Get Estimated Marginal Means for Observed Factor Combinations

This function is a wrapper around get_demand_param_emms. It first calls get_demand_param_emms to calculate Estimated Marginal Means (EMMs) for Q0 and alpha parameters over all combinations of the specified factor levels. It then filters these results to return EMMs only for the combinations of factor levels that were actually present in the original dataset used to fit the beezdemand_nlme model.

Usage

get_observed_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 returned by fit_demand_mixed().

factors_in_emm

Character vector of factor names to compute EMMs over. Defaults to all factors present in the fit_obj. These factors define the grid over which EMMs are initially calculated and then filtered.

at

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

ci_level

Confidence level for the EMMs (default 0.95). Passed to get_demand_param_emms.

include_ev

Logical. If TRUE, calculates and includes Essential Value (EV) derived from alpha. Passed to get_demand_param_emms. Default FALSE.

...

Additional arguments passed to get_demand_param_emms and subsequently to emmeans::emmeans().

Value

A tibble similar to the output of get_demand_param_emms, but filtered to include only rows corresponding to factor level combinations that were observed in the original fit_obj$data. Contains:

Factor levels

Columns for each factor in factors_in_emm.

Q0_param_log10, alpha_param_log10

EMMs for model parameters (log10 scale) and CIs.

Q0_natural, alpha_natural

EMMs back-transformed to natural scale and CIs.

EV, LCL_EV, UCL_EV

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

See also

get_demand_param_emms

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_observed_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>
# }