diff --git a/R/sim_all.R b/R/sim_all.R
index 6b071dcf70fe0048d5603b2893ff68602b5c6f5d..e38cf7c5512b433915b90ce5233286d0467f2705 100644
--- a/R/sim_all.R
+++ b/R/sim_all.R
@@ -2,13 +2,12 @@
#' update
#' @param nosim Number of runs or simulations. For testing use 2 but once you go serious, use at least 200, for better results use 2000.
#' @param resps Number of respondents you want to simulate
-#' @param destype Is it a design created with ngene or with spdesign. Ngene desings should be stored as the standard .ngd output. spdesign should be the spdesign object design$design
+#' @inheritParams readdesign
#' @param designpath The path to the folder where the designs are stored. For example "c:/myfancydec/Designs"
#' @param u A list with utility functions. The list can incorporate as many decision rule groups as you want. However, each group must be in a list in this list. If you just use one group (the normal), this group still has to be in a list in the u list. As a convention name beta coefficients starting with a lower case "b"
#' @param bcoeff List of initial coefficients for the utility function. List content/length can vary based on application, but should all begin with b and be the same as those entered in the utility functions
-#' @param decisiongroups A vector showing how decision groups are numerically distributed
-#' @param manipulations A variable to alter terms of the utility functions examples may be applying a factor or applying changes to terms selectively for different groups
-#'
+#' @inheritParams sim_choice
+#' @inheritParams simulate_choices
#' @return A list, with all information on the simulation. This list an be easily processed by the user and in the rmarkdown template.
#' @export
#'
@@ -28,7 +27,7 @@
#' bheight2=0.25,
#' bheight3=0.50)
#'
-sim_all <- function(nosim=2, resps, destype=NULL, designpath, u, bcoeff, decisiongroups = c(0,1), manipulations = list()){
+sim_all <- function(nosim=2, resps, destype=NULL, designpath, u, bcoeff, decisiongroups = c(0,1), manipulations = list(), estimate = TRUE){
#################################################
########## Input Validation Test ###############
diff --git a/R/sim_choice.R b/R/sim_choice.R
index d36170164d9b1f87f98d1613f0725f8eb5c563e0..45cd91f9ed4b5555b65e9e6bcaa51b8341929f89 100644
--- a/R/sim_choice.R
+++ b/R/sim_choice.R
@@ -1,29 +1,19 @@
-
-
-
-
-
-
-
-
-#' Title
+#' Simulate and estimate choices
#'
#' @param designfile path to a file containing a design.
#' @param no_sim Number of runs i.e. how often do you want the simulation to be repeated
#' @param respondents Number of respondents. How many respondents do you want to simulate in each run.
#' @param ut The first element of the utility function list
-#' @param destype Specify which type of design you use. Either ngene or spdesign
-#' @param bcoefficients List of initial coefficients for the utility function. List content/length can vary based on application, but should all begin with b and be the same as those entered in the utility functions
-#' @param decisiongroups A vector showing how decision groups are numerically distributed
-#' @param manipulations A variable to alter terms of the utility functions examples may be applying a factor or applying changes to terms selectively for different groups
-#'
+#' @inheritParams readdesign
+#' @param estimate If TRUE models will be estimated. If false only a dataset will be simulated. Default is true
+#' @inheritParams simulate_choices
#' @return a list with all information on the run
#' @export
#'
#' @examples \dontrun{ simchoice(designfile="somefile", no_sim=10, respondents=330,
#' mnl_U,ut=u[[1]] ,destype="ngene")}
#'
-sim_choice <- function(designfile, no_sim=10, respondents=330,ut ,destype=destype, bcoefficients, decisiongroups=c(0,1), manipulations = list()) {
+sim_choice <- function(designfile, no_sim=10, respondents=330,ut ,destype=destype, bcoefficients, decisiongroups=c(0,1), manipulations = list() , estimate = TRUE) {
@@ -103,6 +93,10 @@ designs_all <- list()
database <- simulate_choices(data=datadet, utility = ut, setspp = setpp, bcoefficients = bcoefficients, decisiongroups = decisiongroups, manipulations = manipulations)
+### start estimation
+
+ if (estimate==TRUE) {
+
# specify model for mixl estimation
@@ -145,6 +139,7 @@ designs_all <- list()
return(output)
+} else return(database)
}
diff --git a/R/simulate_choices.R b/R/simulate_choices.R
index 9676bd378ab6c3d0fc043052ba7f0869d5d9ef20..3ccdaf29346cbbb82a38a4345a8afabf002bb6a1 100644
--- a/R/simulate_choices.R
+++ b/R/simulate_choices.R
@@ -3,12 +3,11 @@
#' @param data a dataframe that includes a design repeated for the number of observations
#' @param utility a list with the utility functions, one utility function for each alternatives
#' @param setspp an integer, the number of choice sets per person
-#' @param destype Is it a design created with ngene or with spdesign. Ngene designs should be stored as the standard .ngd output. spdesign should be the spdesign object design$design
-#' @return a dataframe that includes simulated choices and a design
+#' @inheritParams readdesign
#' @param bcoefficients List of initial coefficients for the utility function. List content/length can vary based on application, but should all begin with b and be the same as those entered in the utility functions
#' @param decisiongroups A vector showing how decision groups are numerically distributed
#' @param manipulations A variable to alter terms of the utility functions examples may be applying a factor or applying changes to terms selectively for different groups
-#'
+#' @return a dataframe that includes simulated choices and a design
#' @export
#'
#' @examples \dontrun{simulate_choices(datadet, ut,setspp)}
@@ -25,10 +24,8 @@ simulate_choices <- function(data, utility, setspp, destype, bcoefficients, deci
by_formula <- function(equation){ #used to take formulas as inputs in simulation utility function
- # //! cur_data_all may get deprecated in favor of pick
- dplyr::pick(dplyr::everything()) |>
- #cur_data_all() |>
- dplyr::transmute(!!formula.tools::lhs(equation) := !!formula.tools::rhs(equation) )
+ dplyr::pick(dplyr::everything()) |>
+ dplyr::transmute(!!formula.tools::lhs(equation) := !!formula.tools::rhs(equation) )
}
# Here one can add additional case-specific data
@@ -50,7 +47,7 @@ simulate_choices <- function(data, utility, setspp, destype, bcoefficients, deci
if(exists("final_set")) data = dplyr::left_join(data,final_set, by = "ID")
- cat("\n decisiongroups exists: " ,exists("decisiongroups"))
+ cat("\n decisiongroups exists: " ,length(decisiongroups)>2)
if(length(decisiongroups)>2) { ### create a new variable to classify decision groups.
