameter summaries, tidy() for tidy output.

    # Get tidy coefficient table
    tidy(fit_fixed) |> head()
    #> # A tibble: 6 × 10
    #>   id    term  estimate std.error statistic p.value component estimate_scale
    #>   <chr> <chr>    <dbl>     <dbl>     <dbl>   <dbl> <chr>     <chr>
    #> 1 19    Q0       10.2      0.269        NA      NA fixed     natural
    #> 2 30    Q0        2.81     0.226        NA      NA fixed     natural
    #> 3 38    Q0        4.50     0.215        NA      NA fixed     natural
    #> 4 60    Q0        9.92     0.459        NA      NA fixed     natural
    #> 5 68    Q0       10.4      0.329        NA      NA fixed     natural
    #> 6 106   Q0        5.68     0.300        NA      NA fixed     natural
    #> # ℹ 2 more variables: term_display <chr>, estimate_internal <dbl>

    # Get model-level statistics
    glance(fit_fixed)
    #> # A tibble: 1 × 12
    #>   model_class      backend equation k_spec     nobs n_subjects n_success n_fail
    #>   <chr>            <chr>   <chr>    <chr>     <int>      <int>     <int>  <int>
    #> 1 beezdemand_fixed legacy  hs       fixed (2)   146         10        10      0
    #> # ℹ 4 more variables: converged <lgl>, logLik <dbl>, AIC <dbl>, BIC <dbl>

    # Plot individual curves
    plot(fit_fixed, type = "individual", ids = c("19", "51"))

Individual demand curves for two example participants.

    # Basic diagnostics
    check_demand_model(fit_fixed)
    #>
    #> Model Diagnostics
    #> ==================================================
    #> Model class: beezdemand_fixed
    #>
    #> Convergence:
    #>   Status: Converged
    #>
    #> Residuals:
    #>   Mean: 0.0284
    #>   SD: 0.5306
    #>   Range: [-1.458, 2.228]
    #>   Outliers: 3 observations
    #>
    #> --------------------------------------------------
    #> Issues Detected (1):
    #>   1. Detected 3 potential outliers across subjects

# Tier 2: Mixed-Effects Models

## When to Use

Use `fit_demand_mixed()` when you want:

- Group comparisons with statistical inference
- Random effects to account for individual variability
- Post-hoc comparisons between conditions
- Estimated marginal means for factor levels

## The LL4 Transformation

For the mixed-effects approach with the `zben` equation form, transform your consumption data using the LL4 (log-log with 4-parameter adjustment):

    # Transform consumption using LL4
    apt_ll4 <- apt |>
      dplyr::mutate(y_ll4 = ll4(y))

    # View the transformation
    apt_ll4 |>
      dplyr::filter(id == 19) |>
      dplyr::select(id, x, y, y_ll4)

## Complete Example

    # Fit mixed-effects model
    fit_mixed <- fit_demand_mixed(
      data = apt_ll4,
      y_var = "y_ll4",
      x_var = "x",
      id_var = "id",
      equation_form = "zben"
    )

    # Print summary
    print(fit_mixed)
    summary(fit_mixed)

    # Basic diagnostics
    check_demand_model(fit_mixed)
    plot_residuals(fit_mixed)$fitted

## Post-Hoc Analysis with emmeans

For experimental designs with factors, you can use `emmeans` for post-hoc comparisons:

    # For a model with factors (example with ko dataset):
    data(ko)

    # Prepare data with LL4 transformation
    # (Note: the ko dataset already includes a y_ll4 column, but we
    # recreate it here to demonstrate the transformation workflow)
    ko_ll4 <- ko |>
      dplyr::mutate(y_ll4 = ll4(y))

    fit <- fit_demand_mixed(
      data = ko_ll4,
      y_var = "y_ll4",
      x_var = "x",
      id_var = "monkey",
      factors = c("drug", "dose"),
      equation_form = "zben"
    )

    # Get estimated marginal means for Q0 and alpha across drug levels
    emms <- get_demand_param_emms(fit, factors_in_emm = "drug", include_ev = TRUE)

    # Pairwise comparisons of drug conditions
    comps <- get_demand_comparisons(fit, compare_specs = ~drug, contrast_type = "pairwise")

# Tier 3: Hurdle Models

## When to Use

Use `fit_demand_hurdle()` when you have:

- Substantial zero consumption values (>10-15% zeros)
- Need for two-part modeling (zeros vs. positive consumption)
- Interest in both participation and consumption intensity
- TMB backend for fast, stable estimation

## Understanding the Two-Part Structure

The hurdle model separates:

1. Part I: Probability of zero consumption (logistic regression)
2. Part II: Log-consumption given positive response (nonlinear mixed effects)

## Complete Example

    # Fit hurdle model with 3 random effects
    fit_hurdle <- fit_demand_hurdle(
      data = apt,
      y_var = "y",
      x_var = "x",
      id_var = "id",
      random_effects = c("zeros", "q0", "alpha")
    )

    # Summary
    summary(fit_hurdle)

    # Population demand curve
    plot(fit_hurdle, type = "demand")

    # Probability of zero consumption
    plot(fit_hurdle, type = "probability")

    # Basic diagnostics
    check_demand_model(fit_hurdle)
    plot_residuals(fit_hurdle)$fitted
    plot_qq(fit_hurdle)

## Comparing Hurdle Models

Compare nested models using likelihood ratio tests:

    # Fit full model (3 random effects)
    fit_hurdle3 <- fit_demand_hurdle(
      data = apt,
      y_var = "y",
      x_var = "x",
      id_var = "id",
      random_effects = c("zeros", "q0", "alpha")
    )

    # Fit simplified model (2 random effects)
    fit_hurdle2 <- fit_demand_hurdle(
      data = apt,
      y_var = "y",
      x_var = "x",
      id_var = "id",
      random_effects = c("zeros", "q0")  # No alpha random effect
    )

    # Compare models
    compare_hurdle_models(fit_hurdle3, fit_hurdle2)

    # Unified model comparison (AIC/BIC + LRT when appropriate)
    compare_models(fit_hurdle3, fit_hurdle2)

# Choosing an Equation

The `equation` argument determines the functional form of the demand curve. Each equation has trade-offs in terms of flexibility, zero handling, and comparability across studies.

