Check Demand Model Diagnostics

Performs diagnostic checks on fitted demand models, returning information about convergence, boundary conditions, and residual patterns.

Usage

check_demand_model(object, ...)

# S3 method for class 'beezdemand_hurdle'
check_demand_model(object, ...)

# S3 method for class 'beezdemand_nlme'
check_demand_model(object, ...)

# S3 method for class 'beezdemand_fixed'
check_demand_model(object, ...)

# S3 method for class 'beezdemand_tmb'
check_demand_model(object, ...)

Arguments

object: A fitted model object of class beezdemand_hurdle, beezdemand_nlme, or beezdemand_fixed.
...: Additional arguments passed to methods.

Value

An object of class beezdemand_diagnostics containing:

convergence: List with convergence status and messages
boundary: List with boundary condition warnings. For beezdemand_tmb fits it also carries k_identification: NULL unless k was estimated as a free parameter, otherwise the screen described in Details.
residuals: Summary statistics for residuals
random_effects: Summary of random effects (if applicable). variances holds random-effect variances: on the log10 scale for beezdemand_tmb fits, and as reported by nlme::VarCorr() for beezdemand_nlme fits. beezdemand_tmb fits also carry sd_log10 (the corresponding standard deviations, matching summary(fit)$variance_components) and sd_internal_log (the raw natural-log-scale standard deviations used by the near-zero degeneracy check).
issues: Character vector of identified issues
recommendations: Character vector of recommendations

Details

The function checks for:

Convergence status and optimization messages
Parameters at or near boundaries
Residual patterns (heteroscedasticity, outliers)
Random effect variance estimates near zero
For fit_demand_tmb() fits with estimate_k = TRUE, whether the fit shows the signature of an unidentified k: an implausible k, a decay exponent alpha * Q0 * price that never leaves its linear regime over the observed prices, a degenerate alpha, or log_k resting on a user-supplied bound. This is a screen, not a formal test: no flag means nothing was detected, not that k is identified.
Correlation matrices near singularity

Note

This function is named check_demand_model() to avoid potential conflicts with performance::check_model() from the performance package.

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
diagnostics <- check_demand_model(fit)
print(diagnostics)
#> 
#> Model Diagnostics
#> ================================================== 
#> Model class: beezdemand_hurdle 
#> 
#> Convergence:
#>   Status: Converged
#> 
#> Random Effects:
#> 
#> Residuals:
#>   Mean: 0.01173
#>   SD: 0.9277
#>   Range: [-2.862, 3.312]
#>   Outliers: 3 observations
#> 
#> --------------------------------------------------
#> Issues Detected (1):
#>   1. Detected 3 potential outliers (|resid| > 3)
#> 
#> Recommendations:
#>   - Investigate outlying observations
# }