Get Estimated Marginal Means for Demand Parameters

Calculates Estimated Marginal Means (EMMs) for Q0 and alpha parameters from a beezdemand_nlme model for all combinations of specified factor levels. Reports parameters on both their estimation scale (log10) and their natural, back-transformed scale. Optionally includes Essential Value (EV).

Usage

get_demand_param_emms(
  fit_obj,
  factors_in_emm = NULL,
  at = NULL,
  ci_level = 0.95,
  include_ev = FALSE,
  ...
)

Arguments

fit_obj

A beezdemand_nlme object.

factors_in_emm

Character vector of factor names to compute EMMs over. Defaults to all factors present in the fit_obj.

at

Optional named list specifying levels of conditioning variables for emmeans::ref_grid().

ci_level

Confidence level for the EMMs (default 0.95).

include_ev

Logical. If TRUE, calculates and includes Essential Value (EV) derived from alpha, along with its confidence interval (calculated by back-transforming the CI of alpha_param_log10). Default FALSE.

...

Additional arguments passed to emmeans::emmeans().

Value

A tibble containing:

Factor levels

Columns for each factor in factors_in_emm.

Q0_param_log10, alpha_param_log10

EMMs for the model parameters (log10 scale) with their respective confidence intervals (LCL_Q0_param, UCL_Q0_param, etc.).

Q0_natural, alpha_natural

EMMs back-transformed to the natural scale (10^param) with their respective confidence intervals (LCL_Q0_natural, UCL_Q0_natural, etc.).

EV, LCL_EV, UCL_EV

(If include_ev=TRUE) Essential Value and its CI.

Examples

# \donttest{
data(ko, package = "beezdemand")
ko$y_ll4 <- ll4(ko$y, lambda = 4)
fit <- fit_demand_mixed(ko, y_var = "y_ll4", x_var = "x",
  id_var = "monkey", factors = "dose", equation_form = "zben")
#> Generating starting values using method: 'heuristic'
#> Using heuristic method for starting values.
#> --- Fitting NLME Model ---
#> Equation Form: zben
#> Param Space: log10
#> NLME Formula: y_ll4 ~ Q0 * exp(-(10^alpha/Q0) * (10^Q0) * x)
#> Start values (first few): Q0_int=2.27, alpha_int=-3
#> Number of fixed parameters: 10 (Q0: 5, alpha: 5)
get_demand_param_emms(fit)
#> # A tibble: 5 × 13
#>   dose  Q0_param_log10 LCL_Q0_param_log10 UCL_Q0_param_log10 Q0_natural
#>   <fct>          <dbl>              <dbl>              <dbl>      <dbl>
#> 1 3e-05           2.58               2.35               2.80      377. 
#> 2 1e-04           2.38               2.23               2.52      238. 
#> 3 3e-04           2.21               2.10               2.32      163. 
#> 4 0.001           1.91               1.78               2.03       80.5
#> 5 0.003           1.90               1.73               2.07       79.7
#> # ℹ 8 more variables: LCL_Q0_natural <dbl>, UCL_Q0_natural <dbl>,
#> #   alpha_param_log10 <dbl>, LCL_alpha_param_log10 <dbl>,
#> #   UCL_alpha_param_log10 <dbl>, alpha_natural <dbl>, LCL_alpha_natural <dbl>,
#> #   UCL_alpha_natural <dbl>
# }