Equation | Function | Handles Zeros | k Required | Best For
--- | --- | --- | --- | ---
`"hs"` | Hursh & Silberberg (2008) | No | Yes | Traditional analyses, compatibility with older literature
`"koff"` | Koffarnus et al. (2015) | No | Yes | Modified exponential, widely used in applied research
`"simplified"` | Rzeszutek et al. (2025) | Yes | No | Modern analyses; avoids k-dependency and zero issues

Recommendations:

- For new analyses, consider using `"simplified"` (also called SND) as it handles zeros natively and does not require specifying `k`, making results more comparable across studies.
- For replication or comparability with existing literature, use `"hs"` or `"koff"` with the same `k` specification as the original study.
- When using `"hs"` or `"koff"`, zeros in consumption data are incompatible with the log transformation and will be dropped with a warning.

# Choosing Parameters

## The k Parameter

The scaling constant `k` controls the asymptotic range of the demand curve:

- `k = 2`: Good default for most purchase task data
- `k = "ind"`: Calculate k individually for each participant
- `k = "fit"`: Estimate k as a free parameter
- `k = "share"`: Fit a single k shared across all participants

    # Different k specifications
    fit_k2 <- fit_demand_fixed(apt, k = 2)          # Fixed at 2
    fit_kind <- fit_demand_fixed(apt, k = "ind")    # Individual
    fit_kfit <- fit_demand_fixed(apt, k = "fit")    # Free parameter
    fit_kshare <- fit_demand_fixed(apt, k = "share") # Shared across participants

## Interpreting Key Parameters

Parameter | Interpretation | Typical Range
--- | --- | ---
Q0 (Intensity) | Consumption at zero price | Dataset-dependent
alpha (Elasticity) | Rate of consumption decline | 0.0001 - 0.1
Pmax | Price at maximum expenditure | Dataset-dependent
Omax | Maximum expenditure | Dataset-dependent
Breakpoint | First price with zero consumption | Dataset-dependent

# Troubleshooting FAQ

## Model Convergence Issues

Problem: Model fails to converge or produces unreasonable estimates.

Solutions:

1. Check data quality with `check_systematic_demand()`
2. Try different starting values
3. Use a simpler model (fewer random effects)
4. Ensure sufficient data per participant

## Zero Handling

Problem: Many zeros in consumption data.

Solutions:

1. Use `fit_demand_hurdle()` which explicitly models zeros
2. For fixed-effects: zeros are automatically excluded for log-transformed equations
3. Consider data quality: excessive zeros may indicate nonsystematic responding

## Parameter Comparison Across Models

Problem: Comparing alpha values between different model types.

Solution: Be aware of parameterization differences:

- Hurdle models estimate on natural-log scale internally
- Use `tidy(fit, report_space = "log10")` for comparable output
- The transformation is: `log10(alpha) = log(alpha) / log(10)`

# Summary

Approach | Best For | Key Features | Handles Zeros
--- | --- | --- | ---
`fit_demand_fixed()` | Individual curves, quick analysis | Simple, per-subject estimates | Excludes
`fit_demand_mixed()` | Group comparisons, repeated measures | Random effects, emmeans integration | LL4 transform
`fit_demand_hurdle()` | Data with many zeros | Two-part model, TMB backend | Explicitly models

## Next Steps

- Getting Started: See `vignette("beezdemand")` for basic usage
- Fixed-Effect Models: See `vignette("fixed-demand")` for individual demand curve fitting
- Group Comparisons: See `vignette("group-comparisons")` for extra sum-of-squares F-test
- Mixed Models: See `vignette("mixed-demand")` for mixed-effects examples
- Advanced Mixed Models: See `vignette("mixed-demand-advanced")` for factors, EMMs, covariates
- Hurdle Models: See `vignette("hurdle-demand-models")` for hurdle model details
- Cross-Price: See `vignette("cross-price-models")` for substitution analyses
- Migration Guide: See `vignette("migration-guide")` for migrating from `FitCurves()`

# References

- Hursh, S. R., & Silberberg, A. (2008). Economic demand and essential value. Psychological Review, 115(1), 186-198.
- Koffarnus, M. N., Franck, C. T., Stein, J. S., & Bickel, W. K. (2015). A modified exponential behavioral economic demand model to better describe consumption data. Experimental and Clinical Psychopharmacology, 23(6), 504-512.
- Stein, J. S., Koffarnus, M. N., Snider, S. E., Quisenberry, A. J., & Bickel, W. K. (2015). Identification and management of nonsystematic purchase task data: Toward best practice. Experimental and Clinical Psychopharmacology, 23(5), 377-386.
- Kaplan, B. A., Franck, C. T., McKee, K., Gilroy, S. P., & Koffarnus, M. N. (2021). Applying mixed-effects modeling to behavioral economic demand: An introduction. Perspectives on Behavior Science, 44(2), 333-358.
- Rzeszutek, M. J., Regnier, S. D., Franck, C. T., & Koffarnus, M. N. (2025). Overviewing the exponential model of demand and introducing a simplification that solves issues of span, scale, and zeros. Experimental and Clinical Psychopharmacology.