Merge pull request #19 from jhelvy/orthogonal

Orthogonal

CRAN-SUBMISSION

@@ -1,4 +1,3 @@
-Version: 0.3.4
-Date: 2023-06-13 18:09:36 UTC
-SHA:
-     dcb6aa72d97c124bb6194b5a5334c698d7062eb3
+Version: 0.4.0
+Date: 2023-06-30 18:30:53 UTC
+SHA: 2daf0161a16357e90aea1fa42e0c452226a4f3fc

DESCRIPTION

@@ -1,14 +1,14 @@
 Package: cbcTools
-Title: Design and Evaluate Choice-Based Conjoint Survey Experiments
-Version: 0.3.4
+Title: A Simulation-Based Workflow to Design and Evaluate Choice-Based Conjoint Survey Experiments
+Version: 0.4.0
 Maintainer: John Helveston <[email protected]>
 Authors@R: c(
     person(given   = "John",
            family  = "Helveston",
            role    = c("cre", "aut", "cph"),
            email   = "[email protected]",
            comment = c(ORCID = "0000-0002-2657-9191")))
-Description: Design and evaluate choice-based conjoint survey experiments in R. Generate survey designs, including randomized designs and Bayesian D-efficient designs as well as designs with "no choice" options and labeled designs. Conveniently inspect the design balance and overlap, and simulate choice data for a survey design either randomly or according to a multinomial or mixed logit utility model defined by user-provided prior parameters. Conduct power analyses on a survey design by estimating the same model multiple times using different subsets of the data to simulate different sample sizes. Choice simulation and model estimation are handled using the 'logitr' package, and Bayesian D-efficient designs are obtained using the 'idefix' package. For more details see Helveston (2023) <doi:10.18637/jss.v105.i10> and Traets et al (2020) <doi:10.18637/jss.v096.i03>.
+Description: A simulation-based workflow to design and evaluate choice-based conjoint survey experiments. Generate a variety of survey designs, including full factorial designs, orthogonal designs, and Bayesian D-efficient designs as well as designs with "no choice" options and "labeled" (also known as "alternative specific") designs. Full factorial and orthogonal designs are obtained using the 'DoE.base' package (Grömping, 2018) <doi:10.18637/jss.v085.i05>. Bayesian D-efficient designs are obtained using the 'idefix' package (Traets et al, 2020) <doi:10.18637/jss.v096.i03>. Conveniently inspect the design balance and overlap, and simulate choice data for a survey design either randomly or according to a multinomial or mixed logit utility model defined by user-provided prior parameters. Conduct a power analysis for a given survey design by estimating the same model on different subsets of the data to simulate different sample sizes. Choice simulation and model estimation in power analyses are handled using the 'logitr' package (Helveston, 2023) <doi:10.18637/jss.v105.i10>.
 License: MIT + file LICENSE
 Encoding: UTF-8
 LazyData: true
@@ -20,7 +20,7 @@ Suggests:
     testthat,
     tibble
 Imports:
-    dplyr,
+    DoE.base,
     fastDummies,
     ggplot2,
     idefix,

NAMESPACE

@@ -9,11 +9,13 @@ export(cbc_overlap)
 export(cbc_power)
 export(cbc_profiles)
 export(cbc_restrict)
+export(plot_compare_power)
 export(randLN)
 export(randN)
 importFrom(ggplot2,aes)
 importFrom(ggplot2,element_blank)
 importFrom(ggplot2,expand_limits)
+importFrom(ggplot2,facet_wrap)
 importFrom(ggplot2,geom_hline)
 importFrom(ggplot2,geom_point)
 importFrom(ggplot2,ggplot)

R/choices.R

@@ -1,7 +1,9 @@
 #' Simulate choices for a survey design
 #'
 #' Simulate choices for a survey design, either randomly or according to a
-#' utility model defined by user-provided prior parameters.
+#' utility model defined by user-provided prior parameters. All choices are
+#' simulated using the 'logitr' package. For more details see the JSS article
+#' on the 'logitr' package (Helveston, 2023).
 #' @keywords logitr mnl mxl mixed logit simulation
 #'
 #' @param design A data frame of a survey design.
@@ -12,6 +14,9 @@
 #' If `NULL` (the default), choices will be randomly assigned.
 #' @param n_draws The number of Halton draws to use for simulated choices
 #' for mixed logit models. Defaults to `100`.
+#' @references
+#' Helveston, J. P. (2023). logitr: Fast Estimation of Multinomial and Mixed Logit Models with Preference Space and Willingness-to-Pay Space Utility Parameterizations. Journal of Statistical Software, 105(10), 1–37,
+#' \doi{10.18637/jss.v105.i10}
 #' @return Returns the `design` data frame with an additional `choice` column
 #' identifying the simulated choices.
 #' @export
@@ -27,12 +32,13 @@
 #'   freshness = c('Poor', 'Average', 'Excellent')
 #' )
 #'
-#' # Make a randomized survey design
+#' # Make a survey design from all possible profiles
+#' # (This is the default setting where method = 'full' for "full factorial")
 #' design <- cbc_design(
 #'   profiles = profiles,
 #'   n_resp   = 300, # Number of respondents
-#'   n_alts   = 3, # Number of alternatives per question
-#'   n_q      = 6 # Number of questions per respondent
+#'   n_alts   = 3,   # Number of alternatives per question
+#'   n_q      = 6    # Number of questions per respondent
 #' )
 #'
 #' # Simulate random choices