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 log_k logsigma logsigma logsigma_e rho_raw
#> 1.8736539 -5.8010932 0.8954500 -0.9527944 -0.7797945 -1.9498223 -0.4674928
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 -0.614 10.1 0.00164 13.4 44.1 FALSE
#> 2 30 -0.831 0.442 2.84 0.00471 16.6 15.4 FALSE
#> 3 38 -0.359 0.128 4.55 0.00344 14.2 21.0 FALSE
#> 4 60 0.394 0.111 9.66 0.00338 6.78 21.4 FALSE
#> 5 68 0.451 -0.317 10.2 0.00220 9.83 32.8 FALSE
#> 6 106 -0.149 0.486 5.61 0.00492 8.02 14.7 FALSE
#> 7 113 -0.0358 -0.596 6.28 0.00167 21.1 43.4 FALSE
#> 8 142 -0.0509 -0.450 6.19 0.00193 18.6 37.5 FALSE
#> 9 156 0.227 0.203 8.17 0.00371 7.31 19.5 FALSE
#> 10 188 -0.0470 0.710 6.21 0.00615 5.79 11.7 FALSE
coef(fit, type = "fixed") # fixed-effect coefficients
#> # A tibble: 1 × 2
#> `Q0:(Intercept)` `alpha:(Intercept)`
#> <dbl> <dbl>
#> 1 1.87 -5.80
# }