Extract Random Effects from TMB Model

Returns subject-level random effect deviations on the natural (log) scale. These are the Cholesky-transformed deviations, not standardized scores. To obtain the standardized random effects (u matrix), access object$tmb_obj directly.

Usage

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

Arguments

object

A beezdemand_tmb object.

...

Additional arguments.

Value

Data frame with subject-level random effects. Columns:

• id — subject identifier

• b_i, c_i (when present) — first-column convenience aliases for q0_(Intercept) and alpha_(Intercept). Preserved for backward compatibility with older callers.

• q0_<term> — per-block random-effect coefficients for log-Q0, one column per random-effects design column from the parsed block structure. For factor-expanded or multi-block fits, these expose the per-condition slope REs that b_i / c_i alone do not surface.

• alpha_<term> — analogous columns for log-alpha.

Examples

# \donttest{
data(apt)
fit <- fit_demand_tmb(apt, equation = "exponential", verbose = 0)
#>   equation='exponential': Dropped 14 zero-consumption observations (146 remaining).
head(nlme::ranef(fit))
#>    id        b_i        c_i q0_(Intercept) alpha_(Intercept)
#> 1  19  0.4347399 -0.6136355      0.4347399        -0.6136355
#> 2  30 -0.8308979  0.4420229     -0.8308979         0.4420229
#> 3  38 -0.3588356  0.1280378     -0.3588356         0.1280378
#> 4  60  0.3938846  0.1112281      0.3938846         0.1112281
#> 5  68  0.4514144 -0.3174826      0.4514144        -0.3174826
#> 6 106 -0.1487057  0.4857013     -0.1487057         0.4857013
# }