Author: Brent Kaplan
Date: 2026-03-02

# How to Use Cross-Price Demand Model Functions

#### Brent Kaplan

#### 2026-03-02

## Introduction

Cross-price demand analysis examines how consumption of one commodity changes as the price of another commodity varies. This is central to understanding economic relationships between goods:

- Substitutes: When the price of the target commodity increases and consumption of the alternative increases, the goods function as substitutes (e.g., e-cigarettes and combustible cigarettes).
- Complements: When the price of the target increases and consumption of the alternative decreases, the goods function as complements.
- Independent: When the price of one commodity does not meaningfully affect consumption of the other.

These relationships are quantified through cross-price elasticity parameters. The `beezdemand` package provides nonlinear (`fit_cp_nls()`), linear (`fit_cp_linear()`), and mixed-effects linear (`fit_cp_linear(type = "mixed")`) approaches for cross-price modeling.

## Data Structure

    glimpse(etm)
    #> Rows: 240
    #> Columns: 5
    #> $ id     <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
    #> $ x      <dbl> 2, 4, 8, 16, 32, 64, 2, 4, 8, 16, 32, 64, 2, 4, 8, 16, 32, 64, …
    #> $ y      <dbl> 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 3, 5, 5, 16, 17, 13, 0, 0, …
    #> $ target <chr> "alt", "alt", "alt", "alt", "alt", "alt", "alt", "alt", "alt", …
    #> $ group  <chr> "Cigarettes", "Cigarettes", "Cigarettes", "Cigarettes", "Cigare…

Typical columns: - `id`: participant identifier

- `x`: alternative product price

- `y`: consumption level

- `target`: condition type (e.g., “alt”)

- `group`: product category

These are the default canonical column names. If your data uses different names, pass `x_var`, `y_var`, `id_var`, `group_var`, or `target_var` arguments to the fitting functions (e.g., `fit_cp_nls(data, x_var = "price", y_var = "qty")`).

## Complete Cross-Price Analysis Workflow

This section demonstrates a complete analysis workflow using data that contains multiple experimental conditions: target consumption when the alternative is absent (`alone`), target consumption when the alternative is present (`own`), and alternative consumption as a function of target price (`alt`).

### Loading the cp Dataset

    # Load the cross-price example dataset
    data("cp", package = "beezdemand")

    # Examine structure
    glimpse(cp)
    #> Rows: 48
    #> Columns: 5
    #> $ id     <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
    #> $ x      <dbl> 0.00, 0.01, 0.03, 0.06, 0.13, 0.25, 0.50, 1.00, 2.00, 4.00, 8.0…
    #> $ y      <dbl> 24.3430657, 22.8832117, 22.6058394, 21.0291971, 19.7810219, 15.…
    #> $ target <chr> "alone", "alone", "alone", "alone", "alone", "alone", "alone", …
    #> $ group  <chr> "cigarettes", "cigarettes", "cigarettes", "cigarettes", "cigare…

    # View conditions
    table(cp$target)
    #>
    #> alone   alt   own
    #>    16    16    16

The `cp` dataset contains:

- alone: Target commodity (cigarettes) consumption when alternative is not available
- own: Target commodity consumption when alternative is available
- alt: Alternative commodity (e-cigarettes) consumption as a function of target price

Note that `x` represents the target commodity price throughout all conditions.

### Step 1: Fit Target Demand (Alone Condition)

First, we fit a standard demand curve to the target commodity when the alternative is absent:

    # Filter to alone condition
    alone_data <- cp |>
        dplyr::filter(target == "alone")

    # Fit demand curve (modern interface)
    fit_alone <- fit_demand_fixed(
        data = alone_data,
        equation = "koff",
        k = 2
    )

    # View results
    fit_alone
    #>
    #> Fixed-Effect Demand Model
    #> ==========================
    #>
    #> Call:
    #> fit_demand_fixed(data = alone_data, equation = "koff", k = 2)
    #>
    #> Equation: koff
    #> k: fixed (2)
    #> Subjects: 1 ( 1 converged, 0 failed)
    #>
    #> Use summary() for parameter summaries, tidy() for tidy output.

### Step 2: Fit Target Demand (Own Condition)

Next, fit the same demand model to target consumption when the alternative is present:

    # Filter to own condition
    own_data <- cp |>
        dplyr::filter(target == "own")

    # Fit demand curve
    fit_own <- fit_demand_fixed(
        data = own_data,
        equation = "koff",
        k = 2
    )

    # View results
    fit_own
    #>
    #> Fixed-Effect Demand Model
    #> ==========================
    #>
    #> Call:
    #> fit_demand_fixed(data = own_data, equation = "koff", k = 2)
    #>
    #> Equation: koff
    #> k: fixed (2)
    #> Subjects: 1 ( 1 converged, 0 failed)
    #>
    #> Use summary() for parameter summaries, tidy() for tidy output.

