diff --git a/DESCRIPTION b/DESCRIPTION
index 7b8f8dad70b7cf9e4322f5850f31e9462b4f5b8a..310da68825b7258f35eaa2a4b588de34703b7b8f 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
Package: simulateDCE
Title: Simulate data for discrete choice experiments
-Version: 0.1.0
+Version: 0.1.1
Authors@R:
person("Julian", "Sagebiel", , "julian.sagebiel@idiv.de", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-0253-6875"))
@@ -20,6 +20,7 @@ Imports:
psych,
purrr,
readr,
+ rlang,
rmarkdown,
stringr,
tibble,
diff --git a/R/sim_all.R b/R/sim_all.R
index 9d6b6b7fb43311f94dca6003b6427c22fb8c9fe4..c4a8a5be2fac005677d11d9cd6f8f40d7f794824 100644
--- a/R/sim_all.R
+++ b/R/sim_all.R
@@ -5,7 +5,8 @@
#' @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
#' @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 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 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
#'
#' @return A list, with all information on the simulation. This list an be easily processed by the user and in the rmarkdown template.
#' @export
@@ -16,7 +17,8 @@
#' resps =240 # number of respondents
#' nosim=2 # number of simulations to run (about 500 is minimum)
#'
-#' bcoeff <-list(bsq=0.00, # hypothesized beta coefficients for individual terms of the utility function
+#'
+#' bcoeff <-list(bsq=0.00,
#' bredkite=-0.05,
#' bdistance=0.50,
#' bcost=-0.05,
@@ -25,7 +27,7 @@
#' bheight2=0.25,
#' bheight3=0.50)
#'
-sim_all <- function(nosim=2, resps, destype="ngene", designpath, u, bcoeff){
+sim_all <- function(nosim=2, resps, destype="ngene", designpath, u, bcoeff, decisiongroups = c(0,1)){
#################################################
########## Input Validation Test ###############
@@ -47,6 +49,9 @@ sim_all <- function(nosim=2, resps, destype="ngene", designpath, u, bcoeff){
stop("Argument 'bcoeff' must be a list.")
}
+ if (length(u) != length(decisiongroups) -1){
+ stop("Number of decision groups must equal number of utility functions!")
+ }
# Check if values in bcoeff are numeric
if (!all(sapply(bcoeff, is.numeric))) {
stop("Values in 'bcoeff' must be numeric.")
@@ -91,7 +96,7 @@ sim_all <- function(nosim=2, resps, destype="ngene", designpath, u, bcoeff){
tictoc::tic()
all_designs<- purrr::map(designfile, sim_choice,
- no_sim= nosim,respondents = resps, destype=destype, ut=u, bcoefficients = bcoeff) %>% ## iterate simulation over all designs
+ no_sim= nosim,respondents = resps, destype=destype, ut=u, bcoefficients = bcoeff, decisiongroups = decisiongroups) %>% ## iterate simulation over all designs
stats::setNames(designname)
diff --git a/R/sim_choice.R b/R/sim_choice.R
index d3612746a34adefcead307002cd05b0cbaa27d2e..45296f88e31fb1266c2c31414c7eb412e2e43a99 100644
--- a/R/sim_choice.R
+++ b/R/sim_choice.R
@@ -13,6 +13,8 @@
#' @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
#'
#' @return a list with all information on the run
#' @export
@@ -20,7 +22,7 @@
#' @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) {
+sim_choice <- function(designfile, no_sim=10, respondents=330,ut ,destype=destype, bcoefficients, decisiongroups=c(0,1)) {
@@ -38,7 +40,7 @@ sim_choice <- function(designfile, no_sim=10, respondents=330,ut ,destype=destyp
cat("This is Run number ", run)
- database <- simulate_choices(datadet, utility = ut, setspp=setpp, bcoefficients = bcoefficients)
+ database <- simulate_choices(datadet, utility = ut, setspp=setpp, bcoefficients = bcoefficients, decisiongroups = decisiongroups)
cat("This is the utility functions \n" , mnl_U)
@@ -85,6 +87,8 @@ designs_all <- list()
replications <- respondents/nblocks
+ ## if replications is non int, assign unevenly
+
##browser()
datadet<- design %>%
dplyr::arrange(Block,Choice.situation) %>%
@@ -95,7 +99,7 @@ designs_all <- list()
as.data.frame()
- database <- simulate_choices(data=datadet, utility = ut, setspp = setpp, bcoefficients = bcoefficients)
+ database <- simulate_choices(data=datadet, utility = ut, setspp = setpp, bcoefficients = bcoefficients, decisiongroups = decisiongroups)
diff --git a/R/simulate_choices.R b/R/simulate_choices.R
index 5cb9a34be0e5d1c537cfbafcf25931dc78a78aa9..0e7fbd4d8aee83352b4b7d18796ae52685094088 100644
--- a/R/simulate_choices.R
+++ b/R/simulate_choices.R
@@ -5,10 +5,13 @@
#' @param setspp an integer, the number of choice sets per person
#' @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
#' @return a dataframe that includes simulated choices and a design
+#' @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
+#'
#' @export
#'
#' @examples \dontrun{simulate_choices(datadet, ut,setspp)}
-simulate_choices <- function(data, utility, setspp, destype, bcoefficients) { #the part in dataset that needs to be repeated in each run
+simulate_choices <- function(data, utility, setspp, destype, bcoefficients, decisiongroups = c(0,1)) { #the part in dataset that needs to be repeated in each run
diff --git a/README.html b/README.html
deleted file mode 100644
index 693b6d348258fd09e62476844730cc11a3aba3b6..0000000000000000000000000000000000000000
--- a/README.html
+++ /dev/null
@@ -1,1487 +0,0 @@
-<!DOCTYPE html>
-
-<html xmlns="http://www.w3.org/1999/xhtml">
-
-<head>
-
-<meta charset="utf-8">
-<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
-<meta name="generator" content="pandoc" />
-<meta name="viewport" content="width=device-width, initial-scale=1">
-
-<style type="text/css">
-@font-face {
-font-family: octicons-link;
-src: url(data:font/woff;charset=utf-8;base64,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) format('woff');
-}
-body {
--webkit-text-size-adjust: 100%;
-text-size-adjust: 100%;
-color: #333;
-font-family: "Helvetica Neue", Helvetica, "Segoe UI", Arial, freesans, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";
-font-size: 16px;
-line-height: 1.6;
-word-wrap: break-word;
-}
-a {
-background-color: transparent;
-}
-a:active,
-a:hover {
-outline: 0;
-}
-strong {
-font-weight: bold;
-}
-h1 {
-font-size: 2em;
-margin: 0.67em 0;
-}
-img {
-border: 0;
-}
-hr {
-box-sizing: content-box;
-height: 0;
-}
-pre {
-overflow: auto;
-}
-code,
-kbd,
-pre {
-font-family: monospace, monospace;
-font-size: 1em;
-}
-input {
-color: inherit;
-font: inherit;
-margin: 0;
-}
-html input[disabled] {
-cursor: default;
-}
-input {
-line-height: normal;
-}
-input[type="checkbox"] {
-box-sizing: border-box;
-padding: 0;
-}
-table {
-border-collapse: collapse;
-border-spacing: 0;
-}
-td,
-th {
-padding: 0;
-}
-* {
-box-sizing: border-box;
-}
-input {
-font: 13px / 1.4 Helvetica, arial, nimbussansl, liberationsans, freesans, clean, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";
-}
-a {
-color: #4078c0;
-text-decoration: none;
-}
-a:hover,
-a:active {
-text-decoration: underline;
-}
-hr {
-height: 0;
-margin: 15px 0;
-overflow: hidden;
-background: transparent;
-border: 0;
-border-bottom: 1px solid #ddd;
-}
-hr:before {
-display: table;
-content: "";
-}
-hr:after {
-display: table;
-clear: both;
-content: "";
-}
-h1,
-h2,
-h3,
-h4,
-h5,
-h6 {
-margin-top: 15px;
-margin-bottom: 15px;
-line-height: 1.1;
-}
-h1 {
-font-size: 30px;
-}
-h2 {
-font-size: 21px;
-}
-h3 {
-font-size: 16px;
-}
-h4 {
-font-size: 14px;
-}
-h5 {
-font-size: 12px;
-}
-h6 {
-font-size: 11px;
-}
-blockquote {
-margin: 0;
-}
-ul,
-ol {
-padding: 0;
-margin-top: 0;
-margin-bottom: 0;
-}
-ol ol,
-ul ol {
-list-style-type: lower-roman;
-}
-ul ul ol,
-ul ol ol,
-ol ul ol,
-ol ol ol {
-list-style-type: lower-alpha;
-}
-dd {
-margin-left: 0;
-}
-code {
-font-family: Consolas, "Liberation Mono", Menlo, Courier, monospace;
-font-size: 12px;
-}
-pre {
-margin-top: 0;
-margin-bottom: 0;
-font: 12px Consolas, "Liberation Mono", Menlo, Courier, monospace;
-}
-.select::-ms-expand {
-opacity: 0;
-}
-.octicon {
-font: normal normal normal 16px/1 octicons-link;
-display: inline-block;
-text-decoration: none;
-text-rendering: auto;
--webkit-font-smoothing: antialiased;
--moz-osx-font-smoothing: grayscale;
--webkit-user-select: none;
--moz-user-select: none;
--ms-user-select: none;
-user-select: none;
-}
-.octicon-link:before {
-content: '\f05c';
-}
-.markdown-body:before {
-display: table;
-content: "";
-}
-.markdown-body:after {
-display: table;
-clear: both;
-content: "";
-}
-.markdown-body>*:first-child {
-margin-top: 0 !important;
-}
-.markdown-body>*:last-child {
-margin-bottom: 0 !important;
-}
-a:not([href]) {
-color: inherit;
-text-decoration: none;
-}
-.anchor {
-display: inline-block;
-padding-right: 2px;
-margin-left: -18px;
-}
-.anchor:focus {
-outline: none;
-}
-h1,
-h2,
-h3,
-h4,
-h5,
-h6 {
-margin-top: 1em;
-margin-bottom: 16px;
-font-weight: bold;
-line-height: 1.4;
-}
-h1 .octicon-link,
-h2 .octicon-link,
-h3 .octicon-link,
-h4 .octicon-link,
-h5 .octicon-link,
-h6 .octicon-link {
-color: #000;
-vertical-align: middle;
-visibility: hidden;
-}
-h1:hover .anchor,
-h2:hover .anchor,
-h3:hover .anchor,
-h4:hover .anchor,
-h5:hover .anchor,
-h6:hover .anchor {
-text-decoration: none;
-}
-h1:hover .anchor .octicon-link,
-h2:hover .anchor .octicon-link,
-h3:hover .anchor .octicon-link,
-h4:hover .anchor .octicon-link,
-h5:hover .anchor .octicon-link,
-h6:hover .anchor .octicon-link {
-visibility: visible;
-}
-h1 {
-padding-bottom: 0.3em;
-font-size: 2.25em;
-line-height: 1.2;
-border-bottom: 1px solid #eee;
-}
-h1 .anchor {
-line-height: 1;
-}
-h2 {
-padding-bottom: 0.3em;
-font-size: 1.75em;
-line-height: 1.225;
-border-bottom: 1px solid #eee;
-}
-h2 .anchor {
-line-height: 1;
-}
-h3 {
-font-size: 1.5em;
-line-height: 1.43;
-}
-h3 .anchor {
-line-height: 1.2;
-}
-h4 {
-font-size: 1.25em;
-}
-h4 .anchor {
-line-height: 1.2;
-}
-h5 {
-font-size: 1em;
-}
-h5 .anchor {
-line-height: 1.1;
-}
-h6 {
-font-size: 1em;
-color: #777;
-}
-h6 .anchor {
-line-height: 1.1;
-}
-p,
-blockquote,
-ul,
-ol,
-dl,
-table,
-pre {
-margin-top: 0;
-margin-bottom: 16px;
-}
-hr {
-height: 4px;
-padding: 0;
-margin: 16px 0;
-background-color: #e7e7e7;
-border: 0 none;
-}
-ul,
-ol {
-padding-left: 2em;
-}
-ul ul,
-ul ol,
-ol ol,
-ol ul {
-margin-top: 0;
-margin-bottom: 0;
-}
-li>p {
-margin-top: 16px;
-}
-dl {
-padding: 0;
-}
-dl dt {
-padding: 0;
-margin-top: 16px;
-font-size: 1em;
-font-style: italic;
-font-weight: bold;
-}
-dl dd {
-padding: 0 16px;
-margin-bottom: 16px;
-}
-blockquote {
-padding: 0 15px;
-color: #777;
-border-left: 4px solid #ddd;
-}
-blockquote>:first-child {
-margin-top: 0;
-}
-blockquote>:last-child {
-margin-bottom: 0;
-}
-table {
-display: block;
-width: 100%;
-overflow: auto;
-word-break: normal;
-word-break: keep-all;
-}
-table th {
-font-weight: bold;
-}
-table th,
-table td {
-padding: 6px 13px;
-border: 1px solid #ddd;
-}
-table tr {
-background-color: #fff;
-border-top: 1px solid #ccc;
-}
-table tr:nth-child(2n) {
-background-color: #f8f8f8;
-}
-img {
-max-width: 100%;
-box-sizing: content-box;
-background-color: #fff;
-}
-code {
-padding: 0;
-padding-top: 0.2em;
-padding-bottom: 0.2em;
-margin: 0;
-font-size: 85%;
-background-color: rgba(0,0,0,0.04);
-border-radius: 3px;
-}
-code:before,
-code:after {
-letter-spacing: -0.2em;
-content: "\00a0";
-}
-pre>code {
-padding: 0;
-margin: 0;
-font-size: 100%;
-word-break: normal;
-white-space: pre;
-background: transparent;
-border: 0;
-}
-.highlight {
-margin-bottom: 16px;
-}
-.highlight pre,
-pre {
-padding: 16px;
-overflow: auto;
-font-size: 85%;
-line-height: 1.45;
-background-color: #f7f7f7;
-border-radius: 3px;
-}
-.highlight pre {
-margin-bottom: 0;
-word-break: normal;
-}
-pre {
-word-wrap: normal;
-}
-pre code {
-display: inline;
-max-width: initial;
-padding: 0;
-margin: 0;
-overflow: initial;
-line-height: inherit;
-word-wrap: normal;
-background-color: transparent;
-border: 0;
-}
-pre code:before,
-pre code:after {
-content: normal;
-}
-kbd {
-display: inline-block;
-padding: 3px 5px;
-font-size: 11px;
-line-height: 10px;
-color: #555;
-vertical-align: middle;
-background-color: #fcfcfc;
-border: solid 1px #ccc;
-border-bottom-color: #bbb;
-border-radius: 3px;
-box-shadow: inset 0 -1px 0 #bbb;
-}
-.pl-c {
-color: #969896;
-}
-.pl-c1,
-.pl-s .pl-v {
-color: #0086b3;
-}
-.pl-e,
-.pl-en {
-color: #795da3;
-}
-.pl-s .pl-s1,
-.pl-smi {
-color: #333;
-}
-.pl-ent {
-color: #63a35c;
-}
-.pl-k {
-color: #a71d5d;
-}
-.pl-pds,
-.pl-s,
-.pl-s .pl-pse .pl-s1,
-.pl-sr,
-.pl-sr .pl-cce,
-.pl-sr .pl-sra,
-.pl-sr .pl-sre {
-color: #183691;
-}
-.pl-v {
-color: #ed6a43;
-}
-.pl-id {
-color: #b52a1d;
-}
-.pl-ii {
-background-color: #b52a1d;
-color: #f8f8f8;
-}
-.pl-sr .pl-cce {
-color: #63a35c;
-font-weight: bold;
-}
-.pl-ml {
-color: #693a17;
-}
-.pl-mh,
-.pl-mh .pl-en,
-.pl-ms {
-color: #1d3e81;
-font-weight: bold;
-}
-.pl-mq {
-color: #008080;
-}
-.pl-mi {
-color: #333;
-font-style: italic;
-}
-.pl-mb {
-color: #333;
-font-weight: bold;
-}
-.pl-md {
-background-color: #ffecec;
-color: #bd2c00;
-}
-.pl-mi1 {
-background-color: #eaffea;
-color: #55a532;
-}
-.pl-mdr {
-color: #795da3;
-font-weight: bold;
-}
-.pl-mo {
-color: #1d3e81;
-}
-kbd {
-display: inline-block;
-padding: 3px 5px;
-font: 11px Consolas, "Liberation Mono", Menlo, Courier, monospace;
-line-height: 10px;
-color: #555;
-vertical-align: middle;
-background-color: #fcfcfc;
-border: solid 1px #ccc;
-border-bottom-color: #bbb;
-border-radius: 3px;
-box-shadow: inset 0 -1px 0 #bbb;
-}
-.task-list-item {
-list-style-type: none;
-}
-.task-list-item+.task-list-item {
-margin-top: 3px;
-}
-.task-list-item input {
-margin: 0 0.35em 0.25em -1.6em;
-vertical-align: middle;
-}
-:checked+.radio-label {
-z-index: 1;
-position: relative;
-border-color: #4078c0;
-}
-.sourceLine {
-display: inline-block;
-}
-code .kw { color: #000000; }
-code .dt { color: #ed6a43; }
-code .dv { color: #009999; }
-code .bn { color: #009999; }
-code .fl { color: #009999; }
-code .ch { color: #009999; }
-code .st { color: #183691; }
-code .co { color: #969896; }
-code .ot { color: #0086b3; }
-code .al { color: #a61717; }
-code .fu { color: #63a35c; }
-code .er { color: #a61717; background-color: #e3d2d2; }
-code .wa { color: #000000; }
-code .cn { color: #008080; }
-code .sc { color: #008080; }
-code .vs { color: #183691; }
-code .ss { color: #183691; }
-code .im { color: #000000; }
-code .va {color: #008080; }
-code .cf { color: #000000; }
-code .op { color: #000000; }
-code .bu { color: #000000; }
-code .ex { color: #000000; }
-code .pp { color: #999999; }
-code .at { color: #008080; }
-code .do { color: #969896; }
-code .an { color: #008080; }
-code .cv { color: #008080; }
-code .in { color: #008080; }
-</style>
-<style>
-body {
-box-sizing: border-box;
-min-width: 200px;
-max-width: 980px;
-margin: 0 auto;
-padding: 45px;
-padding-top: 0px;
-}
-</style>
-
-
-</head>
-
-<body>
-
-<!-- README.md is generated from README.Rmd. Please edit that file -->
-
-<h1 id="simulatedce">simulateDCE</h1>
-<!-- badges: start -->
-
-<!-- badges: end -->
-
-<p>The goal of simulateDCE is to make it easy to simulate choice
-experiment datasets using designs from NGENE or <code>spdesign</code>.
