Get Pairwise Comparisons for Demand Parameters — get_demand

Conducts pairwise comparisons for Q0 and/or alpha parameters from a beezdemand_nlme model across levels of specified factors. Comparisons are performed on the log10 scale of the parameters. Results include estimates of differences (on log10 scale) and optionally, ratios (on the natural scale by applying 10^difference).

Usage

get_demand_comparisons(
  fit_obj,
  params_to_compare = c("Q0", "alpha"),
  compare_specs = NULL,
  contrast_type = "pairwise",
  contrast_by = NULL,
  adjust = "tukey",
  at = NULL,
  ci_level = 0.95,
  report_ratios = TRUE,
  ...
)

Arguments

fit_obj: A beezdemand_nlme object.
params_to_compare: Character vector: "Q0", "alpha", or c("Q0", "alpha"). Default c("Q0", "alpha").
compare_specs: A formula specifying the factors whose levels are to be included in the EMM calculation prior to contrasting. This defines the "cells" of your design for EMMs. E.g., ~ factor1 (EMMs for levels of factor1, averaging over others), ~ factor1 * factor2 (EMMs for all cells of factor1 x factor2). If NULL, it defaults to an interaction of all factors in fit_obj$param_info$factors.
contrast_type: Character string specifying the type of contrast (passed to method in emmeans::contrast). Commonly "pairwise", "revpairwise", "eff", "consec", "poly". Default "pairwise".
contrast_by: Optional character vector of factor names to condition the contrasts by (passed to by in emmeans::contrast). If NULL (default), contrasts are performed over the primary terms implied by compare_specs and contrast_type. Example: If compare_specs = ~ dose * drug, contrast_type = "pairwise", and contrast_by = "dose", this will perform pairwise comparisons of drug levels within each level of dose. Note: If the original fit_obj model is additive for the factors involved (i.e., no interaction term was fitted), specifying contrast_by will result in identical contrast estimates across the levels of the contrast_by variable(s). In such cases, consider analyzing main effects directly (e.g., compare_specs = ~drug, contrast_by = NULL).
adjust: P-value adjustment method. Default "tukey".
at: Optional named list for emmeans::ref_grid().
ci_level: Confidence level. Default 0.95.
report_ratios: Logical. If TRUE, reports contrasts as ratios. Default TRUE.
...: Additional arguments passed to emmeans::emmeans() or emmeans::contrast().

Value

A list named by parameter. Each element contains:

emmeans: Tibble of EMMs (log10 scale) with CIs.
contrasts_log10: Tibble of comparisons (log10 differences) with CIs and p-values.
contrasts_ratio: (If report_ratios=TRUE and successful) Tibble of comparisons as ratios (natural scale), with CIs for ratios.

S3 class beezdemand_comparison is assigned.

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_comparisons(fit)
#> Using default 'compare_specs': ~ dose for EMMs.
#> 
#> --- Processing comparisons for parameter: Q0 ---
#> Note: adjust = "tukey" was changed to "sidak"
#> because "tukey" is only appropriate for one set of pairwise comparisons
#> Note: adjust = "tukey" was changed to "sidak"
#> because "tukey" is only appropriate for one set of pairwise comparisons
#> 
#> --- Processing comparisons for parameter: alpha ---
#> Note: adjust = "tukey" was changed to "sidak"
#> because "tukey" is only appropriate for one set of pairwise comparisons
#> Note: adjust = "tukey" was changed to "sidak"
#> because "tukey" is only appropriate for one set of pairwise comparisons
#> Demand Parameter Comparisons (from beezdemand_nlme fit)
#> EMMs computed over: ~dose 
#> Contrast type: pairwise
#> P-value adjustment method: tukey 
#> ================================================== 
#> 
# }