diff --git a/man/sim_all.Rd b/man/sim_all.Rd
index 4fd1b2cd5e494685d7916a2b5e6c51ac55ee517a..6cca772af0cb8992e547a8a4929bea68298a9c42 100644
--- a/man/sim_all.Rd
+++ b/man/sim_all.Rd
@@ -13,7 +13,8 @@ sim_all(
u,
bcoeff,
decisiongroups = c(0, 1),
- manipulations = list()
+ manipulations = list(),
+ estimate = TRUE
)
}
\arguments{
@@ -21,8 +22,6 @@ sim_all(
\item{resps}{Number of respondents you want to simulate}
-\item{destype}{Is it a design created with ngene or with spdesign. Ngene desings should be stored as the standard .ngd output. spdesign should be the spdesign object design$design}
-
\item{designpath}{The path to the folder where the designs are stored. For example "c:/myfancydec/Designs"}
\item{u}{A list with utility functions. The list can incorporate as many decision rule groups as you want. However, each group must be in a list in this list. If you just use one group (the normal), this group still has to be in a list in the u list. As a convention name beta coefficients starting with a lower case "b"}
@@ -32,6 +31,8 @@ sim_all(
\item{decisiongroups}{A vector showing how decision groups are numerically distributed}
\item{manipulations}{A variable to alter terms of the utility functions examples may be applying a factor or applying changes to terms selectively for different groups}
+
+\item{estimate}{If TRUE models will be estimated. If false only a dataset will be simulated. Default is true}
}
\value{
A list, with all information on the simulation. This list an be easily processed by the user and in the rmarkdown template.
diff --git a/man/sim_choice.Rd b/man/sim_choice.Rd
index 7e85f8a14a39f2cb06777461d02e30dc608d7966..5dbfddcb825c74a0af917f58ccb0e03a94b884cc 100644
--- a/man/sim_choice.Rd
+++ b/man/sim_choice.Rd
@@ -2,7 +2,7 @@
% Please edit documentation in R/sim_choice.R
\name{sim_choice}
\alias{sim_choice}
-\title{Title}
+\title{Simulate and estimate choices}
\usage{
sim_choice(
designfile,
@@ -12,7 +12,8 @@ sim_choice(
destype = destype,
bcoefficients,
decisiongroups = c(0, 1),
- manipulations = list()
+ manipulations = list(),
+ estimate = TRUE
)
}
\arguments{
@@ -24,19 +25,19 @@ sim_choice(
\item{ut}{The first element of the utility function list}
-\item{destype}{Specify which type of design you use. Either ngene or spdesign}
-
\item{bcoefficients}{List of initial coefficients for the utility function. List content/length can vary based on application, but should all begin with b and be the same as those entered in the utility functions}
\item{decisiongroups}{A vector showing how decision groups are numerically distributed}
\item{manipulations}{A variable to alter terms of the utility functions examples may be applying a factor or applying changes to terms selectively for different groups}
+
+\item{estimate}{If TRUE models will be estimated. If false only a dataset will be simulated. Default is true}
}
\value{
a list with all information on the run
}
\description{
-Title
+Simulate and estimate choices
}
\examples{
\dontrun{ simchoice(designfile="somefile", no_sim=10, respondents=330,
diff --git a/man/simulate_choices.Rd b/man/simulate_choices.Rd
index cf76d0a035103dfc3d9e13acf002a9e9faef3bdc..db94ad023faaca4954ecd12acb75737db4a04bdf 100644
--- a/man/simulate_choices.Rd
+++ b/man/simulate_choices.Rd
@@ -21,8 +21,6 @@ simulate_choices(
\item{setspp}{an integer, the number of choice sets per person}
-\item{destype}{Is it a design created with ngene or with spdesign. Ngene designs should be stored as the standard .ngd output. spdesign should be the spdesign object design$design}
-
\item{bcoefficients}{List of initial coefficients for the utility function. List content/length can vary based on application, but should all begin with b and be the same as those entered in the utility functions}
\item{decisiongroups}{A vector showing how decision groups are numerically distributed}