-You have to store the design file in a subfolder and need to specify
-certain parameters and the utility functions for the data generating
-process. The package is useful for:</p>
-<ol style="list-style-type: decimal">
-<li><p>Test different designs in terms of statistical power, efficiency
-and unbiasedness</p></li>
-<li><p>To test the effects of deviations from RUM, e.g. heuristics, on
-model performance for different designs.</p></li>
-<li><p>In teaching, using simulated data is useful, if you want to know
-the data generating process. It helps to demonstrate Maximum likelihood
-and choice models, knowing exactly what you should expect.</p></li>
-<li><p>You can use simulation in pre-registration to justify your sample
-size and design choice.</p></li>
-<li><p>Before data collection, you can use simulated data to estimate
-the models you plan to use in the actual analysis. You can thus make
-sure, you can estimate all effects for given sample sizes.</p></li>
-</ol>
-<h2 id="installation">Installation</h2>
-<p>You can install the development version of simulateDCE from gitlab.
-You need to install the <code>remotes</code> package first. The current
-version is alpha and there is no version on cran:</p>
-<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a></span>
-<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a><span class="fu">install.packages</span>(<span class="st">"remotes"</span>)</span>
-<span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a>remotes<span class="sc">::</span><span class="fu">install_gitlab</span>(<span class="at">repo =</span> <span class="st">"dj44vuri/simulateDCE"</span> , <span class="at">host =</span> <span class="st">"https://git.idiv.de"</span>)</span></code></pre></div>
-<h2 id="example">Example</h2>
-<p>This is a basic example for a simulation:</p>
-<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a></span>
-<span id="cb2-2"><a href="#cb2-2" tabindex="-1"></a></span>
-<span id="cb2-3"><a href="#cb2-3" tabindex="-1"></a><span class="fu">rm</span>(<span class="at">list=</span><span class="fu">ls</span>())</span>
-<span id="cb2-4"><a href="#cb2-4" tabindex="-1"></a><span class="fu">library</span>(simulateDCE)</span>
-<span id="cb2-5"><a href="#cb2-5" tabindex="-1"></a><span class="fu">library</span>(rlang)</span>
-<span id="cb2-6"><a href="#cb2-6" tabindex="-1"></a><span class="fu">library</span>(formula.tools)</span>
-<span id="cb2-7"><a href="#cb2-7" tabindex="-1"></a></span>
-<span id="cb2-8"><a href="#cb2-8" tabindex="-1"></a></span>
-<span id="cb2-9"><a href="#cb2-9" tabindex="-1"></a><span class="fu">library</span>(rlang)</span>
-<span id="cb2-10"><a href="#cb2-10" tabindex="-1"></a></span>
-<span id="cb2-11"><a href="#cb2-11" tabindex="-1"></a>designpath<span class="ot"><-</span> <span class="fu">system.file</span>(<span class="st">"extdata"</span>,<span class="st">"SE_DRIVE"</span> ,<span class="at">package =</span> <span class="st">"simulateDCE"</span>)</span>
-<span id="cb2-12"><a href="#cb2-12" tabindex="-1"></a></span>
-<span id="cb2-13"><a href="#cb2-13" tabindex="-1"></a>resps <span class="ot">=</span><span class="dv">120</span> <span class="co"># number of respondents</span></span>
-<span id="cb2-14"><a href="#cb2-14" tabindex="-1"></a>nosim<span class="ot">=</span> <span class="dv">2</span> <span class="co"># number of simulations to run (about 500 is minimum)</span></span>
-<span id="cb2-15"><a href="#cb2-15" tabindex="-1"></a></span>
-<span id="cb2-16"><a href="#cb2-16" tabindex="-1"></a></span>
-<span id="cb2-17"><a href="#cb2-17" tabindex="-1"></a></span>
-<span id="cb2-18"><a href="#cb2-18" tabindex="-1"></a></span>
-<span id="cb2-19"><a href="#cb2-19" tabindex="-1"></a></span>
-<span id="cb2-20"><a href="#cb2-20" tabindex="-1"></a></span>
-<span id="cb2-21"><a href="#cb2-21" tabindex="-1"></a><span class="co"># bcoeff <- list(</span></span>
-<span id="cb2-22"><a href="#cb2-22" tabindex="-1"></a><span class="co"># bpreis = -0.036,</span></span>
-<span id="cb2-23"><a href="#cb2-23" tabindex="-1"></a><span class="co"># blade = -0.034,</span></span>
-<span id="cb2-24"><a href="#cb2-24" tabindex="-1"></a><span class="co"># bwarte = -0.049)</span></span>
-<span id="cb2-25"><a href="#cb2-25" tabindex="-1"></a></span>
-<span id="cb2-26"><a href="#cb2-26" tabindex="-1"></a></span>
-<span id="cb2-27"><a href="#cb2-27" tabindex="-1"></a>decisiongroups<span class="ot">=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="fl">0.7</span>,<span class="dv">1</span>)</span>
-<span id="cb2-28"><a href="#cb2-28" tabindex="-1"></a></span>
-<span id="cb2-29"><a href="#cb2-29" tabindex="-1"></a><span class="co"># wrong parameters</span></span>
-<span id="cb2-30"><a href="#cb2-30" tabindex="-1"></a></span>
-<span id="cb2-31"><a href="#cb2-31" tabindex="-1"></a><span class="co"># place b coefficients into an r list:</span></span>
-<span id="cb2-32"><a href="#cb2-32" tabindex="-1"></a>bcoeff <span class="ot">=</span> <span class="fu">list</span>(</span>
-<span id="cb2-33"><a href="#cb2-33" tabindex="-1"></a> <span class="at">bpreis =</span> <span class="sc">-</span><span class="fl">0.01</span>,</span>
-<span id="cb2-34"><a href="#cb2-34" tabindex="-1"></a> <span class="at">blade =</span> <span class="sc">-</span><span class="fl">0.07</span>,</span>
-<span id="cb2-35"><a href="#cb2-35" tabindex="-1"></a> <span class="at">bwarte =</span> <span class="fl">0.02</span>)</span>
-<span id="cb2-36"><a href="#cb2-36" tabindex="-1"></a></span>
-<span id="cb2-37"><a href="#cb2-37" tabindex="-1"></a>manipulations <span class="ot">=</span> <span class="fu">list</span>(<span class="at">alt1.x2=</span> <span class="fu">expr</span>(alt1.x2<span class="sc">/</span><span class="dv">10</span>),</span>
-<span id="cb2-38"><a href="#cb2-38" tabindex="-1"></a> <span class="at">alt1.x3=</span> <span class="fu">expr</span>(alt1.x3<span class="sc">/</span><span class="dv">10</span>),</span>
-<span id="cb2-39"><a href="#cb2-39" tabindex="-1"></a> <span class="at">alt2.x2=</span> <span class="fu">expr</span>(alt2.x2<span class="sc">/</span><span class="dv">10</span>),</span>
-<span id="cb2-40"><a href="#cb2-40" tabindex="-1"></a> <span class="at">alt2.x3=</span> <span class="fu">expr</span>(alt2.x3<span class="sc">/</span><span class="dv">10</span>)</span>
-<span id="cb2-41"><a href="#cb2-41" tabindex="-1"></a>)</span>
-<span id="cb2-42"><a href="#cb2-42" tabindex="-1"></a></span>
-<span id="cb2-43"><a href="#cb2-43" tabindex="-1"></a></span>
-<span id="cb2-44"><a href="#cb2-44" tabindex="-1"></a><span class="co">#place your utility functions here</span></span>
-<span id="cb2-45"><a href="#cb2-45" tabindex="-1"></a>ul<span class="ot"><-</span><span class="fu">list</span>( <span class="at">u1 =</span></span>
-<span id="cb2-46"><a href="#cb2-46" tabindex="-1"></a></span>
-<span id="cb2-47"><a href="#cb2-47" tabindex="-1"></a> <span class="fu">list</span>(</span>
-<span id="cb2-48"><a href="#cb2-48" tabindex="-1"></a> <span class="at">v1 =</span>V<span class="fl">.1</span><span class="sc">~</span> bpreis <span class="sc">*</span> alt1.x1 <span class="sc">+</span> blade<span class="sc">*</span>alt1.x2 <span class="sc">+</span> bwarte<span class="sc">*</span>alt1.x3 ,</span>
-<span id="cb2-49"><a href="#cb2-49" tabindex="-1"></a> <span class="at">v2 =</span>V<span class="fl">.2</span><span class="sc">~</span> bpreis <span class="sc">*</span> alt2.x1 <span class="sc">+</span> blade<span class="sc">*</span>alt2.x2 <span class="sc">+</span> bwarte<span class="sc">*</span>alt2.x3</span>
-<span id="cb2-50"><a href="#cb2-50" tabindex="-1"></a> )</span>
-<span id="cb2-51"><a href="#cb2-51" tabindex="-1"></a></span>
-<span id="cb2-52"><a href="#cb2-52" tabindex="-1"></a> ,</span>
-<span id="cb2-53"><a href="#cb2-53" tabindex="-1"></a> <span class="at">u2 =</span> <span class="fu">list</span>( <span class="at">v1 =</span>V<span class="fl">.1</span><span class="sc">~</span> bpreis <span class="sc">*</span> alt1.x1 ,</span>
-<span id="cb2-54"><a href="#cb2-54" tabindex="-1"></a> <span class="at">v2 =</span>V<span class="fl">.2</span><span class="sc">~</span> bpreis <span class="sc">*</span> alt2.x1)</span>
-<span id="cb2-55"><a href="#cb2-55" tabindex="-1"></a></span>
-<span id="cb2-56"><a href="#cb2-56" tabindex="-1"></a>)</span>
-<span id="cb2-57"><a href="#cb2-57" tabindex="-1"></a></span>
-<span id="cb2-58"><a href="#cb2-58" tabindex="-1"></a></span>
-<span id="cb2-59"><a href="#cb2-59" tabindex="-1"></a>destype<span class="ot">=</span><span class="st">"ngene"</span></span>
-<span id="cb2-60"><a href="#cb2-60" tabindex="-1"></a></span>
-<span id="cb2-61"><a href="#cb2-61" tabindex="-1"></a>sedrive <span class="ot"><-</span> <span class="fu">sim_all</span>(<span class="at">nosim =</span> nosim, <span class="at">resps=</span>resps, <span class="at">destype =</span> destype,</span>
-<span id="cb2-62"><a href="#cb2-62" tabindex="-1"></a> <span class="at">designpath =</span> designpath, <span class="at">u=</span>ul, <span class="at">bcoeff =</span> bcoeff)</span>
-<span id="cb2-63"><a href="#cb2-63" tabindex="-1"></a><span class="co">#> Utility function used in simulation, ie the true utility: </span></span>
-<span id="cb2-64"><a href="#cb2-64" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-65"><a href="#cb2-65" tabindex="-1"></a><span class="co">#> $u1</span></span>
-<span id="cb2-66"><a href="#cb2-66" tabindex="-1"></a><span class="co">#> $u1$v1</span></span>
-<span id="cb2-67"><a href="#cb2-67" tabindex="-1"></a><span class="co">#> V.1 ~ bpreis * alt1.x1 + blade * alt1.x2 + bwarte * alt1.x3</span></span>
-<span id="cb2-68"><a href="#cb2-68" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-69"><a href="#cb2-69" tabindex="-1"></a><span class="co">#> $u1$v2</span></span>
-<span id="cb2-70"><a href="#cb2-70" tabindex="-1"></a><span class="co">#> V.2 ~ bpreis * alt2.x1 + blade * alt2.x2 + bwarte * alt2.x3</span></span>
-<span id="cb2-71"><a href="#cb2-71" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-72"><a href="#cb2-72" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-73"><a href="#cb2-73" tabindex="-1"></a><span class="co">#> $u2</span></span>
-<span id="cb2-74"><a href="#cb2-74" tabindex="-1"></a><span class="co">#> $u2$v1</span></span>
-<span id="cb2-75"><a href="#cb2-75" tabindex="-1"></a><span class="co">#> V.1 ~ bpreis * alt1.x1</span></span>
-<span id="cb2-76"><a href="#cb2-76" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-77"><a href="#cb2-77" tabindex="-1"></a><span class="co">#> $u2$v2</span></span>
-<span id="cb2-78"><a href="#cb2-78" tabindex="-1"></a><span class="co">#> V.2 ~ bpreis * alt2.x1</span></span>
-<span id="cb2-79"><a href="#cb2-79" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-80"><a href="#cb2-80" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-81"><a href="#cb2-81" tabindex="-1"></a><span class="co">#> Utility function used for Logit estimation with mixl: </span></span>
-<span id="cb2-82"><a href="#cb2-82" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-83"><a href="#cb2-83" tabindex="-1"></a><span class="co">#> [1] "U_1 = @bpreis *$alt1_x1 + @blade *$alt1_x2 + @bwarte *$alt1_x3 ;U_2 = @bpreis *$alt2_x1 + @blade *$alt2_x2 + @bwarte *$alt2_x3 ;"</span></span>
-<span id="cb2-84"><a href="#cb2-84" tabindex="-1"></a><span class="co">#> New names:</span></span>
-<span id="cb2-85"><a href="#cb2-85" tabindex="-1"></a><span class="co">#> • `Choice situation` -> `Choice.situation`</span></span>
-<span id="cb2-86"><a href="#cb2-86" tabindex="-1"></a><span class="co">#> • `` -> `...10`</span></span>
-<span id="cb2-87"><a href="#cb2-87" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-88"><a href="#cb2-88" tabindex="-1"></a><span class="co">#> does sou_gis exist: FALSE </span></span>
-<span id="cb2-89"><a href="#cb2-89" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-90"><a href="#cb2-90" tabindex="-1"></a><span class="co">#> dataset final_set exists: FALSE </span></span>
-<span id="cb2-91"><a href="#cb2-91" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-92"><a href="#cb2-92" tabindex="-1"></a><span class="co">#> decisiongroups exists: TRUE</span></span>
-<span id="cb2-93"><a href="#cb2-93" tabindex="-1"></a><span class="co">#> 1 2 </span></span>
-<span id="cb2-94"><a href="#cb2-94" tabindex="-1"></a><span class="co">#> 1007 433 </span></span>
-<span id="cb2-95"><a href="#cb2-95" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-96"><a href="#cb2-96" tabindex="-1"></a><span class="co">#> data has been made </span></span>
-<span id="cb2-97"><a href="#cb2-97" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-98"><a href="#cb2-98" tabindex="-1"></a><span class="co">#> First few observations </span></span>
-<span id="cb2-99"><a href="#cb2-99" tabindex="-1"></a><span class="co">#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block group V_1 V_2 e_1 e_2 U_1 U_2</span></span>
-<span id="cb2-100"><a href="#cb2-100" tabindex="-1"></a><span class="co">#> 1 1 7 80 2.5 10.0 60 20.0 10 1 1 -0.775 -1.800 -0.15343271 3.6612318 -0.9284327 1.8612318</span></span>
