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Migration Guide: FitCurves to fit_demand_fixed

beezdemand Developers

2026-03-02

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Starting with beezdemand version 0.2.0, FitCurves() has been superseded by fit_demand_fixed().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)
#> 
#> Fixed-Effect Demand Model
#> ==========================
#> 
#> Call:
#> fit_demand_fixed(data = apt, equation = "hs", k = 2, x_var = "x", 
#>     y_var = "y", id_var = "id")
#> 
#> Equation: hs 
#> k: fixed (2) 
#> Subjects: 10 ( 10 converged, 0 failed)
#> 
#> Use summary() for parameter summaries, tidy() for tidy output.

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)
#> # 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>

# Or access the results data frame directly
head(fit$results)
#>    id Intensity BP0 BP1 Omaxe Pmaxe Equation       Q0d K       Alpha        R2
#> 1  19        10  NA  20    45    15       hs 10.158664 2 0.002047574 0.9804182
#> 2  30         3  NA  20    20    20       hs  2.807366 2 0.005865523 0.7723159
#> 3  38         4  NA  10    21     7       hs  4.497456 2 0.004203441 0.8767531
#> 4  60        10  NA  10    24     8       hs  9.924274 2 0.004299344 0.9714808
#> 5  68        10  NA  10    36     9       hs 10.390384 2 0.002765273 0.9723669
#> 6 106         5  NA   7    15     5       hs  5.683566 2 0.006281201 0.9283457
#>        Q0se      Alphase alpha_star alpha_star_se  N      AbsSS      SdRes
#> 1 0.2685323 6.090445e-05 0.00836391  0.0002487819 16 0.01113625 0.02820366
#> 2 0.2257764 6.760007e-04 0.02395943  0.0027613206 16 0.10721624 0.08751173
#> 3 0.2146862 3.571177e-04 0.01717017  0.0014587507 14 0.02387617 0.04460584
#> 4 0.4591683 1.449834e-04 0.01756192  0.0005922269 14 0.02013543 0.04096282
#> 5 0.3290277 9.636582e-05 0.01129556  0.0003936341 14 0.01006059 0.02895483
#> 6 0.3002817 4.315615e-04 0.02565739  0.0017628381 11 0.01651102 0.04283174
#>      Q0Low    Q0High    AlphaLow   AlphaHigh        EV    Omaxd     Pmaxd
#> 1 9.582720 10.734609 0.001916947 0.002178201 1.7266939 44.43035 13.869758
#> 2 2.323124  3.291609 0.004415646 0.007315401 0.6027653 15.51003 17.520210
#> 3 4.029695  4.965217 0.003425349 0.004981534 0.8411046 21.64285 15.260658
#> 4 8.923832 10.924716 0.003983452 0.004615236 0.8223426 21.16008  6.761518
#> 5 9.673495 11.107274 0.002555310 0.002975236 1.2785478 32.89890 10.040966
#> 6 5.004282  6.362850 0.005304941 0.007257462 0.5628754 14.48361  8.081306
#>      Omaxa     Pmaxa     Notes converged
#> 1 44.43095 13.955544 converged      TRUE
#> 2 15.51024 17.628574 converged      TRUE
#> 3 21.64314 15.355046 converged      TRUE
#> 4 21.16036  6.803339 converged      TRUE
#> 5 32.89934 10.103070 converged      TRUE
#> 6 14.48380  8.131289 converged      TRUE

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)
#> # 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>

# Or use summary() for comprehensive output
summary(fit)
#> 
#> Fixed-Effect Demand Model Summary
#> ================================================== 
#> 
#> Equation: hs 
#> k: fixed (2) 
#> 
#> Fit Summary:
#>   Total subjects: 10 
#>   Converged: 10 
#>   Failed: 0 
#>   Total observations: 146 
#> 
#> Parameter Summary (across subjects):
#>   Q0:
#>     Median: 6.2498 
#>     Range: [ 2.8074 , 10.3904 ]
#>   alpha:
#>     Median: 0.004251 
#>     Range: [ 0.001987 , 0.00785 ]
#> 
#> Per-subject coefficients:
#> -------------------------
#> # A tibble: 40 × 10
#>    id    term      estimate std.error statistic p.value component estimate_scale
#>    <chr> <chr>        <dbl>     <dbl>     <dbl>   <dbl> <chr>     <chr>         
#>  1 106   Q0         5.68     0.300           NA      NA fixed     natural       
#>  2 106   alpha      0.00628  0.000432        NA      NA fixed     natural       
#>  3 106   alpha_st…  0.0257   0.00176         NA      NA fixed     natural       
#>  4 106   k          2       NA               NA      NA fixed     natural       
#>  5 113   Q0         6.20     0.174           NA      NA fixed     natural       
#>  6 113   alpha      0.00199  0.000109        NA      NA fixed     natural       
#>  7 113   alpha_st…  0.00812  0.000447        NA      NA fixed     natural       
#>  8 113   k          2       NA               NA      NA fixed     natural       
#>  9 142   Q0         6.17     0.641           NA      NA fixed     natural       
#> 10 142   alpha      0.00237  0.000400        NA      NA fixed     natural       
#> # ℹ 30 more rows
#> # ℹ 2 more variables: term_display <chr>, estimate_internal <dbl>

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)
#> # A tibble: 6 × 3
#>       x id    .fitted
#>   <dbl> <chr>   <dbl>
#> 1     0 19      10.2 
#> 2     0 30       2.81
#> 3     0 38       4.50
#> 4     0 60       9.92
#> 5     0 68      10.4 
#> 6     0 106      5.68