### Step 3: Fit Cross-Price Model (Alt Condition)

Finally, fit the cross-price model to alternative consumption as a function of target price:

    # Filter to alt condition
    alt_data <- cp |>
        dplyr::filter(target == "alt")

    # Fit cross-price model
    fit_alt <- fit_cp_nls(
        data = alt_data,
        equation = "exponentiated",
        return_all = TRUE
    )

    # View results
    summary(fit_alt)
    #> Cross-Price Demand Model Summary
    #> ================================
    #>
    #> Model Specification:
    #> Equation type: exponentiated
    #> Functional form: y ~ (10^log10_qalone) * 10^(I * exp(-(10^log10_beta) * x))
    #> Fitting method: nls_multstart
    #> Method details: Multiple starting values optimization with nls.multstart
    #>
    #> Coefficients:
    #>                Estimate Std. Error  t value  Pr(>|t|)
    #> log10_qalone  1.1720228  0.0025378 461.8250 < 2.2e-16 ***
    #> I            -0.3712609  0.0066765 -55.6075 < 2.2e-16 ***
    #> log10_beta   -0.1271127  0.0217331  -5.8488 5.696e-05 ***
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    #>
    #> Confidence Intervals:
    #>                2.5 %   97.5 %
    #> log10_qalone  1.1665  1.17751
    #> I            -0.3857 -0.35684
    #> log10_beta   -0.1741 -0.08016
    #>
    #> Fit Statistics:
    #> R-squared: 0.9973
    #> AIC: 1.3
    #> BIC: 4.39
    #>
    #> Parameter Interpretation (natural scale):
    #> qalone (Q_alone): 14.86  - consumption at zero alternative price
    #> I: -0.3713  - interaction parameter (substitution direction)
    #> beta: 0.7463  - sensitivity parameter (sensitivity of relation to price)
    #>
    #> Optimizer parameters (log10 scale):
    #> log10_qalone: 1.172
    #> log10_beta: -0.1271

`fit_cp_nls()` uses a log10-parameterized optimizer internally (for numerical stability), but `predict()` returns `y_pred` on the natural `y` scale. For the `"exponential"` form, predictions may also include `y_pred_log10`.

### Comparing Results Across Conditions

    # Extract key parameters for each condition
    coef_alone <- coef(fit_alone)
    Q0_alone <- coef_alone$estimate[coef_alone$term == "q0"]
    Alpha_alone <- coef_alone$estimate[coef_alone$term == "alpha"]

    coef_own <- coef(fit_own)
    Q0_own <- coef_own$estimate[coef_own$term == "q0"]
    Alpha_own <- coef_own$estimate[coef_own$term == "alpha"]

    comparison <- data.frame(
        Condition = c("Alone (Target)", "Own (Target)", "Alt (Cross-Price)"),
        Q0_or_Qalone = c(
            Q0_alone,
            Q0_own,
            coef(fit_alt)["qalone"]
        ),
        Alpha_or_I = c(
            Alpha_alone,
            Alpha_own,
            coef(fit_alt)["I"]
        )
    )

    comparison
    #>           Condition Q0_or_Qalone  Alpha_or_I
    #> 1    Alone (Target)     23.54818  0.01406589
    #> 2      Own (Target)     21.19336  0.01562876
    #> 3 Alt (Cross-Price)           NA -0.37126092

Interpretation:

- Comparing `Q0` between alone and own conditions shows how the presence of an alternative affects baseline consumption of the target commodity
- The `I` parameter from the cross-price model indicates whether the products are substitutes (I < 0) or complements (I > 0)

### Combined Visualization

    # Create prediction data
    x_seq <- seq(0.01, max(cp$x), length.out = 100)

    # Get demand predictions for each condition
    pred_alone <- predict(fit_alone, newdata = data.frame(x = x_seq))$.fitted
    pred_own <- predict(fit_own, newdata = data.frame(x = x_seq))$.fitted

    # Cross-price model predictions (always on the natural y scale)
    pred_alt <- predict(fit_alt, newdata = data.frame(x = x_seq))$y_pred

    # Combine into plot data
    plot_data <- data.frame(
        x = rep(x_seq, 3),
        y = c(pred_alone, pred_own, pred_alt),
        Condition = rep(c("Target (Alone)", "Target (Own)", "Alternative (Cross-Price)"), each = length(x_seq))
    )

    # Plot
    ggplot() +
        geom_point(data = cp, aes(x = x, y = y, color = target), alpha = 0.6) +
        geom_line(data = plot_data, aes(x = x, y = y, color = Condition), linewidth = 1) +
        scale_x_log10() +
        scale_y_log10() +
        labs(
            x = "Target Price",
            y = "Consumption",
            title = "Cross-Price Analysis: All Conditions",
            color = "Condition"
        ) +
        theme_bw()