-<span id="cb2-101"><a href="#cb2-101" tabindex="-1"></a><span class="co">#> 2 1 19 20 2.5 5.0 60 2.5 0 1 1 -0.275 -0.775 0.47936686 -0.8086989 0.2043669 -1.5836989</span></span>
-<span id="cb2-102"><a href="#cb2-102" tabindex="-1"></a><span class="co">#> 3 1 30 20 10.0 5.0 80 5.0 10 1 1 -0.800 -0.950 0.12634298 1.2300690 -0.6736570 0.2800690</span></span>
-<span id="cb2-103"><a href="#cb2-103" tabindex="-1"></a><span class="co">#> 4 1 32 40 20.0 2.5 80 2.5 0 1 1 -1.750 -0.975 2.08710825 -0.1882935 0.3371082 -1.1632935</span></span>
-<span id="cb2-104"><a href="#cb2-104" tabindex="-1"></a><span class="co">#> 5 1 39 40 20.0 0.0 80 10.0 10 1 1 -1.800 -1.300 1.05540385 3.1339326 -0.7445961 1.8339326</span></span>
-<span id="cb2-105"><a href="#cb2-105" tabindex="-1"></a><span class="co">#> 6 1 48 60 5.0 2.5 20 5.0 10 1 1 -0.900 -0.350 0.07255885 -0.1156742 -0.8274412 -0.4656742</span></span>
-<span id="cb2-106"><a href="#cb2-106" tabindex="-1"></a><span class="co">#> CHOICE</span></span>
-<span id="cb2-107"><a href="#cb2-107" tabindex="-1"></a><span class="co">#> 1 2</span></span>
-<span id="cb2-108"><a href="#cb2-108" tabindex="-1"></a><span class="co">#> 2 1</span></span>
-<span id="cb2-109"><a href="#cb2-109" tabindex="-1"></a><span class="co">#> 3 2</span></span>
-<span id="cb2-110"><a href="#cb2-110" tabindex="-1"></a><span class="co">#> 4 1</span></span>
-<span id="cb2-111"><a href="#cb2-111" tabindex="-1"></a><span class="co">#> 5 2</span></span>
-<span id="cb2-112"><a href="#cb2-112" tabindex="-1"></a><span class="co">#> 6 2</span></span>
-<span id="cb2-113"><a href="#cb2-113" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-114"><a href="#cb2-114" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-115"><a href="#cb2-115" tabindex="-1"></a><span class="co">#> This is Run number 1 </span></span>
-<span id="cb2-116"><a href="#cb2-116" tabindex="-1"></a><span class="co">#> does sou_gis exist: FALSE </span></span>
-<span id="cb2-117"><a href="#cb2-117" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-118"><a href="#cb2-118" tabindex="-1"></a><span class="co">#> dataset final_set exists: FALSE </span></span>
-<span id="cb2-119"><a href="#cb2-119" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-120"><a href="#cb2-120" tabindex="-1"></a><span class="co">#> decisiongroups exists: TRUE</span></span>
-<span id="cb2-121"><a href="#cb2-121" tabindex="-1"></a><span class="co">#> 1 2 </span></span>
-<span id="cb2-122"><a href="#cb2-122" tabindex="-1"></a><span class="co">#> 1007 433 </span></span>
-<span id="cb2-123"><a href="#cb2-123" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-124"><a href="#cb2-124" tabindex="-1"></a><span class="co">#> data has been made </span></span>
-<span id="cb2-125"><a href="#cb2-125" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-126"><a href="#cb2-126" tabindex="-1"></a><span class="co">#> First few observations </span></span>
-<span id="cb2-127"><a href="#cb2-127" tabindex="-1"></a><span class="co">#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block group V_1 V_2 e_1 e_2 U_1 U_2</span></span>
-<span id="cb2-128"><a href="#cb2-128" tabindex="-1"></a><span class="co">#> 1 1 7 80 2.5 10.0 60 20.0 10 1 1 -0.775 -1.800 -1.2775839 1.5689946 -2.052584 -0.2310054</span></span>
-<span id="cb2-129"><a href="#cb2-129" tabindex="-1"></a><span class="co">#> 2 1 19 20 2.5 5.0 60 2.5 0 1 1 -0.275 -0.775 0.5934850 0.5453996 0.318485 -0.2296004</span></span>
-<span id="cb2-130"><a href="#cb2-130" tabindex="-1"></a><span class="co">#> 3 1 30 20 10.0 5.0 80 5.0 10 1 1 -0.800 -0.950 -0.5127855 1.7551185 -1.312785 0.8051185</span></span>
-<span id="cb2-131"><a href="#cb2-131" tabindex="-1"></a><span class="co">#> 4 1 32 40 20.0 2.5 80 2.5 0 1 1 -1.750 -0.975 3.4643234 1.3685812 1.714323 0.3935812</span></span>
-<span id="cb2-132"><a href="#cb2-132" tabindex="-1"></a><span class="co">#> 5 1 39 40 20.0 0.0 80 10.0 10 1 1 -1.800 -1.300 -0.5128262 0.3011019 -2.312826 -0.9988981</span></span>
-<span id="cb2-133"><a href="#cb2-133" tabindex="-1"></a><span class="co">#> 6 1 48 60 5.0 2.5 20 5.0 10 1 1 -0.900 -0.350 4.3343477 1.2265189 3.434348 0.8765189</span></span>
-<span id="cb2-134"><a href="#cb2-134" tabindex="-1"></a><span class="co">#> CHOICE</span></span>
-<span id="cb2-135"><a href="#cb2-135" tabindex="-1"></a><span class="co">#> 1 2</span></span>
-<span id="cb2-136"><a href="#cb2-136" tabindex="-1"></a><span class="co">#> 2 1</span></span>
-<span id="cb2-137"><a href="#cb2-137" tabindex="-1"></a><span class="co">#> 3 2</span></span>
-<span id="cb2-138"><a href="#cb2-138" tabindex="-1"></a><span class="co">#> 4 1</span></span>
-<span id="cb2-139"><a href="#cb2-139" tabindex="-1"></a><span class="co">#> 5 2</span></span>
-<span id="cb2-140"><a href="#cb2-140" tabindex="-1"></a><span class="co">#> 6 1</span></span>
-<span id="cb2-141"><a href="#cb2-141" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-142"><a href="#cb2-142" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-143"><a href="#cb2-143" tabindex="-1"></a><span class="co">#> This is the utility functions </span></span>
-<span id="cb2-144"><a href="#cb2-144" tabindex="-1"></a><span class="co">#> U_1 = @bpreis *$alt1_x1 + @blade *$alt1_x2 + @bwarte *$alt1_x3 ;U_2 = @bpreis *$alt2_x1 + @blade *$alt2_x2 + @bwarte *$alt2_x3 ;Initial function value: -998.1319 </span></span>
-<span id="cb2-145"><a href="#cb2-145" tabindex="-1"></a><span class="co">#> Initial gradient value:</span></span>
-<span id="cb2-146"><a href="#cb2-146" tabindex="-1"></a><span class="co">#> bpreis blade bwarte </span></span>
-<span id="cb2-147"><a href="#cb2-147" tabindex="-1"></a><span class="co">#> -1060.0 -812.5 520.0 </span></span>
-<span id="cb2-148"><a href="#cb2-148" tabindex="-1"></a><span class="co">#> initial value 998.131940 </span></span>
-<span id="cb2-149"><a href="#cb2-149" tabindex="-1"></a><span class="co">#> iter 2 value 994.260781</span></span>
-<span id="cb2-150"><a href="#cb2-150" tabindex="-1"></a><span class="co">#> iter 3 value 974.092906</span></span>
-<span id="cb2-151"><a href="#cb2-151" tabindex="-1"></a><span class="co">#> iter 4 value 973.856287</span></span>
-<span id="cb2-152"><a href="#cb2-152" tabindex="-1"></a><span class="co">#> iter 5 value 970.270450</span></span>
-<span id="cb2-153"><a href="#cb2-153" tabindex="-1"></a><span class="co">#> iter 6 value 970.262794</span></span>
-<span id="cb2-154"><a href="#cb2-154" tabindex="-1"></a><span class="co">#> iter 6 value 970.262788</span></span>
-<span id="cb2-155"><a href="#cb2-155" tabindex="-1"></a><span class="co">#> iter 6 value 970.262788</span></span>
-<span id="cb2-156"><a href="#cb2-156" tabindex="-1"></a><span class="co">#> final value 970.262788 </span></span>
-<span id="cb2-157"><a href="#cb2-157" tabindex="-1"></a><span class="co">#> converged</span></span>
-<span id="cb2-158"><a href="#cb2-158" tabindex="-1"></a><span class="co">#> This is Run number 2 </span></span>
-<span id="cb2-159"><a href="#cb2-159" tabindex="-1"></a><span class="co">#> does sou_gis exist: FALSE </span></span>
-<span id="cb2-160"><a href="#cb2-160" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-161"><a href="#cb2-161" tabindex="-1"></a><span class="co">#> dataset final_set exists: FALSE </span></span>
-<span id="cb2-162"><a href="#cb2-162" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-163"><a href="#cb2-163" tabindex="-1"></a><span class="co">#> decisiongroups exists: TRUE</span></span>
-<span id="cb2-164"><a href="#cb2-164" tabindex="-1"></a><span class="co">#> 1 2 </span></span>
-<span id="cb2-165"><a href="#cb2-165" tabindex="-1"></a><span class="co">#> 1007 433 </span></span>
-<span id="cb2-166"><a href="#cb2-166" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-167"><a href="#cb2-167" tabindex="-1"></a><span class="co">#> data has been made </span></span>
-<span id="cb2-168"><a href="#cb2-168" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-169"><a href="#cb2-169" tabindex="-1"></a><span class="co">#> First few observations </span></span>
-<span id="cb2-170"><a href="#cb2-170" tabindex="-1"></a><span class="co">#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block group V_1 V_2 e_1 e_2 U_1 U_2</span></span>
-<span id="cb2-171"><a href="#cb2-171" tabindex="-1"></a><span class="co">#> 1 1 7 80 2.5 10.0 60 20.0 10 1 1 -0.775 -1.800 0.34553136 -0.8375727 -0.4294686 -2.6375727</span></span>
-<span id="cb2-172"><a href="#cb2-172" tabindex="-1"></a><span class="co">#> 2 1 19 20 2.5 5.0 60 2.5 0 1 1 -0.275 -0.775 -1.32361481 0.3195766 -1.5986148 -0.4554234</span></span>
-<span id="cb2-173"><a href="#cb2-173" tabindex="-1"></a><span class="co">#> 3 1 30 20 10.0 5.0 80 5.0 10 1 1 -0.800 -0.950 0.08515524 -0.6090259 -0.7148448 -1.5590259</span></span>
-<span id="cb2-174"><a href="#cb2-174" tabindex="-1"></a><span class="co">#> 4 1 32 40 20.0 2.5 80 2.5 0 1 1 -1.750 -0.975 -0.18021132 2.3073397 -1.9302113 1.3323397</span></span>
-<span id="cb2-175"><a href="#cb2-175" tabindex="-1"></a><span class="co">#> 5 1 39 40 20.0 0.0 80 10.0 10 1 1 -1.800 -1.300 -0.55591900 3.4630292 -2.3559190 2.1630292</span></span>
-<span id="cb2-176"><a href="#cb2-176" tabindex="-1"></a><span class="co">#> 6 1 48 60 5.0 2.5 20 5.0 10 1 1 -0.900 -0.350 -0.29734711 3.0420404 -1.1973471 2.6920404</span></span>
-<span id="cb2-177"><a href="#cb2-177" tabindex="-1"></a><span class="co">#> CHOICE</span></span>
-<span id="cb2-178"><a href="#cb2-178" tabindex="-1"></a><span class="co">#> 1 1</span></span>
-<span id="cb2-179"><a href="#cb2-179" tabindex="-1"></a><span class="co">#> 2 2</span></span>
-<span id="cb2-180"><a href="#cb2-180" tabindex="-1"></a><span class="co">#> 3 1</span></span>
-<span id="cb2-181"><a href="#cb2-181" tabindex="-1"></a><span class="co">#> 4 2</span></span>
-<span id="cb2-182"><a href="#cb2-182" tabindex="-1"></a><span class="co">#> 5 2</span></span>
-<span id="cb2-183"><a href="#cb2-183" tabindex="-1"></a><span class="co">#> 6 2</span></span>
-<span id="cb2-184"><a href="#cb2-184" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-185"><a href="#cb2-185" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-186"><a href="#cb2-186" tabindex="-1"></a><span class="co">#> This is the utility functions </span></span>
-<span id="cb2-187"><a href="#cb2-187" tabindex="-1"></a><span class="co">#> U_1 = @bpreis *$alt1_x1 + @blade *$alt1_x2 + @bwarte *$alt1_x3 ;U_2 = @bpreis *$alt2_x1 + @blade *$alt2_x2 + @bwarte *$alt2_x3 ;Initial function value: -998.1319 </span></span>
-<span id="cb2-188"><a href="#cb2-188" tabindex="-1"></a><span class="co">#> Initial gradient value:</span></span>
-<span id="cb2-189"><a href="#cb2-189" tabindex="-1"></a><span class="co">#> bpreis blade bwarte </span></span>
-<span id="cb2-190"><a href="#cb2-190" tabindex="-1"></a><span class="co">#> 440.0 -1087.5 447.5 </span></span>
-<span id="cb2-191"><a href="#cb2-191" tabindex="-1"></a><span class="co">#> initial value 998.131940 </span></span>
-<span id="cb2-192"><a href="#cb2-192" tabindex="-1"></a><span class="co">#> iter 2 value 995.094499</span></span>
-<span id="cb2-193"><a href="#cb2-193" tabindex="-1"></a><span class="co">#> iter 3 value 974.488144</span></span>
-<span id="cb2-194"><a href="#cb2-194" tabindex="-1"></a><span class="co">#> iter 4 value 974.227531</span></span>
-<span id="cb2-195"><a href="#cb2-195" tabindex="-1"></a><span class="co">#> iter 5 value 971.482008</span></span>
-<span id="cb2-196"><a href="#cb2-196" tabindex="-1"></a><span class="co">#> iter 6 value 971.477251</span></span>
-<span id="cb2-197"><a href="#cb2-197" tabindex="-1"></a><span class="co">#> iter 6 value 971.477249</span></span>
-<span id="cb2-198"><a href="#cb2-198" tabindex="-1"></a><span class="co">#> iter 6 value 971.477249</span></span>
-<span id="cb2-199"><a href="#cb2-199" tabindex="-1"></a><span class="co">#> final value 971.477249 </span></span>
-<span id="cb2-200"><a href="#cb2-200" tabindex="-1"></a><span class="co">#> converged</span></span>
-<span id="cb2-201"><a href="#cb2-201" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-202"><a href="#cb2-202" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-203"><a href="#cb2-203" tabindex="-1"></a><span class="co">#> ================ ==== === ===== ==== ===== ===== ===== ====</span></span>
-<span id="cb2-204"><a href="#cb2-204" tabindex="-1"></a><span class="co">#> \ vars n mean sd min max range se</span></span>
-<span id="cb2-205"><a href="#cb2-205" tabindex="-1"></a><span class="co">#> ================ ==== === ===== ==== ===== ===== ===== ====</span></span>
-<span id="cb2-206"><a href="#cb2-206" tabindex="-1"></a><span class="co">#> est_bpreis 1 2 -0.01 0.00 -0.01 0.00 0.00 0.00</span></span>
-<span id="cb2-207"><a href="#cb2-207" tabindex="-1"></a><span class="co">#> est_blade 2 2 -0.04 0.00 -0.04 -0.04 0.00 0.00</span></span>
-<span id="cb2-208"><a href="#cb2-208" tabindex="-1"></a><span class="co">#> est_bwarte 3 2 0.03 0.00 0.03 0.03 0.00 0.00</span></span>
-<span id="cb2-209"><a href="#cb2-209" tabindex="-1"></a><span class="co">#> rob_pval0_bpreis 4 2 0.01 0.01 0.00 0.02 0.02 0.01</span></span>
-<span id="cb2-210"><a href="#cb2-210" tabindex="-1"></a><span class="co">#> rob_pval0_blade 5 2 0.00 0.00 0.00 0.00 0.00 0.00</span></span>
