Provides a summary of a fitted hurdle demand model, including fixed effects, variance components, correlations, and fit statistics.
Arguments
- object
An object of class
beezdemand_hurdlefromfit_demand_hurdle.- report_space
Character. Reporting space for core demand parameters. One of:
"internal": report internal/fitting parameters (default internal naming)"natural": report natural-scale parameters when a natural mapping exists"log10": reportlog10()-scale parameters when a mapping exists
- ...
Additional arguments (currently unused).
Value
An object of class summary.beezdemand_hurdle (also inherits
from beezdemand_summary) containing:
- call
The original function call
- model_class
"beezdemand_hurdle"
- backend
"TMB"
- coefficients
Tibble of fixed effects with estimates, SEs, z-values, p-values
- coefficients_matrix
Matrix form for printing (legacy compatibility)
- variance_components
Matrix of variance/covariance estimates
- correlations
Matrix of correlation estimates
- n_subjects
Number of subjects
- nobs
Number of observations
- converged
Logical indicating convergence
- logLik
Log-likelihood at convergence
- AIC
Akaike Information Criterion
- BIC
Bayesian Information Criterion
- group_metrics
Group-level Pmax and Omax
- individual_metrics
Summary of individual-level parameters
- notes
Character vector of warnings/notes
Examples
# \donttest{
data(apt)
fit <- fit_demand_hurdle(apt, y_var = "y", x_var = "x", id_var = "id")
#> Sample size may be too small for reliable estimation.
#> Subjects: 10, Parameters: 12, Recommended minimum: 60 subjects.
#> Consider using more subjects or the simpler 2-RE model.
#> Fitting HurdleDemand3RE model...
#> Part II: zhao_exponential
#> Subjects: 10, Observations: 160
#> Fixed parameters: 12, Random effects per subject: 3
#> Optimizing...
#> Converged in 81 iterations
#> Computing standard errors...
#> Done. Log-likelihood: 32.81
summary(fit)
#>
#> Two-Part Mixed Effects Hurdle Demand Model
#> ============================================
#>
#> Call:
#> fit_demand_hurdle(data = apt, y_var = "y", x_var = "x", id_var = "id")
#>
#> Convergence: Yes
#> Number of subjects: 10
#> Number of observations: 160
#> Random effects: 3 (zeros, q0, alpha)
#>
#> Fixed Effects:
#> --------------
#> Estimate Std. Error t value
#> beta0 -293.94893 160.41399 -1.832
#> beta1 104.07743 61.07262 1.704
#> log_q0 1.87220 0.12435 15.056
#> log_k 1.83359 0.56794 3.228
#> log_alpha -4.04181 0.65639 -6.158
#> logsigma_a 4.88805 1.34747 3.628
#> logsigma_b -0.95269 0.22912 -4.158
#> logsigma_c -0.79237 0.23839 -3.324
#> logsigma_e -1.95094 0.06294 -30.998
#> rho_ab_raw 0.18315 0.22247 0.823
#> rho_ac_raw 0.23874 0.27580 0.866
#> rho_bc_raw 0.40454 0.32560 1.242
#>
#> Variance Components:
#> --------------------
#> Estimate Std. Error
#> alpha 0.0176 0.0115
#> k 6.2563 3.5532
#> var_a 17607.7218 47451.7806
#> var_b 0.1488 0.0682
#> var_c 0.2050 0.0977
#> cov_ab 9.2703 7.6244
#> cov_ac 14.0774 11.5420
#> cov_bc 0.0715 0.0627
#> var_e 0.0202 0.0025
#>
#> Correlations:
#> -------------
#> Estimate Std. Error
#> rho_ab 0.1811 0.2152
#> rho_ac 0.2343 0.2607
#> rho_bc 0.4094 0.2735
#>
#> Model Fit:
#> ----------
#> Log-likelihood: 32.81
#> AIC: -41.63
#> BIC: -4.73
#>
#> Demand Metrics (Group-Level):
#> -----------------------------
#> Pmax (price at max expenditure): 11.0485
#> Omax (max expenditure): 23.8281
#> Q at Pmax: 2.1567
#> Elasticity at Pmax: -1.0000
#> Method: analytic_lambert_w_hurdle
#>
#> Derived Parameters (Individual-Level Summary):
#> ----------------------------------------------
#> Q0 (Intensity):
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 2.828 5.758 6.236 6.962 9.247 10.209
#> Alpha:
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.00934 0.01240 0.01733 0.01948 0.02624 0.03388
#> Breakpoint:
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 7.593 12.357 15.064 16.995 22.276 27.643
#> Pmax:
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 5.729 7.404 11.483 11.986 15.742 20.780
#> Omax:
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 11.78 16.42 21.14 26.14 36.37 44.19
#>
# }