## Checking Unsystematic Data

    etm |>
        dplyr::filter(group %in% "E-Cigarettes" & id %in% 1)
    #> # A tibble: 6 × 5
    #>      id     x     y target group
    #>   <dbl> <dbl> <dbl> <chr>  <chr>
    #> 1     1     2     3 alt    E-Cigarettes
    #> 2     1     4     5 alt    E-Cigarettes
    #> 3     1     8     5 alt    E-Cigarettes
    #> 4     1    16    16 alt    E-Cigarettes
    #> 5     1    32    17 alt    E-Cigarettes
    #> 6     1    64    13 alt    E-Cigarettes

    unsys_one <- etm |>
        filter(group %in% "E-Cigarettes" & id %in% 1) |>
        check_systematic_cp()

    unsys_one$results
    #> # A tibble: 1 × 15
    #>   id    type  trend_stat trend_threshold trend_direction trend_pass bounce_stat
    #>   <chr> <chr>      <dbl>           <dbl> <chr>           <lgl>            <dbl>
    #> 1 1     cp            NA           0.025 up              NA                 0.2
    #> # 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>

    unsys_one_lnic <- lnic |>
        filter(
            target == "adjusting",
            id == "R_00Q12ahGPKuESBT"
        ) |>
        check_systematic_cp()

    unsys_one_lnic$results
    #> # A tibble: 1 × 15
    #>   id     type  trend_stat trend_threshold trend_direction trend_pass bounce_stat
    #>   <chr>  <chr>      <dbl>           <dbl> <chr>           <lgl>            <dbl>
    #> 1 R_00Q… cp            NA           0.025 down            NA                   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>

    unsys_all <- etm |>
        group_by(id, group) |>
        nest() |>
        mutate(
            sys = map(data, check_systematic_cp),
            results = map(sys, ~ dplyr::select(.x$results, -id))
        ) |>
        select(-data, -sys) |>
        unnest(results)

    knitr::kable(
        unsys_all |>
            group_by(group) |>
            summarise(
                n_subjects = n(),
                pct_systematic = round(mean(systematic, na.rm = TRUE) * 100, 1),
                .groups = "drop"
            ),
        caption = "Systematicity check by product group (ETM dataset)"
    )

Systematicity check by product group (ETM dataset)
group | n_subjects | pct_systematic
--- | --- | ---
Cigarettes | 10 | 90
Combustibles | 10 | 70
E-Cigarettes | 10 | 80
Non-Combustibles | 10 | 90

### Demonstration from Rzeszutek et al. (2025)

#### Low Nicotine Study (Kaplan et al., 2018)

    unsys_all_lnic <- lnic |>
        filter(target == "fixed") |>
        group_by(id, condition) |>
        nest() |>
        mutate(
            sys = map(
                data,
                check_systematic_cp
            )
        ) |>
        mutate(results = map(sys, ~ dplyr::select(.x$results, -id))) |>
        select(-data, -sys) |>
        unnest(results) |>
        arrange(id)

    knitr::kable(
        unsys_all_lnic |>
            group_by(condition) |>
            summarise(
                n_subjects = n(),
                pct_systematic = round(mean(systematic, na.rm = TRUE) * 100, 1),
                .groups = "drop"
            ),
        caption = "Systematicity check by condition (Low Nicotine study, Kaplan et al., 2018)"
    )

Systematicity check by condition (Low Nicotine study, Kaplan et al., 2018)
condition | n_subjects | pct_systematic
--- | --- | ---
100% | 67 | 97.0
2% | 57 | 91.2
2% NegFrame | 59 | 91.5

#### Unpublished Cannabis and Cigarette Data

    unsys_all_can_cig <- can_cig |>
        filter(target %in% c("cannabisFix", "cigarettesFix")) |>
        group_by(id, target) |>
        nest() |>
        mutate(
            sys = map(data, check_systematic_cp),
            results = map(sys, ~ dplyr::select(.x$results, -id))
        ) |>
        select(-data, -sys) |>
        unnest(results) |>
        arrange(id)

    knitr::kable(
        unsys_all_can_cig |>
            group_by(target) |>
            summarise(
                n_subjects = n(),
                pct_systematic = round(mean(systematic, na.rm = TRUE) * 100, 1),
                .groups = "drop"
            ),
        caption = "Systematicity check by target (Cannabis/Cigarettes, unpublished data)"
    )

Systematicity check by target (Cannabis/Cigarettes, unpublished data)
target | n_subjects | pct_systematic
--- | --- | ---
cannabisFix | 99 | 67.7
cigarettesFix | 99 | 77.8

#### In Progress Experimental Tobacco Marketplace Data

    unsys_all_ongoing_etm <- ongoing_etm |>
        # one person is doubled up
        distinct() |>
        filter(target %in% c("FixCig", "ECig")) |>
        group_by(id, target) |>
        nest() |>
        mutate(
            sys = map(data, check_systematic_cp),
            results = map(sys, ~ dplyr::select(.x$results, -id))
        ) |>
        select(-data, -sys) |>
        unnest(results)

    knitr::kable(
        unsys_all_ongoing_etm |>
            group_by(target) |>
            summarise(
                n_subjects = n(),
                pct_systematic = round(mean(systematic, na.rm = TRUE) * 100, 1),
                .groups = "drop"
            ),
        caption = "Systematicity check by target (Ongoing ETM data)"
    )

Systematicity check by target (Ongoing ETM data)
target | n_subjects | pct_systematic
--- | --- | ---
ECig | 47 | 83.0
FixCig | 47 | 89.4