-<span id="cb2-211"><a href="#cb2-211" tabindex="-1"></a><span class="co">#> rob_pval0_bwarte 6 2 0.01 0.00 0.01 0.01 0.00 0.00</span></span>
-<span id="cb2-212"><a href="#cb2-212" tabindex="-1"></a><span class="co">#> ================ ==== === ===== ==== ===== ===== ===== ====</span></span>
-<span id="cb2-213"><a href="#cb2-213" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-214"><a href="#cb2-214" tabindex="-1"></a><span class="co">#> TRUE </span></span>
-<span id="cb2-215"><a href="#cb2-215" tabindex="-1"></a><span class="co">#> 100 </span></span>
-<span id="cb2-216"><a href="#cb2-216" tabindex="-1"></a><span class="co">#> Utility function used in simulation, ie the true utility: </span></span>
-<span id="cb2-217"><a href="#cb2-217" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-218"><a href="#cb2-218" tabindex="-1"></a><span class="co">#> $u1</span></span>
-<span id="cb2-219"><a href="#cb2-219" tabindex="-1"></a><span class="co">#> $u1$v1</span></span>
-<span id="cb2-220"><a href="#cb2-220" tabindex="-1"></a><span class="co">#> V.1 ~ bpreis * alt1.x1 + blade * alt1.x2 + bwarte * alt1.x3</span></span>
-<span id="cb2-221"><a href="#cb2-221" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-222"><a href="#cb2-222" tabindex="-1"></a><span class="co">#> $u1$v2</span></span>
-<span id="cb2-223"><a href="#cb2-223" tabindex="-1"></a><span class="co">#> V.2 ~ bpreis * alt2.x1 + blade * alt2.x2 + bwarte * alt2.x3</span></span>
-<span id="cb2-224"><a href="#cb2-224" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-225"><a href="#cb2-225" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-226"><a href="#cb2-226" tabindex="-1"></a><span class="co">#> $u2</span></span>
-<span id="cb2-227"><a href="#cb2-227" tabindex="-1"></a><span class="co">#> $u2$v1</span></span>
-<span id="cb2-228"><a href="#cb2-228" tabindex="-1"></a><span class="co">#> V.1 ~ bpreis * alt1.x1</span></span>
-<span id="cb2-229"><a href="#cb2-229" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-230"><a href="#cb2-230" tabindex="-1"></a><span class="co">#> $u2$v2</span></span>
-<span id="cb2-231"><a href="#cb2-231" tabindex="-1"></a><span class="co">#> V.2 ~ bpreis * alt2.x1</span></span>
-<span id="cb2-232"><a href="#cb2-232" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-233"><a href="#cb2-233" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-234"><a href="#cb2-234" tabindex="-1"></a><span class="co">#> Utility function used for Logit estimation with mixl: </span></span>
-<span id="cb2-235"><a href="#cb2-235" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-236"><a href="#cb2-236" tabindex="-1"></a><span class="co">#> [1] "U_1 = @bpreis *$alt1_x1 + @blade *$alt1_x2 + @bwarte *$alt1_x3 ;U_2 = @bpreis *$alt2_x1 + @blade *$alt2_x2 + @bwarte *$alt2_x3 ;"</span></span>
-<span id="cb2-237"><a href="#cb2-237" tabindex="-1"></a><span class="co">#> New names:</span></span>
-<span id="cb2-238"><a href="#cb2-238" tabindex="-1"></a><span class="co">#> • `Choice situation` -> `Choice.situation`</span></span>
-<span id="cb2-239"><a href="#cb2-239" tabindex="-1"></a><span class="co">#> • `` -> `...10`</span></span>
-<span id="cb2-240"><a href="#cb2-240" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-241"><a href="#cb2-241" tabindex="-1"></a><span class="co">#> does sou_gis exist: FALSE </span></span>
-<span id="cb2-242"><a href="#cb2-242" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-243"><a href="#cb2-243" tabindex="-1"></a><span class="co">#> dataset final_set exists: FALSE </span></span>
-<span id="cb2-244"><a href="#cb2-244" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-245"><a href="#cb2-245" tabindex="-1"></a><span class="co">#> decisiongroups exists: TRUE</span></span>
-<span id="cb2-246"><a href="#cb2-246" tabindex="-1"></a><span class="co">#> 1 2 </span></span>
-<span id="cb2-247"><a href="#cb2-247" tabindex="-1"></a><span class="co">#> 1007 433 </span></span>
-<span id="cb2-248"><a href="#cb2-248" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-249"><a href="#cb2-249" tabindex="-1"></a><span class="co">#> data has been made </span></span>
-<span id="cb2-250"><a href="#cb2-250" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-251"><a href="#cb2-251" tabindex="-1"></a><span class="co">#> First few observations </span></span>
-<span id="cb2-252"><a href="#cb2-252" tabindex="-1"></a><span class="co">#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block group V_1 V_2 e_1 e_2 U_1 U_2</span></span>
-<span id="cb2-253"><a href="#cb2-253" tabindex="-1"></a><span class="co">#> 1 1 12 60 2.5 0.0 20 20.0 10 1 1 -0.775 -1.400 0.63130736 -1.4347994 -0.1436926 -2.8347994</span></span>
-<span id="cb2-254"><a href="#cb2-254" tabindex="-1"></a><span class="co">#> 2 1 16 20 10.0 5.0 40 5.0 0 1 1 -0.800 -0.750 5.09739937 0.4118885 4.2973994 -0.3381115</span></span>
-<span id="cb2-255"><a href="#cb2-255" tabindex="-1"></a><span class="co">#> 3 1 17 20 20.0 0.0 80 10.0 10 1 1 -1.600 -1.300 0.22397799 0.4666321 -1.3760220 -0.8333679</span></span>
-<span id="cb2-256"><a href="#cb2-256" tabindex="-1"></a><span class="co">#> 4 1 25 60 5.0 10.0 20 20.0 5 1 1 -0.750 -1.500 -0.05146482 2.2007592 -0.8014648 0.7007592</span></span>
-<span id="cb2-257"><a href="#cb2-257" tabindex="-1"></a><span class="co">#> 5 1 29 20 5.0 10.0 80 5.0 0 1 1 -0.350 -1.150 1.57620781 4.9154679 1.2262078 3.7654679</span></span>
-<span id="cb2-258"><a href="#cb2-258" tabindex="-1"></a><span class="co">#> 6 1 32 40 10.0 2.5 80 2.5 5 1 1 -1.050 -0.875 -0.47930823 0.7058788 -1.5293082 -0.1691212</span></span>
-<span id="cb2-259"><a href="#cb2-259" tabindex="-1"></a><span class="co">#> CHOICE</span></span>
-<span id="cb2-260"><a href="#cb2-260" tabindex="-1"></a><span class="co">#> 1 1</span></span>
-<span id="cb2-261"><a href="#cb2-261" tabindex="-1"></a><span class="co">#> 2 1</span></span>
-<span id="cb2-262"><a href="#cb2-262" tabindex="-1"></a><span class="co">#> 3 2</span></span>
-<span id="cb2-263"><a href="#cb2-263" tabindex="-1"></a><span class="co">#> 4 2</span></span>
-<span id="cb2-264"><a href="#cb2-264" tabindex="-1"></a><span class="co">#> 5 2</span></span>
-<span id="cb2-265"><a href="#cb2-265" tabindex="-1"></a><span class="co">#> 6 2</span></span>
-<span id="cb2-266"><a href="#cb2-266" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-267"><a href="#cb2-267" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-268"><a href="#cb2-268" tabindex="-1"></a><span class="co">#> This is Run number 1 </span></span>
-<span id="cb2-269"><a href="#cb2-269" tabindex="-1"></a><span class="co">#> does sou_gis exist: FALSE </span></span>
-<span id="cb2-270"><a href="#cb2-270" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-271"><a href="#cb2-271" tabindex="-1"></a><span class="co">#> dataset final_set exists: FALSE </span></span>
-<span id="cb2-272"><a href="#cb2-272" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-273"><a href="#cb2-273" tabindex="-1"></a><span class="co">#> decisiongroups exists: TRUE</span></span>
-<span id="cb2-274"><a href="#cb2-274" tabindex="-1"></a><span class="co">#> 1 2 </span></span>
-<span id="cb2-275"><a href="#cb2-275" tabindex="-1"></a><span class="co">#> 1007 433 </span></span>
-<span id="cb2-276"><a href="#cb2-276" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-277"><a href="#cb2-277" tabindex="-1"></a><span class="co">#> data has been made </span></span>
-<span id="cb2-278"><a href="#cb2-278" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-279"><a href="#cb2-279" tabindex="-1"></a><span class="co">#> First few observations </span></span>
-<span id="cb2-280"><a href="#cb2-280" tabindex="-1"></a><span class="co">#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block group V_1 V_2 e_1 e_2 U_1 U_2</span></span>
-<span id="cb2-281"><a href="#cb2-281" tabindex="-1"></a><span class="co">#> 1 1 12 60 2.5 0.0 20 20.0 10 1 1 -0.775 -1.400 0.3494617 0.1551114 -0.42553827 -1.24488857</span></span>
-<span id="cb2-282"><a href="#cb2-282" tabindex="-1"></a><span class="co">#> 2 1 16 20 10.0 5.0 40 5.0 0 1 1 -0.800 -0.750 1.5845207 0.5556039 0.78452066 -0.19439613</span></span>
-<span id="cb2-283"><a href="#cb2-283" tabindex="-1"></a><span class="co">#> 3 1 17 20 20.0 0.0 80 10.0 10 1 1 -1.600 -1.300 4.1993459 -0.1612424 2.59934589 -1.46124241</span></span>
-<span id="cb2-284"><a href="#cb2-284" tabindex="-1"></a><span class="co">#> 4 1 25 60 5.0 10.0 20 20.0 5 1 1 -0.750 -1.500 0.6527215 1.3949219 -0.09727852 -0.10507806</span></span>
-<span id="cb2-285"><a href="#cb2-285" tabindex="-1"></a><span class="co">#> 5 1 29 20 5.0 10.0 80 5.0 0 1 1 -0.350 -1.150 2.6927356 -1.3232777 2.34273564 -2.47327770</span></span>
-<span id="cb2-286"><a href="#cb2-286" tabindex="-1"></a><span class="co">#> 6 1 32 40 10.0 2.5 80 2.5 5 1 1 -1.050 -0.875 0.3758168 0.8556930 -0.67418318 -0.01930696</span></span>
-<span id="cb2-287"><a href="#cb2-287" tabindex="-1"></a><span class="co">#> CHOICE</span></span>
-<span id="cb2-288"><a href="#cb2-288" tabindex="-1"></a><span class="co">#> 1 1</span></span>
-<span id="cb2-289"><a href="#cb2-289" tabindex="-1"></a><span class="co">#> 2 1</span></span>
-<span id="cb2-290"><a href="#cb2-290" tabindex="-1"></a><span class="co">#> 3 1</span></span>
-<span id="cb2-291"><a href="#cb2-291" tabindex="-1"></a><span class="co">#> 4 1</span></span>
-<span id="cb2-292"><a href="#cb2-292" tabindex="-1"></a><span class="co">#> 5 1</span></span>
-<span id="cb2-293"><a href="#cb2-293" tabindex="-1"></a><span class="co">#> 6 2</span></span>
-<span id="cb2-294"><a href="#cb2-294" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-295"><a href="#cb2-295" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-296"><a href="#cb2-296" tabindex="-1"></a><span class="co">#> This is the utility functions </span></span>
-<span id="cb2-297"><a href="#cb2-297" tabindex="-1"></a><span class="co">#> U_1 = @bpreis *$alt1_x1 + @blade *$alt1_x2 + @bwarte *$alt1_x3 ;U_2 = @bpreis *$alt2_x1 + @blade *$alt2_x2 + @bwarte *$alt2_x3 ;Initial function value: -998.1319 </span></span>
-<span id="cb2-298"><a href="#cb2-298" tabindex="-1"></a><span class="co">#> Initial gradient value:</span></span>
-<span id="cb2-299"><a href="#cb2-299" tabindex="-1"></a><span class="co">#> bpreis blade bwarte </span></span>
-<span id="cb2-300"><a href="#cb2-300" tabindex="-1"></a><span class="co">#> -2340.0 -857.5 510.0 </span></span>
-<span id="cb2-301"><a href="#cb2-301" tabindex="-1"></a><span class="co">#> initial value 998.131940 </span></span>
-<span id="cb2-302"><a href="#cb2-302" tabindex="-1"></a><span class="co">#> iter 2 value 989.566757</span></span>
-<span id="cb2-303"><a href="#cb2-303" tabindex="-1"></a><span class="co">#> iter 3 value 968.906950</span></span>
-<span id="cb2-304"><a href="#cb2-304" tabindex="-1"></a><span class="co">#> iter 4 value 968.775516</span></span>
-<span id="cb2-305"><a href="#cb2-305" tabindex="-1"></a><span class="co">#> iter 5 value 959.377427</span></span>
-<span id="cb2-306"><a href="#cb2-306" tabindex="-1"></a><span class="co">#> iter 6 value 959.364632</span></span>
-<span id="cb2-307"><a href="#cb2-307" tabindex="-1"></a><span class="co">#> iter 7 value 959.364588</span></span>
-<span id="cb2-308"><a href="#cb2-308" tabindex="-1"></a><span class="co">#> iter 7 value 959.364588</span></span>
-<span id="cb2-309"><a href="#cb2-309" tabindex="-1"></a><span class="co">#> iter 7 value 959.364588</span></span>
-<span id="cb2-310"><a href="#cb2-310" tabindex="-1"></a><span class="co">#> final value 959.364588 </span></span>
-<span id="cb2-311"><a href="#cb2-311" tabindex="-1"></a><span class="co">#> converged</span></span>
-<span id="cb2-312"><a href="#cb2-312" tabindex="-1"></a><span class="co">#> This is Run number 2 </span></span>
-<span id="cb2-313"><a href="#cb2-313" tabindex="-1"></a><span class="co">#> does sou_gis exist: FALSE </span></span>
-<span id="cb2-314"><a href="#cb2-314" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-315"><a href="#cb2-315" tabindex="-1"></a><span class="co">#> dataset final_set exists: FALSE </span></span>
-<span id="cb2-316"><a href="#cb2-316" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-317"><a href="#cb2-317" tabindex="-1"></a><span class="co">#> decisiongroups exists: TRUE</span></span>
-<span id="cb2-318"><a href="#cb2-318" tabindex="-1"></a><span class="co">#> 1 2 </span></span>
-<span id="cb2-319"><a href="#cb2-319" tabindex="-1"></a><span class="co">#> 1007 433 </span></span>
-<span id="cb2-320"><a href="#cb2-320" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-321"><a href="#cb2-321" tabindex="-1"></a><span class="co">#> data has been made </span></span>
-<span id="cb2-322"><a href="#cb2-322" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-323"><a href="#cb2-323" tabindex="-1"></a><span class="co">#> First few observations </span></span>
-<span id="cb2-324"><a href="#cb2-324" tabindex="-1"></a><span class="co">#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block group V_1 V_2 e_1 e_2 U_1</span></span>
-<span id="cb2-325"><a href="#cb2-325" tabindex="-1"></a><span class="co">#> 1 1 12 60 2.5 0.0 20 20.0 10 1 1 -0.775 -1.400 -0.5067469 -4.908678e-05 -1.2817469</span></span>
