Migration Guide: FitCurves to fit_demand_fixed

In beezdemand: Behavioral Economic Easy Demand

knitr::opts_chunk$set(
  echo = TRUE,
  collapse = TRUE,
  comment = "#>",
  dev = "png",
  dpi = 144,
  fig.width = 7,
  fig.height = 5,
  warning = FALSE,
  message = FALSE
)
library(beezdemand)
library(dplyr)

Overview

Starting with beezdemand version 0.2.0, FitCurves() has been superseded by fit_demand_fixed(). This guide helps you migrate existing code to use the modern API.

For related workflows, see:

• vignette("beezdemand") for getting started
• vignette("model-selection") for choosing a model class
• vignette("mixed-demand") and vignette("mixed-demand-advanced") for mixed-effects models
• vignette("hurdle-demand-models") for hurdle models

Why Migrate?

The new fit_demand_fixed() function provides:

• Structured S3 objects with consistent methods (summary(), tidy(), glance(), predict(), plot(), confint(), augment())
• Tidyverse integration via tibble outputs from tidy() and glance()
• Standardized API consistent with other beezdemand model classes
• Better reproducibility with stored call and parameter information

FitCurves() will continue to work but is no longer actively developed. New features will only be added to fit_demand_fixed().

Quick Migration Reference

| FitCurves() | fit_demand_fixed() | Notes | |-------------|---------------------|-------| | dat | data | Renamed for consistency | | xcol | x_var | Renamed for consistency | | ycol | y_var | Renamed for consistency | | idcol | id_var | Renamed for consistency | | detailed = TRUE | Always detailed | fit_demand_fixed always returns full results | | groupcol | Not supported | Use factor models instead | | Returns data.frame | Returns S3 object | Use tidy() for data frame output |

Basic Migration

Before (FitCurves)

# Old approach
results <- FitCurves(
  dat = apt,
  equation = "hs",
  k = 2,
  xcol = "x",
  ycol = "y",
  idcol = "id"
)

# results is a data.frame with all parameters
head(results)

After (fit_demand_fixed)

# New approach
fit <- fit_demand_fixed(
  data = apt,
  equation = "hs",
  k = 2,
  x_var = "x",
  y_var = "y",
  id_var = "id"
)

# fit is a structured S3 object
print(fit)

Extracting Results

Getting Parameter Estimates

Before (FitCurves)

# FitCurves returns a data frame directly
results <- FitCurves(apt, "hs", k = 2)
q0_values <- results$Q0d
alpha_values <- results$Alpha

After (fit_demand_fixed)

# Use tidy() for a tibble of coefficients
fit <- fit_demand_fixed(apt, equation = "hs", k = 2)
coefs <- tidy(fit)
head(coefs)

# Or access the results data frame directly
head(fit$results)

Getting Model-Level Statistics

Before (FitCurves)

# Manual calculation required
results <- FitCurves(apt, "hs", k = 2)
n_converged <- sum(!is.na(results$Alpha))
mean_r2 <- mean(results$R2, na.rm = TRUE)

After (fit_demand_fixed)

# Use glance() for model-level statistics
fit <- fit_demand_fixed(apt, equation = "hs", k = 2)
glance(fit)

# Or use summary() for comprehensive output
summary(fit)

Working with Predictions

Before (FitCurves)

# Required detailed = TRUE and manual extraction
results <- FitCurves(apt, "hs", k = 2, detailed = TRUE)
# Predictions stored in list element
predictions <- results$newdats  # List of data frames

After (fit_demand_fixed)

# Use predict() method
fit <- fit_demand_fixed(apt, equation = "hs", k = 2)

# Predict at new prices
new_prices <- data.frame(x = c(0, 0.5, 1, 2, 5, 10))
preds <- predict(fit, newdata = new_prices)
head(preds)

Plotting

Before (FitCurves)

# Used separate PlotCurves() function
results <- FitCurves(apt, "hs", k = 2, detailed = TRUE)
PlotCurves(results)

After (fit_demand_fixed)

# Use plot() method directly on the fit object
fit <- fit_demand_fixed(apt, equation = "hs", k = 2)

# Plot first 5 subjects
plot(fit, ids = unique(apt$id)[1:5], facet = TRUE)

New in 0.2.0: Diagnostics + Residual Workflows

Once you migrate to the modern API, you can use standardized post-fit helpers:

• augment() to get fitted values + residuals in a tidy format
• check_demand_model() for a structured diagnostic summary
• plot_residuals() for common residual diagnostics (via augment())

fit <- fit_demand_fixed(apt, equation = "hs", k = 2)

augment(fit) |> head()
check_demand_model(fit)
plot_residuals(fit)$fitted

New in 0.2.0: Unified Systematicity Wrappers

For user-facing workflows, prefer:

• check_systematic_demand() (purchase task)
• check_systematic_cp() (cross-price)

These wrap legacy helpers (e.g., CheckUnsystematic()) but return a standardized beezdemand_systematicity object.

sys <- check_systematic_demand(apt)
head(sys$results)

Advanced Features

Aggregated Data (Mean/Pooled)

Both functions support aggregation, with identical syntax:

# Fit to mean data
fit_mean <- fit_demand_fixed(apt, equation = "hs", k = 2, agg = "Mean")
tidy(fit_mean)

Parameter Space

Both functions support log10 parameterization:

# Fit in log10 space
fit_log <- fit_demand_fixed(
  apt,
  equation = "hs",
  k = 2,
  param_space = "log10"
)

# tidy() can report in either scale
tidy(fit_log, report_space = "log10") |> head()
tidy(fit_log, report_space = "natural") |> head()

Feature Comparison

| Feature | FitCurves() | fit_demand_fixed() | |---------|-------------|---------------------| | Equations | hs, koff, linear | hs, koff, linear | | k options | numeric, "ind", "fit", "share" | numeric, "ind", "fit", "share" | | Aggregation | "Mean", "Pooled" | "Mean", "Pooled" | | Parameter space | natural, log10 | natural, log10 | | summary() method | No | Yes | | tidy() method | No | Yes | | glance() method | No | Yes | | predict() method | No | Yes | | plot() method | No | Yes | | coef() method | No | Yes | | augment() method | No | Yes | | confint() method | No | Yes |

Suppressing Deprecation Warnings

If you need to continue using FitCurves() without warnings (e.g., in legacy code or during a gradual migration), you can suppress the deprecation message:

# Suppress deprecation warning temporarily
rlang::with_options(
  lifecycle_verbosity = "quiet",
  FitCurves(apt, "hs", k = 2)
)

However, we strongly recommend migrating to fit_demand_fixed() for new code.

Need Help?

If you encounter issues during migration:

1. Check the function documentation: ?fit_demand_fixed
2. Review the beezdemand GitHub repository
3. Open an issue if you find bugs or have feature requests

See Also

• vignette("beezdemand") -- Getting started with beezdemand
• vignette("model-selection") -- Choosing the right model class
• vignette("fixed-demand") -- Fixed-effect demand modeling
• vignette("mixed-demand") -- Mixed-effects nonlinear demand models
• vignette("mixed-demand-advanced") -- Advanced mixed-effects topics
• vignette("hurdle-demand-models") -- Two-part hurdle demand models
• vignette("cross-price-models") -- Cross-price demand analysis
• vignette("group-comparisons") -- Group comparisons

Session Information

sessionInfo()

beezdemand documentation built on March 3, 2026, 9:07 a.m.