## Nonlinear Model Fitting

### Two Stage

    fit_one <- etm |>
        dplyr::filter(group %in% "E-Cigarettes" & id %in% 1) |>
        fit_cp_nls(
            equation = "exponentiated",
            return_all = TRUE
        )

    summary(fit_one)
    #> Cross-Price Demand Model Summary
    #> ================================
    #>
    #> Model Specification:
    #> Equation type: exponentiated
    #> Functional form: y ~ (10^log10_qalone) * 10^(I * exp(-(10^log10_beta) * x))
    #> Fitting method: nls_multstart
    #> Method details: Multiple starting values optimization with nls.multstart
    #>
    #> Coefficients:
    #>               Estimate Std. Error t value  Pr(>|t|)
    #> log10_qalone  1.194135   0.060311 19.7995 0.0002815 ***
    #> I            -1.268376   0.837302 -1.5148 0.2270524
    #> log10_beta   -0.737728   0.265129 -2.7825 0.0688459 .
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    #>
    #> Confidence Intervals:
    #>               2.5 % 97.5 %
    #> log10_qalone  1.002  1.386
    #> I            -3.933  1.396
    #> log10_beta   -1.581  0.106
    #>
    #> Fit Statistics:
    #> R-squared: 0.8597
    #> AIC: 34.06
    #> BIC: 33.23
    #>
    #> Parameter Interpretation (natural scale):
    #> qalone (Q_alone): 15.64  - consumption at zero alternative price
    #> I: -1.268  - interaction parameter (substitution direction)
    #> beta: 0.1829  - sensitivity parameter (sensitivity of relation to price)
    #>
    #> Optimizer parameters (log10 scale):
    #> log10_qalone: 1.194
    #> log10_beta: -0.7377

    plot(fit_one, x_trans = "log10")

    fit_all <- etm |>
        group_by(id, group) |>
        nest() |>
        mutate(
            unsys = map(data, check_systematic_cp),
            fit = map(data, fit_cp_nls, equation = "exponentiated", return_all = TRUE),
            summary = map(fit, summary),
            plot = map(fit, plot, x_trans = "log10"),
            glance = map(fit, glance),
            tidy = map(fit, tidy)
        )

    # Show parameter estimates for first 3 subjects only
    knitr::kable(
        fit_all |>
            slice(1:3) |>
            unnest(tidy) |>
            select(id, group, term, estimate, std.error),
        digits = 3,
        caption = "Example parameter estimates (first 3 subjects)"
    )