-<span id="cb2-326"><a href="#cb2-326" tabindex="-1"></a><span class="co">#> 2 1 16 20 10.0 5.0 40 5.0 0 1 1 -0.800 -0.750 2.1209149 5.835693e-01 1.3209149</span></span>
-<span id="cb2-327"><a href="#cb2-327" tabindex="-1"></a><span class="co">#> 3 1 17 20 20.0 0.0 80 10.0 10 1 1 -1.600 -1.300 -0.3310010 7.106139e-01 -1.9310010</span></span>
-<span id="cb2-328"><a href="#cb2-328" tabindex="-1"></a><span class="co">#> 4 1 25 60 5.0 10.0 20 20.0 5 1 1 -0.750 -1.500 1.0469501 3.053872e-01 0.2969501</span></span>
-<span id="cb2-329"><a href="#cb2-329" tabindex="-1"></a><span class="co">#> 5 1 29 20 5.0 10.0 80 5.0 0 1 1 -0.350 -1.150 1.2182730 -6.215119e-01 0.8682730</span></span>
-<span id="cb2-330"><a href="#cb2-330" tabindex="-1"></a><span class="co">#> 6 1 32 40 10.0 2.5 80 2.5 5 1 1 -1.050 -0.875 -0.3318808 5.251218e+00 -1.3818808</span></span>
-<span id="cb2-331"><a href="#cb2-331" tabindex="-1"></a><span class="co">#> U_2 CHOICE</span></span>
-<span id="cb2-332"><a href="#cb2-332" tabindex="-1"></a><span class="co">#> 1 -1.4000491 1</span></span>
-<span id="cb2-333"><a href="#cb2-333" tabindex="-1"></a><span class="co">#> 2 -0.1664307 1</span></span>
-<span id="cb2-334"><a href="#cb2-334" tabindex="-1"></a><span class="co">#> 3 -0.5893861 2</span></span>
-<span id="cb2-335"><a href="#cb2-335" tabindex="-1"></a><span class="co">#> 4 -1.1946128 1</span></span>
-<span id="cb2-336"><a href="#cb2-336" tabindex="-1"></a><span class="co">#> 5 -1.7715119 1</span></span>
-<span id="cb2-337"><a href="#cb2-337" tabindex="-1"></a><span class="co">#> 6 4.3762180 2</span></span>
-<span id="cb2-338"><a href="#cb2-338" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-339"><a href="#cb2-339" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-340"><a href="#cb2-340" tabindex="-1"></a><span class="co">#> This is the utility functions </span></span>
-<span id="cb2-341"><a href="#cb2-341" tabindex="-1"></a><span class="co">#> U_1 = @bpreis *$alt1_x1 + @blade *$alt1_x2 + @bwarte *$alt1_x3 ;U_2 = @bpreis *$alt2_x1 + @blade *$alt2_x2 + @bwarte *$alt2_x3 ;Initial function value: -998.1319 </span></span>
-<span id="cb2-342"><a href="#cb2-342" tabindex="-1"></a><span class="co">#> Initial gradient value:</span></span>
-<span id="cb2-343"><a href="#cb2-343" tabindex="-1"></a><span class="co">#> bpreis blade bwarte </span></span>
-<span id="cb2-344"><a href="#cb2-344" tabindex="-1"></a><span class="co">#> -2120.0 -842.5 582.5 </span></span>
-<span id="cb2-345"><a href="#cb2-345" tabindex="-1"></a><span class="co">#> initial value 998.131940 </span></span>
-<span id="cb2-346"><a href="#cb2-346" tabindex="-1"></a><span class="co">#> iter 2 value 990.498814</span></span>
-<span id="cb2-347"><a href="#cb2-347" tabindex="-1"></a><span class="co">#> iter 3 value 970.036278</span></span>
-<span id="cb2-348"><a href="#cb2-348" tabindex="-1"></a><span class="co">#> iter 4 value 970.031934</span></span>
-<span id="cb2-349"><a href="#cb2-349" tabindex="-1"></a><span class="co">#> iter 5 value 961.943463</span></span>
-<span id="cb2-350"><a href="#cb2-350" tabindex="-1"></a><span class="co">#> iter 6 value 961.698866</span></span>
-<span id="cb2-351"><a href="#cb2-351" tabindex="-1"></a><span class="co">#> iter 7 value 961.698562</span></span>
-<span id="cb2-352"><a href="#cb2-352" tabindex="-1"></a><span class="co">#> iter 7 value 961.698561</span></span>
-<span id="cb2-353"><a href="#cb2-353" tabindex="-1"></a><span class="co">#> iter 7 value 961.698561</span></span>
-<span id="cb2-354"><a href="#cb2-354" tabindex="-1"></a><span class="co">#> final value 961.698561 </span></span>
-<span id="cb2-355"><a href="#cb2-355" tabindex="-1"></a><span class="co">#> converged</span></span>
-<span id="cb2-356"><a href="#cb2-356" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-357"><a href="#cb2-357" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-358"><a href="#cb2-358" tabindex="-1"></a><span class="co">#> ================ ==== === ===== ==== ===== ===== ===== ====</span></span>
-<span id="cb2-359"><a href="#cb2-359" tabindex="-1"></a><span class="co">#> \ vars n mean sd min max range se</span></span>
-<span id="cb2-360"><a href="#cb2-360" tabindex="-1"></a><span class="co">#> ================ ==== === ===== ==== ===== ===== ===== ====</span></span>
-<span id="cb2-361"><a href="#cb2-361" tabindex="-1"></a><span class="co">#> est_bpreis 1 2 -0.01 0.00 -0.01 -0.01 0.00 0.00</span></span>
-<span id="cb2-362"><a href="#cb2-362" tabindex="-1"></a><span class="co">#> est_blade 2 2 -0.05 0.00 -0.05 -0.05 0.00 0.00</span></span>
-<span id="cb2-363"><a href="#cb2-363" tabindex="-1"></a><span class="co">#> est_bwarte 3 2 0.02 0.01 0.01 0.02 0.01 0.00</span></span>
-<span id="cb2-364"><a href="#cb2-364" tabindex="-1"></a><span class="co">#> rob_pval0_bpreis 4 2 0.00 0.00 0.00 0.00 0.00 0.00</span></span>
-<span id="cb2-365"><a href="#cb2-365" tabindex="-1"></a><span class="co">#> rob_pval0_blade 5 2 0.00 0.00 0.00 0.00 0.00 0.00</span></span>
-<span id="cb2-366"><a href="#cb2-366" tabindex="-1"></a><span class="co">#> rob_pval0_bwarte 6 2 0.16 0.19 0.03 0.30 0.27 0.13</span></span>
-<span id="cb2-367"><a href="#cb2-367" tabindex="-1"></a><span class="co">#> ================ ==== === ===== ==== ===== ===== ===== ====</span></span>
-<span id="cb2-368"><a href="#cb2-368" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-369"><a href="#cb2-369" tabindex="-1"></a><span class="co">#> FALSE TRUE </span></span>
-<span id="cb2-370"><a href="#cb2-370" tabindex="-1"></a><span class="co">#> 50 50 </span></span>
-<span id="cb2-371"><a href="#cb2-371" tabindex="-1"></a><span class="co">#> Utility function used in simulation, ie the true utility: </span></span>
-<span id="cb2-372"><a href="#cb2-372" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-373"><a href="#cb2-373" tabindex="-1"></a><span class="co">#> $u1</span></span>
-<span id="cb2-374"><a href="#cb2-374" tabindex="-1"></a><span class="co">#> $u1$v1</span></span>
-<span id="cb2-375"><a href="#cb2-375" tabindex="-1"></a><span class="co">#> V.1 ~ bpreis * alt1.x1 + blade * alt1.x2 + bwarte * alt1.x3</span></span>
-<span id="cb2-376"><a href="#cb2-376" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-377"><a href="#cb2-377" tabindex="-1"></a><span class="co">#> $u1$v2</span></span>
-<span id="cb2-378"><a href="#cb2-378" tabindex="-1"></a><span class="co">#> V.2 ~ bpreis * alt2.x1 + blade * alt2.x2 + bwarte * alt2.x3</span></span>
-<span id="cb2-379"><a href="#cb2-379" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-380"><a href="#cb2-380" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-381"><a href="#cb2-381" tabindex="-1"></a><span class="co">#> $u2</span></span>
-<span id="cb2-382"><a href="#cb2-382" tabindex="-1"></a><span class="co">#> $u2$v1</span></span>
-<span id="cb2-383"><a href="#cb2-383" tabindex="-1"></a><span class="co">#> V.1 ~ bpreis * alt1.x1</span></span>
-<span id="cb2-384"><a href="#cb2-384" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-385"><a href="#cb2-385" tabindex="-1"></a><span class="co">#> $u2$v2</span></span>
-<span id="cb2-386"><a href="#cb2-386" tabindex="-1"></a><span class="co">#> V.2 ~ bpreis * alt2.x1</span></span>
-<span id="cb2-387"><a href="#cb2-387" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-388"><a href="#cb2-388" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-389"><a href="#cb2-389" tabindex="-1"></a><span class="co">#> Utility function used for Logit estimation with mixl: </span></span>
-<span id="cb2-390"><a href="#cb2-390" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-391"><a href="#cb2-391" tabindex="-1"></a><span class="co">#> [1] "U_1 = @bpreis *$alt1_x1 + @blade *$alt1_x2 + @bwarte *$alt1_x3 ;U_2 = @bpreis *$alt2_x1 + @blade *$alt2_x2 + @bwarte *$alt2_x3 ;"</span></span>
-<span id="cb2-392"><a href="#cb2-392" tabindex="-1"></a><span class="co">#> New names:</span></span>
-<span id="cb2-393"><a href="#cb2-393" tabindex="-1"></a><span class="co">#> • `Choice situation` -> `Choice.situation`</span></span>
-<span id="cb2-394"><a href="#cb2-394" tabindex="-1"></a><span class="co">#> • `` -> `...10`</span></span>
-<span id="cb2-395"><a href="#cb2-395" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-396"><a href="#cb2-396" tabindex="-1"></a><span class="co">#> does sou_gis exist: FALSE </span></span>
-<span id="cb2-397"><a href="#cb2-397" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-398"><a href="#cb2-398" tabindex="-1"></a><span class="co">#> dataset final_set exists: FALSE </span></span>
-<span id="cb2-399"><a href="#cb2-399" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-400"><a href="#cb2-400" tabindex="-1"></a><span class="co">#> decisiongroups exists: TRUE</span></span>
-<span id="cb2-401"><a href="#cb2-401" tabindex="-1"></a><span class="co">#> 1 2 </span></span>
-<span id="cb2-402"><a href="#cb2-402" tabindex="-1"></a><span class="co">#> 1007 433 </span></span>
-<span id="cb2-403"><a href="#cb2-403" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-404"><a href="#cb2-404" tabindex="-1"></a><span class="co">#> data has been made </span></span>
-<span id="cb2-405"><a href="#cb2-405" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-406"><a href="#cb2-406" tabindex="-1"></a><span class="co">#> First few observations </span></span>
-<span id="cb2-407"><a href="#cb2-407" tabindex="-1"></a><span class="co">#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block group V_1 V_2 e_1 e_2 U_1</span></span>
-<span id="cb2-408"><a href="#cb2-408" tabindex="-1"></a><span class="co">#> 1 1 3 80 5.0 0.0 20 5.0 10.0 1 1 -1.150 -0.350 1.88045081 0.33059180 0.7304508</span></span>
-<span id="cb2-409"><a href="#cb2-409" tabindex="-1"></a><span class="co">#> 2 1 5 60 2.5 5.0 20 20.0 5.0 1 1 -0.675 -1.500 -0.08733163 -0.07195918 -0.7623316</span></span>
-<span id="cb2-410"><a href="#cb2-410" tabindex="-1"></a><span class="co">#> 3 1 10 80 2.5 2.5 20 20.0 0.0 1 1 -0.925 -1.600 -0.31269859 3.95512677 -1.2376986</span></span>
-<span id="cb2-411"><a href="#cb2-411" tabindex="-1"></a><span class="co">#> 4 1 34 80 2.5 5.0 60 5.0 5.0 1 1 -0.875 -0.850 0.20206751 -0.87018279 -0.6729325</span></span>
-<span id="cb2-412"><a href="#cb2-412" tabindex="-1"></a><span class="co">#> 5 1 37 40 5.0 10.0 60 5.0 2.5 1 1 -0.550 -0.900 -0.25607132 -1.21928402 -0.8060713</span></span>
-<span id="cb2-413"><a href="#cb2-413" tabindex="-1"></a><span class="co">#> 6 1 39 20 20.0 2.5 60 2.5 2.5 1 1 -1.550 -0.725 1.39833272 -0.08165078 -0.1516673</span></span>
-<span id="cb2-414"><a href="#cb2-414" tabindex="-1"></a><span class="co">#> U_2 CHOICE</span></span>
-<span id="cb2-415"><a href="#cb2-415" tabindex="-1"></a><span class="co">#> 1 -0.0194082 1</span></span>
-<span id="cb2-416"><a href="#cb2-416" tabindex="-1"></a><span class="co">#> 2 -1.5719592 1</span></span>
-<span id="cb2-417"><a href="#cb2-417" tabindex="-1"></a><span class="co">#> 3 2.3551268 2</span></span>
-<span id="cb2-418"><a href="#cb2-418" tabindex="-1"></a><span class="co">#> 4 -1.7201828 1</span></span>
-<span id="cb2-419"><a href="#cb2-419" tabindex="-1"></a><span class="co">#> 5 -2.1192840 1</span></span>
-<span id="cb2-420"><a href="#cb2-420" tabindex="-1"></a><span class="co">#> 6 -0.8066508 1</span></span>
-<span id="cb2-421"><a href="#cb2-421" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-422"><a href="#cb2-422" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-423"><a href="#cb2-423" tabindex="-1"></a><span class="co">#> This is Run number 1 </span></span>
-<span id="cb2-424"><a href="#cb2-424" tabindex="-1"></a><span class="co">#> does sou_gis exist: FALSE </span></span>
-<span id="cb2-425"><a href="#cb2-425" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-426"><a href="#cb2-426" tabindex="-1"></a><span class="co">#> dataset final_set exists: FALSE </span></span>
-<span id="cb2-427"><a href="#cb2-427" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-428"><a href="#cb2-428" tabindex="-1"></a><span class="co">#> decisiongroups exists: TRUE</span></span>
-<span id="cb2-429"><a href="#cb2-429" tabindex="-1"></a><span class="co">#> 1 2 </span></span>
-<span id="cb2-430"><a href="#cb2-430" tabindex="-1"></a><span class="co">#> 1007 433 </span></span>
-<span id="cb2-431"><a href="#cb2-431" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-432"><a href="#cb2-432" tabindex="-1"></a><span class="co">#> data has been made </span></span>
-<span id="cb2-433"><a href="#cb2-433" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-434"><a href="#cb2-434" tabindex="-1"></a><span class="co">#> First few observations </span></span>
-<span id="cb2-435"><a href="#cb2-435" tabindex="-1"></a><span class="co">#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block group V_1 V_2 e_1 e_2 U_1</span></span>
-<span id="cb2-436"><a href="#cb2-436" tabindex="-1"></a><span class="co">#> 1 1 3 80 5.0 0.0 20 5.0 10.0 1 1 -1.150 -0.350 -0.0940785 -0.8728874 -1.24407850</span></span>
-<span id="cb2-437"><a href="#cb2-437" tabindex="-1"></a><span class="co">#> 2 1 5 60 2.5 5.0 20 20.0 5.0 1 1 -0.675 -1.500 -0.6796651 -1.1297414 -1.35466507</span></span>
