Convenience function to extract subject-specific demand parameters from
a fitted hurdle demand model. Equivalent to accessing object$subject_pars.
Value
Data frame with subject-specific parameters including:
- id
Subject identifier
- a_i
Random effect for Part I (zeros)
- b_i
Random effect for Part II (Q0)
- c_i
Random effect for alpha (3-RE model only)
- Q0
Subject-specific intensity (consumption at price 0)
- alpha
Subject-specific elasticity
- breakpoint
Price where P(quit) = 0.5
- Pmax
Price at maximum expenditure
- Omax
Maximum expenditure
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
pars <- get_subject_pars(fit)
head(pars)
#> id a_i b_i c_i Q0 alpha breakpoint Pmax
#> 1 19 -24.18572 0.4327276 -0.1848782 10.023473 0.01460071 21.256530 13.292153
#> 2 30 -51.52766 -0.8325815 -0.3938517 2.828144 0.01184726 27.643280 16.381413
#> 3 38 18.03621 -0.3566930 -0.2240901 4.551737 0.01403927 14.167670 13.823717
#> 4 60 41.85352 0.3917919 0.4987260 9.621440 0.02892408 11.269526 6.709803
#> 5 68 23.55110 0.4511080 0.1328454 10.209411 0.02006133 13.436440 9.674080
#> 6 106 82.94144 -0.1452772 0.3451208 5.623335 0.02480560 7.593382 7.823835
#> Omax
#> 1 44.18881
#> 2 15.36567
#> 3 20.86896
#> 4 21.41158
#> 5 32.75738
#> 6 14.59192
# }