Example parameter estimates (first 3 subjects)
id | group | term | estimate | std.error
--- | --- | --- | --- | ---
1 | Cigarettes | log10_qalone | -4.515900e+01 | 2.267248e+03
1 | Cigarettes | I | -2.979000e+00 | 2.266886e+03
1 | Cigarettes | log10_beta | -3.111000e+00 | 3.393980e+02
1 | Combustibles | log10_qalone | 3.010000e-01 | 0.000000e+00
1 | Combustibles | I | -5.212610e+02 | 3.023230e+02
1 | Combustibles | log10_beta | 5.720000e-01 | 3.400000e-02
1 | E-Cigarettes | log10_qalone | 1.194000e+00 | 6.000000e-02
1 | E-Cigarettes | I | -1.268000e+00 | 8.370000e-01
1 | E-Cigarettes | log10_beta | -7.380000e-01 | 2.650000e-01
1 | Non-Combustibles | log10_qalone | -4.521400e+01 | 4.216897e+04
1 | Non-Combustibles | I | -2.963000e+00 | 4.216867e+04
1 | Non-Combustibles | log10_beta | -3.742000e+00 | 6.217747e+03
2 | Cigarettes | log10_qalone | -3.078160e+02 | 1.915342e+06
2 | Cigarettes | I | 3.082850e+02 | 1.915342e+06
2 | Cigarettes | log10_beta | -4.310000e+00 | 2.700805e+03
2 | Combustibles | log10_qalone | -4.830000e-01 | 2.381000e+00
2 | Combustibles | I | 9.950000e-01 | 2.271000e+00
2 | Combustibles | log10_beta | -1.527000e+00 | 1.663000e+00
2 | E-Cigarettes | log10_qalone | -3.077080e+02 | 1.133752e+06
2 | E-Cigarettes | I | 3.082790e+02 | 1.133752e+06
2 | E-Cigarettes | log10_beta | -4.397000e+00 | 1.598611e+03
2 | Non-Combustibles | log10_qalone | -3.080510e+02 | 5.758916e+06
2 | Non-Combustibles | I | 3.082640e+02 | 5.758916e+06
2 | Non-Combustibles | log10_beta | -4.831000e+00 | 8.117098e+03
3 | Cigarettes | log10_qalone | 1.000000e+00 | 0.000000e+00
3 | Cigarettes | I | -4.017660e+02 | 2.325640e+02
3 | Cigarettes | log10_beta | -3.290000e-01 | 3.400000e-02
3 | Combustibles | log10_qalone | -4.481500e+01 | 3.020400e+03
3 | Combustibles | I | -1.428000e+00 | 3.020061e+03
3 | Combustibles | log10_beta | -3.171000e+00 | 9.393800e+02
3 | E-Cigarettes | log10_qalone | -4.501600e+01 | 5.324700e+01
3 | E-Cigarettes | I | -2.466000e+00 | 5.244800e+01
3 | E-Cigarettes | log10_beta | -2.244000e+00 | 1.166900e+01
3 | Non-Combustibles | log10_qalone | -4.510200e+01 | 2.997900e+01
3 | Non-Combustibles | I | -2.269000e+00 | 2.892100e+01
3 | Non-Combustibles | log10_beta | -2.094000e+00 | 7.884000e+00
4 | Cigarettes | log10_qalone | -4.395200e+01 | 5.640785e+03
4 | Cigarettes | I | -2.957000e+00 | 5.640440e+03
4 | Cigarettes | log10_beta | -3.308000e+00 | 8.422170e+02
4 | Combustibles | log10_qalone | 1.447000e+00 | 5.400000e-02
4 | Combustibles | I | -3.174723e+03 | 1.313616e+06
4 | Combustibles | log10_beta | 1.200000e-02 | 2.183300e+01
4 | E-Cigarettes | log10_qalone | -4.491600e+01 | 7.298490e+02
4 | E-Cigarettes | I | -2.872000e+00 | 7.294550e+02
4 | E-Cigarettes | log10_beta | -2.865000e+00 | 1.157390e+02
4 | Non-Combustibles | log10_qalone | -4.533800e+01 | 1.974971e+04
4 | Non-Combustibles | I | -1.332000e+00 | 1.974939e+04
4 | Non-Combustibles | log10_beta | -3.577000e+00 | 6.498072e+03
5 | Cigarettes | log10_qalone | 0.000000e+00 | 0.000000e+00
5 | Cigarettes | I | 0.000000e+00 | 0.000000e+00
5 | Cigarettes | log10_beta | -7.120000e+00 | 6.800000e-02
5 | Combustibles | log10_qalone | 1.448000e+00 | 0.000000e+00
5 | Combustibles | I | -6.810000e+00 | 1.150000e-01
5 | Combustibles | log10_beta | 1.800000e-02 | 3.000000e-03
5 | E-Cigarettes | log10_qalone | 9.030000e-01 | 0.000000e+00
5 | E-Cigarettes | I | -1.486004e+11 | 4.754863e+10
5 | E-Cigarettes | log10_beta | 1.070000e+00 | 6.000000e-03
5 | Non-Combustibles | log10_qalone | 1.724000e+00 | 0.000000e+00
5 | Non-Combustibles | I | -8.603600e+02 | 4.971870e+02
5 | Non-Combustibles | log10_beta | 7.000000e-02 | 2.700000e-02
6 | Cigarettes | log10_qalone | -4.504800e+01 | 8.240780e+02
6 | Cigarettes | I | -2.277000e+00 | 8.237050e+02
6 | Cigarettes | log10_beta | -2.892000e+00 | 1.642750e+02
6 | Combustibles | log10_qalone | -4.540600e+01 | 2.709070e+02
6 | Combustibles | I | -2.843000e+00 | 2.704450e+02
6 | Combustibles | log10_beta | -2.643000e+00 | 4.490300e+01
6 | E-Cigarettes | log10_qalone | 3.160000e-01 | 4.600000e-02
6 | E-Cigarettes | I | -4.890000e-01 | 1.680000e-01
6 | E-Cigarettes | log10_beta | -9.410000e-01 | 2.580000e-01
6 | Non-Combustibles | log10_qalone | -4.525000e+01 | 6.354410e+02
6 | Non-Combustibles | I | -2.619000e+00 | 6.350490e+02
6 | Non-Combustibles | log10_beta | -2.835000e+00 | 1.108870e+02
7 | Cigarettes | log10_qalone | 3.010000e-01 | 0.000000e+00
7 | Cigarettes | I | -2.154420e+02 | 1.342600e+01
7 | Cigarettes | log10_beta | -6.870000e-01 | 4.000000e-03
7 | Combustibles | log10_qalone | 1.082000e+00 | 5.900000e-01
7 | Combustibles | I | -5.340000e-01 | 4.990000e-01
7 | Combustibles | log10_beta | -1.639000e+00 | 1.047000e+00
7 | E-Cigarettes | log10_qalone | -4.466600e+01 | 4.022285e+03
7 | E-Cigarettes | I | -2.971000e+00 | 4.021935e+03
7 | E-Cigarettes | log10_beta | -3.235000e+00 | 5.996800e+02
7 | Non-Combustibles | log10_qalone | -4.536000e+01 | 9.208600e+03
7 | Non-Combustibles | I | -2.277000e+00 | 9.208267e+03
7 | Non-Combustibles | log10_beta | -3.413000e+00 | 1.779549e+03
8 | Cigarettes | log10_qalone | -4.510600e+01 | 4.086164e+04
8 | Cigarettes | I | -2.664000e+00 | 4.086134e+04
8 | Cigarettes | log10_beta | -3.735000e+00 | 6.701295e+03
8 | Combustibles | log10_qalone | 3.082040e+02 | 3.013474e+05
8 | Combustibles | I | -3.083770e+02 | 3.013469e+05
8 | Combustibles | log10_beta | -4.245000e+00 | 4.253780e+02
8 | E-Cigarettes | log10_qalone | 4.770000e-01 | 8.400000e-02
8 | E-Cigarettes | I | -8.805930e+02 | 4.930348e+05
8 | E-Cigarettes | log10_beta | -2.700000e-02 | 3.233800e+01
8 | Non-Combustibles | log10_qalone | 4.810000e-01 | 3.500000e-02
8 | Non-Combustibles | I | -2.251000e+00 | 7.430000e-01
8 | Non-Combustibles | log10_beta | -1.077000e+00 | 9.900000e-02
9 | Cigarettes | log10_qalone | -4.524400e+01 | 2.270866e+04
9 | Cigarettes | I | -2.910000e+00 | 2.270833e+04
9 | Cigarettes | log10_beta | -3.608000e+00 | 3.417431e+03
9 | Combustibles | log10_qalone | 3.010000e-01 | 0.000000e+00
9 | Combustibles | I | -1.630526e+11 | 5.225423e+10
9 | Combustibles | log10_beta | 7.700000e-01 | 6.000000e-03
9 | E-Cigarettes | log10_qalone | 6.520000e-01 | 1.170000e-01
9 | E-Cigarettes | I | -4.460000e-01 | 1.180000e-01
9 | E-Cigarettes | log10_beta | -1.387000e+00 | 3.800000e-01
9 | Non-Combustibles | log10_qalone | -4.419500e+01 | 4.062960e+02
9 | Non-Combustibles | I | -2.695000e+00 | 4.058770e+02
9 | Non-Combustibles | log10_beta | -2.736000e+00 | 6.985300e+01
10 | Cigarettes | log10_qalone | -4.541400e+01 | 7.800100e+01
10 | Cigarettes | I | -2.402000e+00 | 7.738400e+01
10 | Cigarettes | log10_beta | -2.352000e+00 | 1.664800e+01
10 | Combustibles | log10_qalone | 1.279000e+00 | 0.000000e+00
10 | Combustibles | I | -6.525461e+03 | 3.788643e+03
10 | Combustibles | log10_beta | 6.440000e-01 | 2.900000e-02
10 | E-Cigarettes | log10_qalone | 0.000000e+00 | 0.000000e+00
10 | E-Cigarettes | I | 0.000000e+00 | 0.000000e+00
10 | E-Cigarettes | log10_beta | -4.892000e+00 | 9.500000e-02
10 | Non-Combustibles | log10_qalone | 1.439000e+00 | 1.400000e-02
10 | Non-Combustibles | I | -8.492300e+01 | 1.419300e+02
10 | Non-Combustibles | log10_beta | 3.090000e-01 | 1.500000e-01