-<span id="cb2-438"><a href="#cb2-438" tabindex="-1"></a><span class="co">#> 3 1 10 80 2.5 2.5 20 20.0 0.0 1 1 -0.925 -1.600 1.7899847 0.6372528 0.86498471</span></span>
-<span id="cb2-439"><a href="#cb2-439" tabindex="-1"></a><span class="co">#> 4 1 34 80 2.5 5.0 60 5.0 5.0 1 1 -0.875 -0.850 0.9429192 0.7744473 0.06791921</span></span>
-<span id="cb2-440"><a href="#cb2-440" tabindex="-1"></a><span class="co">#> 5 1 37 40 5.0 10.0 60 5.0 2.5 1 1 -0.550 -0.900 0.1003092 2.5583115 -0.44969083</span></span>
-<span id="cb2-441"><a href="#cb2-441" tabindex="-1"></a><span class="co">#> 6 1 39 20 20.0 2.5 60 2.5 2.5 1 1 -1.550 -0.725 -0.3851894 0.9776369 -1.93518939</span></span>
-<span id="cb2-442"><a href="#cb2-442" tabindex="-1"></a><span class="co">#> U_2 CHOICE</span></span>
-<span id="cb2-443"><a href="#cb2-443" tabindex="-1"></a><span class="co">#> 1 -1.22288745 2</span></span>
-<span id="cb2-444"><a href="#cb2-444" tabindex="-1"></a><span class="co">#> 2 -2.62974141 1</span></span>
-<span id="cb2-445"><a href="#cb2-445" tabindex="-1"></a><span class="co">#> 3 -0.96274717 1</span></span>
-<span id="cb2-446"><a href="#cb2-446" tabindex="-1"></a><span class="co">#> 4 -0.07555271 1</span></span>
-<span id="cb2-447"><a href="#cb2-447" tabindex="-1"></a><span class="co">#> 5 1.65831145 2</span></span>
-<span id="cb2-448"><a href="#cb2-448" tabindex="-1"></a><span class="co">#> 6 0.25263691 2</span></span>
-<span id="cb2-449"><a href="#cb2-449" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-450"><a href="#cb2-450" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-451"><a href="#cb2-451" tabindex="-1"></a><span class="co">#> This is the utility functions </span></span>
-<span id="cb2-452"><a href="#cb2-452" tabindex="-1"></a><span class="co">#> U_1 = @bpreis *$alt1_x1 + @blade *$alt1_x2 + @bwarte *$alt1_x3 ;U_2 = @bpreis *$alt2_x1 + @blade *$alt2_x2 + @bwarte *$alt2_x3 ;Initial function value: -998.1319 </span></span>
-<span id="cb2-453"><a href="#cb2-453" tabindex="-1"></a><span class="co">#> Initial gradient value:</span></span>
-<span id="cb2-454"><a href="#cb2-454" tabindex="-1"></a><span class="co">#> bpreis blade bwarte </span></span>
-<span id="cb2-455"><a href="#cb2-455" tabindex="-1"></a><span class="co">#> -2300.0 -967.5 417.5 </span></span>
-<span id="cb2-456"><a href="#cb2-456" tabindex="-1"></a><span class="co">#> initial value 998.131940 </span></span>
-<span id="cb2-457"><a href="#cb2-457" tabindex="-1"></a><span class="co">#> iter 2 value 989.783897</span></span>
-<span id="cb2-458"><a href="#cb2-458" tabindex="-1"></a><span class="co">#> iter 3 value 967.441065</span></span>
-<span id="cb2-459"><a href="#cb2-459" tabindex="-1"></a><span class="co">#> iter 4 value 966.807343</span></span>
-<span id="cb2-460"><a href="#cb2-460" tabindex="-1"></a><span class="co">#> iter 5 value 957.535574</span></span>
-<span id="cb2-461"><a href="#cb2-461" tabindex="-1"></a><span class="co">#> iter 6 value 957.518843</span></span>
-<span id="cb2-462"><a href="#cb2-462" tabindex="-1"></a><span class="co">#> iter 7 value 957.518805</span></span>
-<span id="cb2-463"><a href="#cb2-463" tabindex="-1"></a><span class="co">#> iter 7 value 957.518805</span></span>
-<span id="cb2-464"><a href="#cb2-464" tabindex="-1"></a><span class="co">#> iter 7 value 957.518805</span></span>
-<span id="cb2-465"><a href="#cb2-465" tabindex="-1"></a><span class="co">#> final value 957.518805 </span></span>
-<span id="cb2-466"><a href="#cb2-466" tabindex="-1"></a><span class="co">#> converged</span></span>
-<span id="cb2-467"><a href="#cb2-467" tabindex="-1"></a><span class="co">#> This is Run number 2 </span></span>
-<span id="cb2-468"><a href="#cb2-468" tabindex="-1"></a><span class="co">#> does sou_gis exist: FALSE </span></span>
-<span id="cb2-469"><a href="#cb2-469" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-470"><a href="#cb2-470" tabindex="-1"></a><span class="co">#> dataset final_set exists: FALSE </span></span>
-<span id="cb2-471"><a href="#cb2-471" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-472"><a href="#cb2-472" tabindex="-1"></a><span class="co">#> decisiongroups exists: TRUE</span></span>
-<span id="cb2-473"><a href="#cb2-473" tabindex="-1"></a><span class="co">#> 1 2 </span></span>
-<span id="cb2-474"><a href="#cb2-474" tabindex="-1"></a><span class="co">#> 1007 433 </span></span>
-<span id="cb2-475"><a href="#cb2-475" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-476"><a href="#cb2-476" tabindex="-1"></a><span class="co">#> data has been made </span></span>
-<span id="cb2-477"><a href="#cb2-477" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-478"><a href="#cb2-478" tabindex="-1"></a><span class="co">#> First few observations </span></span>
-<span id="cb2-479"><a href="#cb2-479" tabindex="-1"></a><span class="co">#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block group V_1 V_2 e_1 e_2 U_1</span></span>
-<span id="cb2-480"><a href="#cb2-480" tabindex="-1"></a><span class="co">#> 1 1 3 80 5.0 0.0 20 5.0 10.0 1 1 -1.150 -0.350 0.39615659 0.74248610 -0.7538434</span></span>
-<span id="cb2-481"><a href="#cb2-481" tabindex="-1"></a><span class="co">#> 2 1 5 60 2.5 5.0 20 20.0 5.0 1 1 -0.675 -1.500 -0.17578286 0.04260786 -0.8507829</span></span>
-<span id="cb2-482"><a href="#cb2-482" tabindex="-1"></a><span class="co">#> 3 1 10 80 2.5 2.5 20 20.0 0.0 1 1 -0.925 -1.600 0.44905385 0.79514566 -0.4759461</span></span>
-<span id="cb2-483"><a href="#cb2-483" tabindex="-1"></a><span class="co">#> 4 1 34 80 2.5 5.0 60 5.0 5.0 1 1 -0.875 -0.850 0.27140789 4.63953174 -0.6035921</span></span>
-<span id="cb2-484"><a href="#cb2-484" tabindex="-1"></a><span class="co">#> 5 1 37 40 5.0 10.0 60 5.0 2.5 1 1 -0.550 -0.900 -0.03370054 0.84622952 -0.5837005</span></span>
-<span id="cb2-485"><a href="#cb2-485" tabindex="-1"></a><span class="co">#> 6 1 39 20 20.0 2.5 60 2.5 2.5 1 1 -1.550 -0.725 -0.47233862 1.05805421 -2.0223386</span></span>
-<span id="cb2-486"><a href="#cb2-486" tabindex="-1"></a><span class="co">#> U_2 CHOICE</span></span>
-<span id="cb2-487"><a href="#cb2-487" tabindex="-1"></a><span class="co">#> 1 0.39248610 2</span></span>
-<span id="cb2-488"><a href="#cb2-488" tabindex="-1"></a><span class="co">#> 2 -1.45739214 1</span></span>
-<span id="cb2-489"><a href="#cb2-489" tabindex="-1"></a><span class="co">#> 3 -0.80485434 1</span></span>
-<span id="cb2-490"><a href="#cb2-490" tabindex="-1"></a><span class="co">#> 4 3.78953174 2</span></span>
-<span id="cb2-491"><a href="#cb2-491" tabindex="-1"></a><span class="co">#> 5 -0.05377048 2</span></span>
-<span id="cb2-492"><a href="#cb2-492" tabindex="-1"></a><span class="co">#> 6 0.33305421 2</span></span>
-<span id="cb2-493"><a href="#cb2-493" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-494"><a href="#cb2-494" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-495"><a href="#cb2-495" tabindex="-1"></a><span class="co">#> This is the utility functions </span></span>
-<span id="cb2-496"><a href="#cb2-496" tabindex="-1"></a><span class="co">#> U_1 = @bpreis *$alt1_x1 + @blade *$alt1_x2 + @bwarte *$alt1_x3 ;U_2 = @bpreis *$alt2_x1 + @blade *$alt2_x2 + @bwarte *$alt2_x3 ;Initial function value: -998.1319 </span></span>
-<span id="cb2-497"><a href="#cb2-497" tabindex="-1"></a><span class="co">#> Initial gradient value:</span></span>
-<span id="cb2-498"><a href="#cb2-498" tabindex="-1"></a><span class="co">#> bpreis blade bwarte </span></span>
-<span id="cb2-499"><a href="#cb2-499" tabindex="-1"></a><span class="co">#> -1600.0 -857.5 402.5 </span></span>
-<span id="cb2-500"><a href="#cb2-500" tabindex="-1"></a><span class="co">#> initial value 998.131940 </span></span>
-<span id="cb2-501"><a href="#cb2-501" tabindex="-1"></a><span class="co">#> iter 2 value 993.094191</span></span>
-<span id="cb2-502"><a href="#cb2-502" tabindex="-1"></a><span class="co">#> iter 3 value 975.977284</span></span>
-<span id="cb2-503"><a href="#cb2-503" tabindex="-1"></a><span class="co">#> iter 4 value 975.860148</span></span>
-<span id="cb2-504"><a href="#cb2-504" tabindex="-1"></a><span class="co">#> iter 5 value 971.032264</span></span>
-<span id="cb2-505"><a href="#cb2-505" tabindex="-1"></a><span class="co">#> iter 6 value 971.027871</span></span>
-<span id="cb2-506"><a href="#cb2-506" tabindex="-1"></a><span class="co">#> iter 6 value 971.027867</span></span>
-<span id="cb2-507"><a href="#cb2-507" tabindex="-1"></a><span class="co">#> iter 6 value 971.027867</span></span>
-<span id="cb2-508"><a href="#cb2-508" tabindex="-1"></a><span class="co">#> final value 971.027867 </span></span>
-<span id="cb2-509"><a href="#cb2-509" tabindex="-1"></a><span class="co">#> converged</span></span>
-<span id="cb2-510"><a href="#cb2-510" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-511"><a href="#cb2-511" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-512"><a href="#cb2-512" tabindex="-1"></a><span class="co">#> ================ ==== === ===== ==== ===== ===== ===== ====</span></span>
-<span id="cb2-513"><a href="#cb2-513" tabindex="-1"></a><span class="co">#> \ vars n mean sd min max range se</span></span>
-<span id="cb2-514"><a href="#cb2-514" tabindex="-1"></a><span class="co">#> ================ ==== === ===== ==== ===== ===== ===== ====</span></span>
-<span id="cb2-515"><a href="#cb2-515" tabindex="-1"></a><span class="co">#> est_bpreis 1 2 -0.01 0.00 -0.01 -0.01 0.00 0.00</span></span>
-<span id="cb2-516"><a href="#cb2-516" tabindex="-1"></a><span class="co">#> est_blade 2 2 -0.05 0.01 -0.05 -0.04 0.01 0.01</span></span>
-<span id="cb2-517"><a href="#cb2-517" tabindex="-1"></a><span class="co">#> est_bwarte 3 2 0.00 0.01 0.00 0.01 0.01 0.00</span></span>
-<span id="cb2-518"><a href="#cb2-518" tabindex="-1"></a><span class="co">#> rob_pval0_bpreis 4 2 0.00 0.00 0.00 0.00 0.00 0.00</span></span>
-<span id="cb2-519"><a href="#cb2-519" tabindex="-1"></a><span class="co">#> rob_pval0_blade 5 2 0.00 0.00 0.00 0.00 0.00 0.00</span></span>
-<span id="cb2-520"><a href="#cb2-520" tabindex="-1"></a><span class="co">#> rob_pval0_bwarte 6 2 0.68 0.20 0.54 0.82 0.28 0.14</span></span>
-<span id="cb2-521"><a href="#cb2-521" tabindex="-1"></a><span class="co">#> ================ ==== === ===== ==== ===== ===== ===== ====</span></span>
-<span id="cb2-522"><a href="#cb2-522" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-523"><a href="#cb2-523" tabindex="-1"></a><span class="co">#> FALSE </span></span>
-<span id="cb2-524"><a href="#cb2-524" tabindex="-1"></a><span class="co">#> 100 </span></span>
-<span id="cb2-525"><a href="#cb2-525" tabindex="-1"></a><span class="co">#> Utility function used in simulation, ie the true utility: </span></span>
-<span id="cb2-526"><a href="#cb2-526" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-527"><a href="#cb2-527" tabindex="-1"></a><span class="co">#> $u1</span></span>
-<span id="cb2-528"><a href="#cb2-528" tabindex="-1"></a><span class="co">#> $u1$v1</span></span>
-<span id="cb2-529"><a href="#cb2-529" tabindex="-1"></a><span class="co">#> V.1 ~ bpreis * alt1.x1 + blade * alt1.x2 + bwarte * alt1.x3</span></span>
-<span id="cb2-530"><a href="#cb2-530" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-531"><a href="#cb2-531" tabindex="-1"></a><span class="co">#> $u1$v2</span></span>
-<span id="cb2-532"><a href="#cb2-532" tabindex="-1"></a><span class="co">#> V.2 ~ bpreis * alt2.x1 + blade * alt2.x2 + bwarte * alt2.x3</span></span>
-<span id="cb2-533"><a href="#cb2-533" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-534"><a href="#cb2-534" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-535"><a href="#cb2-535" tabindex="-1"></a><span class="co">#> $u2</span></span>
-<span id="cb2-536"><a href="#cb2-536" tabindex="-1"></a><span class="co">#> $u2$v1</span></span>
-<span id="cb2-537"><a href="#cb2-537" tabindex="-1"></a><span class="co">#> V.1 ~ bpreis * alt1.x1</span></span>
-<span id="cb2-538"><a href="#cb2-538" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-539"><a href="#cb2-539" tabindex="-1"></a><span class="co">#> $u2$v2</span></span>
-<span id="cb2-540"><a href="#cb2-540" tabindex="-1"></a><span class="co">#> V.2 ~ bpreis * alt2.x1</span></span>
-<span id="cb2-541"><a href="#cb2-541" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-542"><a href="#cb2-542" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-543"><a href="#cb2-543" tabindex="-1"></a><span class="co">#> Utility function used for Logit estimation with mixl: </span></span>
-<span id="cb2-544"><a href="#cb2-544" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-545"><a href="#cb2-545" tabindex="-1"></a><span class="co">#> [1] "U_1 = @bpreis *$alt1_x1 + @blade *$alt1_x2 + @bwarte *$alt1_x3 ;U_2 = @bpreis *$alt2_x1 + @blade *$alt2_x2 + @bwarte *$alt2_x3 ;"</span></span>
-<span id="cb2-546"><a href="#cb2-546" tabindex="-1"></a><span class="co">#> New names:</span></span>
-<span id="cb2-547"><a href="#cb2-547" tabindex="-1"></a><span class="co">#> • `Choice situation` -> `Choice.situation`</span></span>
-<span id="cb2-548"><a href="#cb2-548" tabindex="-1"></a><span class="co">#> • `` -> `...10`</span></span>