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()
#> # A tibble: 6 × 7
#>   id        x     y group     k .fitted  .resid
#>   <chr> <dbl> <dbl> <chr> <dbl>   <dbl>   <dbl>
#> 1 19      0      10 a         2   10.2  -0.159 
#> 2 19      0.5    10 a         2    9.69  0.314 
#> 3 19      1      10 a         2    9.24  0.760 
#> 4 19      1.5     8 a         2    8.82 -0.819 
#> 5 19      2       8 a         2    8.42 -0.421 
#> 6 19      2.5     8 a         2    8.04 -0.0444
check_demand_model(fit)
#> 
#> 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
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)
#> # A tibble: 4 × 10
#>   id    term       estimate std.error statistic p.value component estimate_scale
#>   <chr> <chr>         <dbl>     <dbl>     <dbl>   <dbl> <chr>     <chr>         
#> 1 mean  Q0          7.44      2.03e-1        NA      NA fixed     natural       
#> 2 mean  alpha       0.00435   8.89e-5        NA      NA fixed     natural       
#> 3 mean  alpha_star  0.0178    3.63e-4        NA      NA fixed     natural       
#> 4 mean  k           2        NA              NA      NA fixed     natural       
#> # ℹ 2 more variables: term_display <chr>, estimate_internal <dbl>

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()
#> # 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       1.01     0.0115        NA      NA fixed     log10         
#> 2 30    Q0       0.448    0.0349        NA      NA fixed     log10         
#> 3 38    Q0       0.653    0.0207        NA      NA fixed     log10         
#> 4 60    Q0       0.997    0.0201        NA      NA fixed     log10         
#> 5 68    Q0       1.02     0.0138        NA      NA fixed     log10         
#> 6 106   Q0       0.755    0.0229        NA      NA fixed     log10         
#> # ℹ 2 more variables: term_display <chr>, estimate_internal <dbl>
tidy(fit_log, report_space = "natural") |> 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>

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:

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

Session Information

sessionInfo()
#> R version 4.5.2 (2025-10-31)
#> Platform: aarch64-apple-darwin20
#> Running under: macOS Tahoe 26.3
#> 
#> Matrix products: default
#> BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib 
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1
#> 
#> locale:
#> [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#> 
#> time zone: America/New_York
#> tzcode source: internal
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] purrr_1.2.0      beezdemand_0.2.0 ggplot2_4.0.1    tidyr_1.3.2     
#> [5] dplyr_1.1.4     
#> 
#> loaded via a namespace (and not attached):
#>  [1] nls.multstart_2.0.0 gtable_0.3.6        TMB_1.9.19         
#>  [4] xfun_0.55           bslib_0.9.0         insight_1.4.4      
#>  [7] lattice_0.22-6      vctrs_0.6.5         tools_4.5.2        
#> [10] Rdpack_2.6.4        generics_0.1.4      pbkrtest_0.5.5     
#> [13] parallel_4.5.2      tibble_3.3.0        pkgconfig_2.0.3    
#> [16] Matrix_1.7-4        RColorBrewer_1.1-3  S7_0.2.1           
#> [19] lifecycle_1.0.4     compiler_4.5.2      farver_2.1.2       
#> [22] minpack.lm_1.2-4    codetools_0.2-20    nlstools_2.1-0     
#> [25] htmltools_0.5.9     sass_0.4.10         yaml_2.3.12        
#> [28] pillar_1.11.1       nloptr_2.2.1        jquerylib_0.1.4    
#> [31] MASS_7.3-60.2       cachem_1.1.0        reformulas_0.4.2   
#> [34] boot_1.3-32         nlme_3.1-168        tidyselect_1.2.1   
#> [37] digest_0.6.39       performance_0.15.2  mvtnorm_1.3-3      
#> [40] labeling_0.4.3      splines_4.5.2       fastmap_1.2.0      
#> [43] grid_4.5.2          cli_3.6.5           magrittr_2.0.4     
#> [46] utf8_1.2.6          broom_1.0.11        withr_3.0.2        
#> [49] scales_1.4.0        backports_1.5.0     estimability_1.5.1 
#> [52] rmarkdown_2.30      emmeans_2.0.1       otel_0.2.0         
#> [55] lme4_1.1-38         coda_0.19-4.1       evaluate_1.0.5     
#> [58] knitr_1.51          rbibutils_2.4       mgcv_1.9-3         
#> [61] rlang_1.1.6         Rcpp_1.1.0          xtable_1.8-4       
#> [64] glue_1.8.0          nlsr_2023.8.31      minqa_1.2.8        
#> [67] jsonlite_2.0.0      R6_2.6.1

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.

Migration Guide: FitCurves to fit_demand_fixed

beezdemand Developers

2026-03-02

Overview

Why Migrate?

Quick Migration Reference

Basic Migration

Before (FitCurves)

After (fit_demand_fixed)

Extracting Results

Getting Parameter Estimates

Before (FitCurves)

After (fit_demand_fixed)

Getting Model-Level Statistics

Before (FitCurves)

After (fit_demand_fixed)

Working with Predictions

Before (FitCurves)

After (fit_demand_fixed)

Plotting

Before (FitCurves)

After (fit_demand_fixed)

New in 0.2.0: Diagnostics + Residual Workflows

New in 0.2.0: Unified Systematicity Wrappers

Advanced Features

Aggregated Data (Mean/Pooled)

Parameter Space

Feature Comparison

Suppressing Deprecation Warnings

Need Help?

See Also

Session Information