    # Show one example plot
    fit_all$plot[[2]]

### Fit to Group (pooled by group)

    fit_pooled <- etm |>
        group_by(group) |>
        nest() |>
        mutate(
            unsys = map(data, check_systematic_cp),
            fit = map(data, fit_cp_nls, equation = "exponentiated", return_all = TRUE),
            summary = map(fit, summary),
            plot = map(fit, plot, x_trans = "log10"),
            glance = map(fit, glance),
            tidy = map(fit, tidy)
        )

    # Show tidy results instead of summary
    knitr::kable(
        fit_pooled |>
            unnest(tidy) |>
            select(group, term, estimate, std.error),
        digits = 3,
        caption = "Pooled model parameter estimates by product group"
    )

Pooled model parameter estimates by product group
group | term | estimate | std.error
--- | --- | --- | ---
Cigarettes | log10_qalone | 0.098 | 0.205
Cigarettes | I | -0.868 | 1.251
Cigarettes | log10_beta | -0.938 | 0.827
Combustibles | log10_qalone | 0.969 | 0.098
Combustibles | I | -0.661 | 0.683
Combustibles | log10_beta | -0.743 | 0.579
E-Cigarettes | log10_qalone | 0.540 | 0.105
E-Cigarettes | I | -0.661 | 0.735
E-Cigarettes | log10_beta | -0.742 | 0.622
Non-Combustibles | log10_qalone | 0.924 | 0.134
Non-Combustibles | I | -4.148 | 19.320
Non-Combustibles | log10_beta | -0.271 | 0.904

    # Show one plot example
    fit_pooled |>
        dplyr::filter(group == "E-Cigarettes") |>
        dplyr::pull(plot) |>
        pluck(1)

### Fit to Group (mean)

    fit_mean <- etm |>
        group_by(group, x) |>
        summarise(
            y = mean(y),
            .groups = "drop"
        ) |>
        group_by(group) |>
        nest() |>
        mutate(
            unsys = map(data, check_systematic_cp),
            fit = map(data, fit_cp_nls, equation = "exponentiated", return_all = TRUE),
            summary = map(fit, summary),
            plot = map(fit, plot, x_trans = "log10"),
            glance = map(fit, glance),
            tidy = map(fit, tidy)
        )