-<span id="cb2-549"><a href="#cb2-549" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-550"><a href="#cb2-550" tabindex="-1"></a><span class="co">#> does sou_gis exist: FALSE </span></span>
-<span id="cb2-551"><a href="#cb2-551" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-552"><a href="#cb2-552" tabindex="-1"></a><span class="co">#> dataset final_set exists: FALSE </span></span>
-<span id="cb2-553"><a href="#cb2-553" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-554"><a href="#cb2-554" tabindex="-1"></a><span class="co">#> decisiongroups exists: TRUE</span></span>
-<span id="cb2-555"><a href="#cb2-555" tabindex="-1"></a><span class="co">#> 1 2 </span></span>
-<span id="cb2-556"><a href="#cb2-556" tabindex="-1"></a><span class="co">#> 1007 433 </span></span>
-<span id="cb2-557"><a href="#cb2-557" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-558"><a href="#cb2-558" tabindex="-1"></a><span class="co">#> data has been made </span></span>
-<span id="cb2-559"><a href="#cb2-559" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-560"><a href="#cb2-560" tabindex="-1"></a><span class="co">#> First few observations </span></span>
-<span id="cb2-561"><a href="#cb2-561" tabindex="-1"></a><span class="co">#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block group V_1 V_2 e_1 e_2 U_1 U_2</span></span>
-<span id="cb2-562"><a href="#cb2-562" tabindex="-1"></a><span class="co">#> 1 1 9 80 5.0 0 60 20.0 10.0 1 1 -1.150 -1.800 0.8053210 -0.7760447 -0.34467900 -2.5760447</span></span>
-<span id="cb2-563"><a href="#cb2-563" tabindex="-1"></a><span class="co">#> 2 1 12 60 2.5 10 40 20.0 0.0 1 1 -0.575 -1.800 2.4581484 -0.5422855 1.88314842 -2.3422855</span></span>
-<span id="cb2-564"><a href="#cb2-564" tabindex="-1"></a><span class="co">#> 3 1 13 20 20.0 10 80 2.5 0.0 1 1 -1.400 -0.975 0.4806134 -1.7030310 -0.91938664 -2.6780310</span></span>
-<span id="cb2-565"><a href="#cb2-565" tabindex="-1"></a><span class="co">#> 4 1 70 80 5.0 10 20 20.0 2.5 1 1 -0.950 -1.550 -0.8558539 1.9784273 -1.80585392 0.4284273</span></span>
-<span id="cb2-566"><a href="#cb2-566" tabindex="-1"></a><span class="co">#> 5 1 71 60 20.0 10 80 10.0 0.0 1 1 -1.800 -1.500 1.4200481 0.8856199 -0.37995187 -0.6143801</span></span>
-<span id="cb2-567"><a href="#cb2-567" tabindex="-1"></a><span class="co">#> 6 1 73 60 10.0 0 40 20.0 10.0 1 1 -1.300 -1.600 1.3592506 -0.1823192 0.05925063 -1.7823192</span></span>
-<span id="cb2-568"><a href="#cb2-568" tabindex="-1"></a><span class="co">#> CHOICE</span></span>
-<span id="cb2-569"><a href="#cb2-569" tabindex="-1"></a><span class="co">#> 1 1</span></span>
-<span id="cb2-570"><a href="#cb2-570" tabindex="-1"></a><span class="co">#> 2 1</span></span>
-<span id="cb2-571"><a href="#cb2-571" tabindex="-1"></a><span class="co">#> 3 1</span></span>
-<span id="cb2-572"><a href="#cb2-572" tabindex="-1"></a><span class="co">#> 4 2</span></span>
-<span id="cb2-573"><a href="#cb2-573" tabindex="-1"></a><span class="co">#> 5 1</span></span>
-<span id="cb2-574"><a href="#cb2-574" tabindex="-1"></a><span class="co">#> 6 1</span></span>
-<span id="cb2-575"><a href="#cb2-575" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-576"><a href="#cb2-576" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-577"><a href="#cb2-577" tabindex="-1"></a><span class="co">#> This is Run number 1 </span></span>
-<span id="cb2-578"><a href="#cb2-578" tabindex="-1"></a><span class="co">#> does sou_gis exist: FALSE </span></span>
-<span id="cb2-579"><a href="#cb2-579" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-580"><a href="#cb2-580" tabindex="-1"></a><span class="co">#> dataset final_set exists: FALSE </span></span>
-<span id="cb2-581"><a href="#cb2-581" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-582"><a href="#cb2-582" tabindex="-1"></a><span class="co">#> decisiongroups exists: TRUE</span></span>
-<span id="cb2-583"><a href="#cb2-583" tabindex="-1"></a><span class="co">#> 1 2 </span></span>
-<span id="cb2-584"><a href="#cb2-584" tabindex="-1"></a><span class="co">#> 1007 433 </span></span>
-<span id="cb2-585"><a href="#cb2-585" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-586"><a href="#cb2-586" tabindex="-1"></a><span class="co">#> data has been made </span></span>
-<span id="cb2-587"><a href="#cb2-587" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-588"><a href="#cb2-588" tabindex="-1"></a><span class="co">#> First few observations </span></span>
-<span id="cb2-589"><a href="#cb2-589" tabindex="-1"></a><span class="co">#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block group V_1 V_2 e_1 e_2 U_1 U_2</span></span>
-<span id="cb2-590"><a href="#cb2-590" tabindex="-1"></a><span class="co">#> 1 1 9 80 5.0 0 60 20.0 10.0 1 1 -1.150 -1.800 1.1057596 0.8176933 -0.04424039 -0.9823067</span></span>
-<span id="cb2-591"><a href="#cb2-591" tabindex="-1"></a><span class="co">#> 2 1 12 60 2.5 10 40 20.0 0.0 1 1 -0.575 -1.800 1.4664332 0.2855647 0.89143319 -1.5144353</span></span>
-<span id="cb2-592"><a href="#cb2-592" tabindex="-1"></a><span class="co">#> 3 1 13 20 20.0 10 80 2.5 0.0 1 1 -1.400 -0.975 -0.2567456 2.0307365 -1.65674558 1.0557365</span></span>
-<span id="cb2-593"><a href="#cb2-593" tabindex="-1"></a><span class="co">#> 4 1 70 80 5.0 10 20 20.0 2.5 1 1 -0.950 -1.550 1.7936733 0.2273817 0.84367334 -1.3226183</span></span>
-<span id="cb2-594"><a href="#cb2-594" tabindex="-1"></a><span class="co">#> 5 1 71 60 20.0 10 80 10.0 0.0 1 1 -1.800 -1.500 -0.5080847 1.8371868 -2.30808468 0.3371868</span></span>
-<span id="cb2-595"><a href="#cb2-595" tabindex="-1"></a><span class="co">#> 6 1 73 60 10.0 0 40 20.0 10.0 1 1 -1.300 -1.600 0.2315646 0.5250324 -1.06843538 -1.0749676</span></span>
-<span id="cb2-596"><a href="#cb2-596" tabindex="-1"></a><span class="co">#> CHOICE</span></span>
-<span id="cb2-597"><a href="#cb2-597" tabindex="-1"></a><span class="co">#> 1 1</span></span>
-<span id="cb2-598"><a href="#cb2-598" tabindex="-1"></a><span class="co">#> 2 1</span></span>
-<span id="cb2-599"><a href="#cb2-599" tabindex="-1"></a><span class="co">#> 3 2</span></span>
-<span id="cb2-600"><a href="#cb2-600" tabindex="-1"></a><span class="co">#> 4 1</span></span>
-<span id="cb2-601"><a href="#cb2-601" tabindex="-1"></a><span class="co">#> 5 2</span></span>
-<span id="cb2-602"><a href="#cb2-602" tabindex="-1"></a><span class="co">#> 6 1</span></span>
-<span id="cb2-603"><a href="#cb2-603" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-604"><a href="#cb2-604" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-605"><a href="#cb2-605" tabindex="-1"></a><span class="co">#> This is the utility functions </span></span>
-<span id="cb2-606"><a href="#cb2-606" tabindex="-1"></a><span class="co">#> U_1 = @bpreis *$alt1_x1 + @blade *$alt1_x2 + @bwarte *$alt1_x3 ;U_2 = @bpreis *$alt2_x1 + @blade *$alt2_x2 + @bwarte *$alt2_x3 ;Initial function value: -998.1319 </span></span>
-<span id="cb2-607"><a href="#cb2-607" tabindex="-1"></a><span class="co">#> Initial gradient value:</span></span>
-<span id="cb2-608"><a href="#cb2-608" tabindex="-1"></a><span class="co">#> bpreis blade bwarte </span></span>
-<span id="cb2-609"><a href="#cb2-609" tabindex="-1"></a><span class="co">#> -2720.0 -3230.0 1477.5 </span></span>
-<span id="cb2-610"><a href="#cb2-610" tabindex="-1"></a><span class="co">#> initial value 998.131940 </span></span>
-<span id="cb2-611"><a href="#cb2-611" tabindex="-1"></a><span class="co">#> iter 2 value 961.903346</span></span>
-<span id="cb2-612"><a href="#cb2-612" tabindex="-1"></a><span class="co">#> iter 3 value 923.013503</span></span>
-<span id="cb2-613"><a href="#cb2-613" tabindex="-1"></a><span class="co">#> iter 4 value 921.553693</span></span>
-<span id="cb2-614"><a href="#cb2-614" tabindex="-1"></a><span class="co">#> iter 5 value 899.826852</span></span>
-<span id="cb2-615"><a href="#cb2-615" tabindex="-1"></a><span class="co">#> iter 6 value 899.416093</span></span>
-<span id="cb2-616"><a href="#cb2-616" tabindex="-1"></a><span class="co">#> iter 7 value 899.408733</span></span>
-<span id="cb2-617"><a href="#cb2-617" tabindex="-1"></a><span class="co">#> iter 7 value 899.408728</span></span>
-<span id="cb2-618"><a href="#cb2-618" tabindex="-1"></a><span class="co">#> iter 7 value 899.408728</span></span>
-<span id="cb2-619"><a href="#cb2-619" tabindex="-1"></a><span class="co">#> final value 899.408728 </span></span>
-<span id="cb2-620"><a href="#cb2-620" tabindex="-1"></a><span class="co">#> converged</span></span>
-<span id="cb2-621"><a href="#cb2-621" tabindex="-1"></a><span class="co">#> This is Run number 2 </span></span>
-<span id="cb2-622"><a href="#cb2-622" tabindex="-1"></a><span class="co">#> does sou_gis exist: FALSE </span></span>
-<span id="cb2-623"><a href="#cb2-623" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-624"><a href="#cb2-624" tabindex="-1"></a><span class="co">#> dataset final_set exists: FALSE </span></span>
-<span id="cb2-625"><a href="#cb2-625" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-626"><a href="#cb2-626" tabindex="-1"></a><span class="co">#> decisiongroups exists: TRUE</span></span>
-<span id="cb2-627"><a href="#cb2-627" tabindex="-1"></a><span class="co">#> 1 2 </span></span>
-<span id="cb2-628"><a href="#cb2-628" tabindex="-1"></a><span class="co">#> 1007 433 </span></span>
-<span id="cb2-629"><a href="#cb2-629" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-630"><a href="#cb2-630" tabindex="-1"></a><span class="co">#> data has been made </span></span>
-<span id="cb2-631"><a href="#cb2-631" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-632"><a href="#cb2-632" tabindex="-1"></a><span class="co">#> First few observations </span></span>
-<span id="cb2-633"><a href="#cb2-633" tabindex="-1"></a><span class="co">#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block group V_1 V_2 e_1 e_2 U_1 U_2</span></span>
-<span id="cb2-634"><a href="#cb2-634" tabindex="-1"></a><span class="co">#> 1 1 9 80 5.0 0 60 20.0 10.0 1 1 -1.150 -1.800 -0.55606335 0.4418297 -1.7060633 -1.3581703</span></span>
-<span id="cb2-635"><a href="#cb2-635" tabindex="-1"></a><span class="co">#> 2 1 12 60 2.5 10 40 20.0 0.0 1 1 -0.575 -1.800 -0.70525965 0.3030154 -1.2802596 -1.4969846</span></span>
-<span id="cb2-636"><a href="#cb2-636" tabindex="-1"></a><span class="co">#> 3 1 13 20 20.0 10 80 2.5 0.0 1 1 -1.400 -0.975 0.76358526 1.1547805 -0.6364147 0.1797805</span></span>
-<span id="cb2-637"><a href="#cb2-637" tabindex="-1"></a><span class="co">#> 4 1 70 80 5.0 10 20 20.0 2.5 1 1 -0.950 -1.550 -0.50057341 1.6569802 -1.4505734 0.1069802</span></span>
-<span id="cb2-638"><a href="#cb2-638" tabindex="-1"></a><span class="co">#> 5 1 71 60 20.0 10 80 10.0 0.0 1 1 -1.800 -1.500 0.95196390 4.6640005 -0.8480361 3.1640005</span></span>
-<span id="cb2-639"><a href="#cb2-639" tabindex="-1"></a><span class="co">#> 6 1 73 60 10.0 0 40 20.0 10.0 1 1 -1.300 -1.600 -0.07807331 0.9519585 -1.3780733 -0.6480415</span></span>
-<span id="cb2-640"><a href="#cb2-640" tabindex="-1"></a><span class="co">#> CHOICE</span></span>
-<span id="cb2-641"><a href="#cb2-641" tabindex="-1"></a><span class="co">#> 1 2</span></span>
-<span id="cb2-642"><a href="#cb2-642" tabindex="-1"></a><span class="co">#> 2 1</span></span>
-<span id="cb2-643"><a href="#cb2-643" tabindex="-1"></a><span class="co">#> 3 2</span></span>
-<span id="cb2-644"><a href="#cb2-644" tabindex="-1"></a><span class="co">#> 4 2</span></span>
-<span id="cb2-645"><a href="#cb2-645" tabindex="-1"></a><span class="co">#> 5 2</span></span>
-<span id="cb2-646"><a href="#cb2-646" tabindex="-1"></a><span class="co">#> 6 2</span></span>
-<span id="cb2-647"><a href="#cb2-647" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-648"><a href="#cb2-648" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-649"><a href="#cb2-649" tabindex="-1"></a><span class="co">#> This is the utility functions </span></span>
-<span id="cb2-650"><a href="#cb2-650" tabindex="-1"></a><span class="co">#> U_1 = @bpreis *$alt1_x1 + @blade *$alt1_x2 + @bwarte *$alt1_x3 ;U_2 = @bpreis *$alt2_x1 + @blade *$alt2_x2 + @bwarte *$alt2_x3 ;Initial function value: -998.1319 </span></span>
-<span id="cb2-651"><a href="#cb2-651" tabindex="-1"></a><span class="co">#> Initial gradient value:</span></span>
-<span id="cb2-652"><a href="#cb2-652" tabindex="-1"></a><span class="co">#> bpreis blade bwarte </span></span>
-<span id="cb2-653"><a href="#cb2-653" tabindex="-1"></a><span class="co">#> -1480.0 -3742.5 752.5 </span></span>
-<span id="cb2-654"><a href="#cb2-654" tabindex="-1"></a><span class="co">#> initial value 998.131940 </span></span>
-<span id="cb2-655"><a href="#cb2-655" tabindex="-1"></a><span class="co">#> iter 2 value 931.326512</span></span>
-<span id="cb2-656"><a href="#cb2-656" tabindex="-1"></a><span class="co">#> iter 3 value 904.389362</span></span>
-<span id="cb2-657"><a href="#cb2-657" tabindex="-1"></a><span class="co">#> iter 4 value 902.747564</span></span>
-<span id="cb2-658"><a href="#cb2-658" tabindex="-1"></a><span class="co">#> iter 5 value 895.585146</span></span>
