Extract coefficients from a fitted beezdemand_tmb model. The
type argument selects the return shape. The default,
"internal", is unchanged: a named numeric vector of the
optimizer's flat parameterization (entries include beta_q0,
beta_alpha, logsigma*, and any covariance
hyperparameters; intercepts are on the log scale because the optimizer
works in unconstrained space). This is the numeric-vector escape hatch
consumed by tooling such as car::deltaMethod and
multcomp::glht.
Value
For type = "internal", a named numeric vector. For
type = "subject"/"combined", a tibble with one row per
subject (or one row per subject-by-within-id-factor-level cell when
the fit has within-id factor variation). For type = "fixed", a
one-row tibble of fixed-effect coefficients.
Details
type = "subject" (alias "combined") returns the
per-subject parameter tibble from get_subject_pars (with
expanded = NULL, so within-id factor expansion is auto-detected).
This is concept-parity with coef.beezdemand_nlme(type = "combined")
but not column-identical: it returns resolved per-subject parameters
(Q0, alpha, ...), not a per-design-term coefficient
matrix. type = "fixed" returns a one-row tibble of the
fixed-effect coefficients only (the beta_q0 / beta_alpha
block on the internal parameterization), excluding log_k,
logsigma*, and rho*.
Scale conversion is not performed here: supplying report_space
through ... is an error. Use get_subject_pars or
predict.beezdemand_tmb for natural-scale parameters.
Examples
# \donttest{
data(apt)
fit <- fit_demand_tmb(apt, equation = "exponential", verbose = 0)
#> equation='exponential': Dropped 14 zero-consumption observations (146 remaining).
coef(fit) # raw optimizer vector (default, "internal")
#> beta_q0 beta_alpha logsigma logsigma logsigma_e rho_raw
#> 1.8799653 -5.5720123 -0.9506009 -0.7771868 -1.9469323 -0.4593234
coef(fit, type = "subject") # per-subject parameter tibble
#> # A tibble: 10 × 8
#> id b_i c_i Q0 alpha Pmax Omax pmax_at_bound
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl>
#> 1 19 0.435 -0.612 10.1 0.00206 13.9 44.1 FALSE
#> 2 30 -0.830 0.444 2.86 0.00593 17.1 15.3 FALSE
#> 3 38 -0.363 0.117 4.56 0.00428 14.9 21.3 FALSE
#> 4 60 0.396 0.116 9.74 0.00427 6.98 21.3 FALSE
#> 5 68 0.450 -0.322 10.3 0.00276 10.2 33.0 FALSE
#> 6 106 -0.153 0.477 5.63 0.00613 8.42 14.8 FALSE
#> 7 113 -0.0392 -0.604 6.30 0.00208 22.2 43.8 FALSE
#> 8 142 -0.0472 -0.444 6.25 0.00244 19.0 37.3 FALSE
#> 9 156 0.231 0.212 8.26 0.00470 7.48 19.4 FALSE
#> 10 188 -0.0475 0.716 6.25 0.00778 5.97 11.7 FALSE
coef(fit, type = "fixed") # fixed-effect coefficients
#> # A tibble: 1 × 2
#> `Q0:(Intercept)` `alpha:(Intercept)`
#> <dbl> <dbl>
#> 1 1.88 -5.57
# }