    # Show tidy results
    knitr::kable(
        fit_mean |>
            unnest(tidy) |>
            select(group, term, estimate, std.error),
        digits = 3,
        caption = "Mean model parameter estimates by product group"
    )

Mean model parameter estimates by product group
group | term | estimate | std.error
--- | --- | --- | ---
Cigarettes | log10_qalone | 0.098 | 0.074
Cigarettes | I | -0.868 | 0.453
Cigarettes | log10_beta | -0.938 | 0.299
Combustibles | log10_qalone | 0.969 | 0.031
Combustibles | I | -0.661 | 0.218
Combustibles | log10_beta | -0.743 | 0.185
E-Cigarettes | log10_qalone | 0.540 | 0.052
E-Cigarettes | I | -0.661 | 0.367
E-Cigarettes | log10_beta | -0.742 | 0.310
Non-Combustibles | log10_qalone | 0.924 | 0.007
Non-Combustibles | I | -4.148 | 0.992
Non-Combustibles | log10_beta | -0.271 | 0.046

    # Show parameter estimates plot
    fit_mean |>
        unnest(cols = c(glance, tidy)) |>
        select(
            group,
            term,
            estimate
        ) |>
        ggplot(aes(x = group, y = estimate, group = term)) +
        geom_bar(stat = "identity") +
        geom_point() +
        facet_wrap(~term, ncol = 1, scales = "free_y")

    # Show one example plot
    fit_mean |>
        dplyr::filter(group %in% "E-Cigarettes") |>
        dplyr::pull(plot) |>
        pluck(1)

## Linear Model Fitting

    fit_one_linear <- etm |>
        dplyr::filter(group %in% "E-Cigarettes" & id %in% 1) |>
        fit_cp_linear(
            type = "fixed",
            log10x = TRUE,
            return_all = TRUE
        )

    summary(fit_one_linear)
    #> Linear Cross-Price Demand Model Summary
    #> =======================================
    #>
    #> Formula: y ~ log10(x)
    #> Method: lm
    #>
    #> Coefficients:
    #>             Estimate Std. Error t value Pr(>|t|)
    #> (Intercept)  0.13333    3.55773  0.0375  0.97190
    #> log10(x)     9.20649    3.03472  3.0337  0.03864 *
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    #>
    #> R-squared: 0.697   Adjusted R-squared: 0.6213
    plot(fit_one_linear, x_trans = "log10")

## Linear Mixed-Effects Model

    fit_mixed <- fit_cp_linear(
        etm,
        type = "mixed",
        log10x = TRUE,
        group_effects = "interaction",
        random_slope = FALSE,
        return_all = TRUE
    )

    summary(fit_mixed)
    #> Mixed-Effects Linear Cross-Price Demand Model Summary
    #> ====================================================
    #>
    #> Formula: y ~ log10(x) * group + (1 | id)
    #> Method: lmer
    #>
    #> Fixed Effects:
    #>                                Estimate Std. Error t value
    #> (Intercept)                    -0.10000    2.66378 -0.0375
    #> log10(x)                        0.80675    1.85305  0.4354
    #> groupCombustibles               2.09333    3.07225  0.6814
    #> groupE-Cigarettes               0.98667    3.07225  0.3212
    #> groupNon-Combustibles           0.14667    3.07225  0.0477
    #> log10(x):groupCombustibles      3.83445    2.62060  1.4632
    #> log10(x):groupE-Cigarettes      0.78777    2.62060  0.3006
    #> log10(x):groupNon-Combustibles  4.76459    2.62060  1.8181
    #>
    #> Random Effects:
    #>     Group        Term Variance  Std.Dev       NA
    #>        id (Intercept)     <NA> 23.76351 4.874783
    #>  Residual        <NA>     <NA> 54.45409 7.379301
    #>
    #> Model Fit:
    #> R2 (marginal): 0.1121   [Fixed effects only]
    #> R2 (conditional): 0.3819   [Fixed + random effects]
    #> AIC: 1655
    #> BIC: 1690
    #>
    #> Note: R2 values for mixed models are approximate.

    # plot fixed effects only
    plot(fit_mixed, x_trans = "log10", pred_type = "fixed")

    # plot random effects only
    plot(fit_mixed, x_trans = "log10", pred_type = "random")

    # plot both fixed and random effects
    plot(fit_mixed, x_trans = "log10", pred_type = "all")

## Extracting Model Coefficients

    glance(fit_one)
    #> # A tibble: 1 × 6
    #>   r.squared   aic   bic equation      method        transform
    #>       <dbl> <dbl> <dbl> <chr>         <chr>         <chr>
    #> 1     0.860  34.1  33.2 exponentiated nls_multstart none
    tidy(fit_one)
    #> # A tibble: 3 × 5
    #>   term         estimate std.error statistic  p.value
    #>   <chr>           <dbl>     <dbl>     <dbl>    <dbl>
    #> 1 log10_qalone    1.19     0.0603     19.8  0.000282
    #> 2 I              -1.27     0.837      -1.51 0.227
    #> 3 log10_beta     -0.738    0.265      -2.78 0.0688

    extract_coefficients(fit_mixed)
    #> $fixed
    #>                    (Intercept)                       log10(x)
    #>                     -0.1000000                      0.8067540
    #>              groupCombustibles              groupE-Cigarettes
    #>                      2.0933333                      0.9866667
    #>          groupNon-Combustibles     log10(x):groupCombustibles
    #>                      0.1466667                      3.8344541
    #>     log10(x):groupE-Cigarettes log10(x):groupNon-Combustibles
    #>                      0.7877715                      4.7645940
    #>
    #> $random
    #> $id
    #>    (Intercept)
    #> 1   -1.0155373
    #> 2   -2.0805203
    #> 3   -2.6890820
    #> 4    0.1255158
    #> 5   11.0796263
    #> 6   -3.3356788
    #> 7   -2.3087309
    #> 8   -2.4608713
    #> 9   -2.7651522
    #> 10   5.4504307
    #>
    #> with conditional variances for "id"
    #>
    #> $combined
    #> $id
    #>    (Intercept) log10(x) groupCombustibles groupE-Cigarettes
    #> 1  -1.11553732 0.806754          2.093333         0.9866667
    #> 2  -2.18052028 0.806754          2.093333         0.9866667
    #> 3  -2.78908198 0.806754          2.093333         0.9866667
    #> 4   0.02551585 0.806754          2.093333         0.9866667
    #> 5  10.97962630 0.806754          2.093333         0.9866667
    #> 6  -3.43567877 0.806754          2.093333         0.9866667
    #> 7  -2.40873092 0.806754          2.093333         0.9866667
    #> 8  -2.56087134 0.806754          2.093333         0.9866667
    #> 9  -2.86515219 0.806754          2.093333         0.9866667
    #> 10  5.35043065 0.806754          2.093333         0.9866667
    #>    groupNon-Combustibles log10(x):groupCombustibles log10(x):groupE-Cigarettes
    #> 1              0.1466667                   3.834454                  0.7877715
    #> 2              0.1466667                   3.834454                  0.7877715
    #> 3              0.1466667                   3.834454                  0.7877715
    #> 4              0.1466667                   3.834454                  0.7877715
    #> 5              0.1466667                   3.834454                  0.7877715
    #> 6              0.1466667                   3.834454                  0.7877715
    #> 7              0.1466667                   3.834454                  0.7877715
    #> 8              0.1466667                   3.834454                  0.7877715
    #> 9              0.1466667                   3.834454                  0.7877715
    #> 10             0.1466667                   3.834454                  0.7877715
    #>    log10(x):groupNon-Combustibles
    #> 1                        4.764594
    #> 2                        4.764594
    #> 3                        4.764594
    #> 4                        4.764594
    #> 5                        4.764594
    #> 6                        4.764594
    #> 7                        4.764594
    #> 8                        4.764594
    #> 9                        4.764594
    #> 10                       4.764594
    #>
    #> attr(,"class")
    #> [1] "coef.mer"

## Post-hoc Estimated Marginal Means and Comparisons

    cp_posthoc_slopes(fit_mixed)
    #> Slope Estimates and Comparisons
    #> ===============================
    #>
    #> Estimated Marginal Means:
    #>  group               x.trend         SE  df    lower.CL   upper.CL
    #>  Cigarettes       0.01665965 0.03826583 223 -0.05874926 0.09206855
    #>  Combustibles     0.09584197 0.03826583 223  0.02043307 0.17125088
    #>  E-Cigarettes     0.03292730 0.03826583 223 -0.04248160 0.10833621
    #>  Non-Combustibles 0.11504957 0.03826583 223  0.03964066 0.19045847
    #>
    #> Degrees-of-freedom method: kenward-roger
    #> Confidence level used: 0.95
    #>
    #> Significant interaction: No
    #>
    #> No significant interaction detected (alpha = 0.05 ). Pairwise slope comparisons not performed.
    #> P-value adjustment method: tukey
    cp_posthoc_intercepts(fit_mixed)
    #> Intercept Estimates and Comparisons
    #> ===================================
    #>
    #> Estimated Marginal Means:
    #>  group                emmean       SE    df  lower.CL upper.CL
    #>  Cigarettes       -0.1000000 2.663776 59.69 -5.428913 5.228913
    #>  Combustibles      1.9933333 2.663776 59.69 -3.335580 7.322246
    #>  E-Cigarettes      0.8866667 2.663776 59.69 -4.442246 6.215580
    #>  Non-Combustibles  0.0466667 2.663776 59.69 -5.282246 5.375580
    #>
    #> Degrees-of-freedom method: kenward-roger
    #> Confidence level used: 0.95
    #>
    #> Significant interaction: No
    #>
    #> No significant interaction detected (alpha = 0.05 ). Pairwise intercept comparisons not performed.
    #> P-value adjustment method: tukey

## See Also

- `vignette("beezdemand")` – Getting started with beezdemand
- `vignette("model-selection")` – Choosing the right model class for your data
- `vignette("group-comparisons")` – Extra sum-of-squares F-test for group comparisons
- `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("migration-guide")` – Migrating from `FitCurves()`