-<span id="cb2-659"><a href="#cb2-659" tabindex="-1"></a><span class="co">#> iter 6 value 895.212841</span></span>
-<span id="cb2-660"><a href="#cb2-660" tabindex="-1"></a><span class="co">#> iter 7 value 895.208094</span></span>
-<span id="cb2-661"><a href="#cb2-661" tabindex="-1"></a><span class="co">#> iter 7 value 895.208091</span></span>
-<span id="cb2-662"><a href="#cb2-662" tabindex="-1"></a><span class="co">#> iter 7 value 895.208091</span></span>
-<span id="cb2-663"><a href="#cb2-663" tabindex="-1"></a><span class="co">#> final value 895.208091 </span></span>
-<span id="cb2-664"><a href="#cb2-664" tabindex="-1"></a><span class="co">#> converged</span></span>
-<span id="cb2-665"><a href="#cb2-665" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-666"><a href="#cb2-666" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-667"><a href="#cb2-667" tabindex="-1"></a><span class="co">#> ================ ==== === ===== ==== ===== ===== ===== ====</span></span>
-<span id="cb2-668"><a href="#cb2-668" tabindex="-1"></a><span class="co">#> \ vars n mean sd min max range se</span></span>
-<span id="cb2-669"><a href="#cb2-669" tabindex="-1"></a><span class="co">#> ================ ==== === ===== ==== ===== ===== ===== ====</span></span>
-<span id="cb2-670"><a href="#cb2-670" tabindex="-1"></a><span class="co">#> est_bpreis 1 2 -0.01 0.00 -0.01 -0.01 0.00 0.00</span></span>
-<span id="cb2-671"><a href="#cb2-671" tabindex="-1"></a><span class="co">#> est_blade 2 2 -0.05 0.01 -0.05 -0.04 0.01 0.01</span></span>
-<span id="cb2-672"><a href="#cb2-672" tabindex="-1"></a><span class="co">#> est_bwarte 3 2 0.01 0.03 0.00 0.03 0.04 0.02</span></span>
-<span id="cb2-673"><a href="#cb2-673" tabindex="-1"></a><span class="co">#> rob_pval0_bpreis 4 2 0.00 0.00 0.00 0.00 0.00 0.00</span></span>
-<span id="cb2-674"><a href="#cb2-674" tabindex="-1"></a><span class="co">#> rob_pval0_blade 5 2 0.00 0.00 0.00 0.00 0.00 0.00</span></span>
-<span id="cb2-675"><a href="#cb2-675" tabindex="-1"></a><span class="co">#> rob_pval0_bwarte 6 2 0.28 0.40 0.00 0.57 0.57 0.28</span></span>
-<span id="cb2-676"><a href="#cb2-676" tabindex="-1"></a><span class="co">#> ================ ==== === ===== ==== ===== ===== ===== ====</span></span>
-<span id="cb2-677"><a href="#cb2-677" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-678"><a href="#cb2-678" tabindex="-1"></a><span class="co">#> FALSE TRUE </span></span>
-<span id="cb2-679"><a href="#cb2-679" tabindex="-1"></a><span class="co">#> 50 50 </span></span>
-<span id="cb2-680"><a href="#cb2-680" tabindex="-1"></a><span class="co">#> Utility function used in simulation, ie the true utility: </span></span>
-<span id="cb2-681"><a href="#cb2-681" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-682"><a href="#cb2-682" tabindex="-1"></a><span class="co">#> $u1</span></span>
-<span id="cb2-683"><a href="#cb2-683" tabindex="-1"></a><span class="co">#> $u1$v1</span></span>
-<span id="cb2-684"><a href="#cb2-684" tabindex="-1"></a><span class="co">#> V.1 ~ bpreis * alt1.x1 + blade * alt1.x2 + bwarte * alt1.x3</span></span>
-<span id="cb2-685"><a href="#cb2-685" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-686"><a href="#cb2-686" tabindex="-1"></a><span class="co">#> $u1$v2</span></span>
-<span id="cb2-687"><a href="#cb2-687" tabindex="-1"></a><span class="co">#> V.2 ~ bpreis * alt2.x1 + blade * alt2.x2 + bwarte * alt2.x3</span></span>
-<span id="cb2-688"><a href="#cb2-688" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-689"><a href="#cb2-689" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-690"><a href="#cb2-690" tabindex="-1"></a><span class="co">#> $u2</span></span>
-<span id="cb2-691"><a href="#cb2-691" tabindex="-1"></a><span class="co">#> $u2$v1</span></span>
-<span id="cb2-692"><a href="#cb2-692" tabindex="-1"></a><span class="co">#> V.1 ~ bpreis * alt1.x1</span></span>
-<span id="cb2-693"><a href="#cb2-693" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-694"><a href="#cb2-694" tabindex="-1"></a><span class="co">#> $u2$v2</span></span>
-<span id="cb2-695"><a href="#cb2-695" tabindex="-1"></a><span class="co">#> V.2 ~ bpreis * alt2.x1</span></span>
-<span id="cb2-696"><a href="#cb2-696" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-697"><a href="#cb2-697" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-698"><a href="#cb2-698" tabindex="-1"></a><span class="co">#> Utility function used for Logit estimation with mixl: </span></span>
-<span id="cb2-699"><a href="#cb2-699" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-700"><a href="#cb2-700" tabindex="-1"></a><span class="co">#> [1] "U_1 = @bpreis *$alt1_x1 + @blade *$alt1_x2 + @bwarte *$alt1_x3 ;U_2 = @bpreis *$alt2_x1 + @blade *$alt2_x2 + @bwarte *$alt2_x3 ;"</span></span>
-<span id="cb2-701"><a href="#cb2-701" tabindex="-1"></a><span class="co">#> New names:</span></span>
-<span id="cb2-702"><a href="#cb2-702" tabindex="-1"></a><span class="co">#> • `Choice situation` -> `Choice.situation`</span></span>
-<span id="cb2-703"><a href="#cb2-703" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-704"><a href="#cb2-704" tabindex="-1"></a><span class="co">#> does sou_gis exist: FALSE </span></span>
-<span id="cb2-705"><a href="#cb2-705" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-706"><a href="#cb2-706" tabindex="-1"></a><span class="co">#> dataset final_set exists: FALSE </span></span>
-<span id="cb2-707"><a href="#cb2-707" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-708"><a href="#cb2-708" tabindex="-1"></a><span class="co">#> decisiongroups exists: TRUE</span></span>
-<span id="cb2-709"><a href="#cb2-709" tabindex="-1"></a><span class="co">#> 1 2 </span></span>
-<span id="cb2-710"><a href="#cb2-710" tabindex="-1"></a><span class="co">#> 1007 433 </span></span>
-<span id="cb2-711"><a href="#cb2-711" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-712"><a href="#cb2-712" tabindex="-1"></a><span class="co">#> data has been made </span></span>
-<span id="cb2-713"><a href="#cb2-713" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-714"><a href="#cb2-714" tabindex="-1"></a><span class="co">#> First few observations </span></span>
-<span id="cb2-715"><a href="#cb2-715" tabindex="-1"></a><span class="co">#> ID Choice_situation Block alt1_x1 alt2_x1 alt1_x2 alt2_x2 alt1_x3 alt2_x3 group V_1 V_2 e_1 e_2 U_1 U_2</span></span>
-<span id="cb2-716"><a href="#cb2-716" tabindex="-1"></a><span class="co">#> 1 1 1 1 80 20 2.5 20.0 10 5 1 -0.775 -1.500 0.12234341 0.6503776 -0.6526566 -0.84962241</span></span>
-<span id="cb2-717"><a href="#cb2-717" tabindex="-1"></a><span class="co">#> 2 1 2 1 60 40 5.0 10.0 5 10 1 -0.850 -0.900 -0.43360819 0.6615210 -1.2836082 -0.23847900</span></span>
-<span id="cb2-718"><a href="#cb2-718" tabindex="-1"></a><span class="co">#> 3 1 3 1 60 20 20.0 20.0 0 10 1 -2.000 -1.400 -0.31286639 5.7827787 -2.3128664 4.38277870</span></span>
-<span id="cb2-719"><a href="#cb2-719" tabindex="-1"></a><span class="co">#> 4 1 4 1 20 80 20.0 2.5 0 10 1 -1.600 -0.775 -0.15949911 0.6857678 -1.7594991 -0.08923219</span></span>
-<span id="cb2-720"><a href="#cb2-720" tabindex="-1"></a><span class="co">#> 5 1 5 1 40 80 10.0 5.0 10 5 1 -0.900 -1.050 -0.05237788 1.5859039 -0.9523779 0.53590389</span></span>
-<span id="cb2-721"><a href="#cb2-721" tabindex="-1"></a><span class="co">#> 6 1 6 1 60 80 5.0 2.5 0 0 1 -0.950 -0.975 2.34036634 0.2393918 1.3903663 -0.73560816</span></span>
-<span id="cb2-722"><a href="#cb2-722" tabindex="-1"></a><span class="co">#> CHOICE</span></span>
-<span id="cb2-723"><a href="#cb2-723" tabindex="-1"></a><span class="co">#> 1 1</span></span>
-<span id="cb2-724"><a href="#cb2-724" tabindex="-1"></a><span class="co">#> 2 2</span></span>
-<span id="cb2-725"><a href="#cb2-725" tabindex="-1"></a><span class="co">#> 3 2</span></span>
-<span id="cb2-726"><a href="#cb2-726" tabindex="-1"></a><span class="co">#> 4 2</span></span>
-<span id="cb2-727"><a href="#cb2-727" tabindex="-1"></a><span class="co">#> 5 2</span></span>
-<span id="cb2-728"><a href="#cb2-728" tabindex="-1"></a><span class="co">#> 6 1</span></span>
-<span id="cb2-729"><a href="#cb2-729" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-730"><a href="#cb2-730" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-731"><a href="#cb2-731" tabindex="-1"></a><span class="co">#> This is Run number 1 </span></span>
-<span id="cb2-732"><a href="#cb2-732" tabindex="-1"></a><span class="co">#> does sou_gis exist: FALSE </span></span>
-<span id="cb2-733"><a href="#cb2-733" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-734"><a href="#cb2-734" tabindex="-1"></a><span class="co">#> dataset final_set exists: FALSE </span></span>
-<span id="cb2-735"><a href="#cb2-735" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-736"><a href="#cb2-736" tabindex="-1"></a><span class="co">#> decisiongroups exists: TRUE</span></span>
-<span id="cb2-737"><a href="#cb2-737" tabindex="-1"></a><span class="co">#> 1 2 </span></span>
-<span id="cb2-738"><a href="#cb2-738" tabindex="-1"></a><span class="co">#> 1007 433 </span></span>
-<span id="cb2-739"><a href="#cb2-739" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-740"><a href="#cb2-740" tabindex="-1"></a><span class="co">#> data has been made </span></span>
-<span id="cb2-741"><a href="#cb2-741" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-742"><a href="#cb2-742" tabindex="-1"></a><span class="co">#> First few observations </span></span>
-<span id="cb2-743"><a href="#cb2-743" tabindex="-1"></a><span class="co">#> ID Choice_situation Block alt1_x1 alt2_x1 alt1_x2 alt2_x2 alt1_x3 alt2_x3 group V_1 V_2 e_1 e_2 U_1</span></span>
-<span id="cb2-744"><a href="#cb2-744" tabindex="-1"></a><span class="co">#> 1 1 1 1 80 20 2.5 20.0 10 5 1 -0.775 -1.500 1.74395858 1.6507121 0.968958575</span></span>
-<span id="cb2-745"><a href="#cb2-745" tabindex="-1"></a><span class="co">#> 2 1 2 1 60 40 5.0 10.0 5 10 1 -0.850 -0.900 1.10763894 0.9337414 0.257638936</span></span>
-<span id="cb2-746"><a href="#cb2-746" tabindex="-1"></a><span class="co">#> 3 1 3 1 60 20 20.0 20.0 0 10 1 -2.000 -1.400 -1.23519031 -0.7089281 -3.235190315</span></span>
-<span id="cb2-747"><a href="#cb2-747" tabindex="-1"></a><span class="co">#> 4 1 4 1 20 80 20.0 2.5 0 10 1 -1.600 -0.775 -0.06854059 2.1896932 -1.668540589</span></span>
-<span id="cb2-748"><a href="#cb2-748" tabindex="-1"></a><span class="co">#> 5 1 5 1 40 80 10.0 5.0 10 5 1 -0.900 -1.050 0.90927543 0.3884170 0.009275432</span></span>
-<span id="cb2-749"><a href="#cb2-749" tabindex="-1"></a><span class="co">#> 6 1 6 1 60 80 5.0 2.5 0 0 1 -0.950 -0.975 0.57272851 0.4992305 -0.377271490</span></span>
-<span id="cb2-750"><a href="#cb2-750" tabindex="-1"></a><span class="co">#> U_2 CHOICE</span></span>
-<span id="cb2-751"><a href="#cb2-751" tabindex="-1"></a><span class="co">#> 1 0.15071215 1</span></span>
-<span id="cb2-752"><a href="#cb2-752" tabindex="-1"></a><span class="co">#> 2 0.03374141 1</span></span>
-<span id="cb2-753"><a href="#cb2-753" tabindex="-1"></a><span class="co">#> 3 -2.10892809 2</span></span>
-<span id="cb2-754"><a href="#cb2-754" tabindex="-1"></a><span class="co">#> 4 1.41469320 2</span></span>
-<span id="cb2-755"><a href="#cb2-755" tabindex="-1"></a><span class="co">#> 5 -0.66158301 1</span></span>
-<span id="cb2-756"><a href="#cb2-756" tabindex="-1"></a><span class="co">#> 6 -0.47576952 1</span></span>
-<span id="cb2-757"><a href="#cb2-757" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-758"><a href="#cb2-758" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-759"><a href="#cb2-759" tabindex="-1"></a><span class="co">#> This is the utility functions </span></span>
-<span id="cb2-760"><a href="#cb2-760" tabindex="-1"></a><span class="co">#> U_1 = @bpreis *$alt1_x1 + @blade *$alt1_x2 + @bwarte *$alt1_x3 ;U_2 = @bpreis *$alt2_x1 + @blade *$alt2_x2 + @bwarte *$alt2_x3 ;Initial function value: -998.1319 </span></span>
-<span id="cb2-761"><a href="#cb2-761" tabindex="-1"></a><span class="co">#> Initial gradient value:</span></span>
-<span id="cb2-762"><a href="#cb2-762" tabindex="-1"></a><span class="co">#> bpreis blade bwarte </span></span>
-<span id="cb2-763"><a href="#cb2-763" tabindex="-1"></a><span class="co">#> -880 -830 405 </span></span>
-<span id="cb2-764"><a href="#cb2-764" tabindex="-1"></a><span class="co">#> initial value 998.131940 </span></span>
-<span id="cb2-765"><a href="#cb2-765" tabindex="-1"></a><span class="co">#> iter 2 value 994.862696</span></span>
-<span id="cb2-766"><a href="#cb2-766" tabindex="-1"></a><span class="co">#> iter 3 value 973.696602</span></span>
-<span id="cb2-767"><a href="#cb2-767" tabindex="-1"></a><span class="co">#> iter 4 value 973.644209</span></span>
-<span id="cb2-768"><a href="#cb2-768" tabindex="-1"></a><span class="co">#> iter 5 value 971.118222</span></span>
-<span id="cb2-769"><a href="#cb2-769" tabindex="-1"></a><span class="co">#> iter 6 value 971.113908</span></span>
-<span id="cb2-770"><a href="#cb2-770" tabindex="-1"></a><span class="co">#> iter 6 value 971.113906</span></span>
-<span id="cb2-771"><a href="#cb2-771" tabindex="-1"></a><span class="co">#> iter 6 value 971.113906</span></span>
-<span id="cb2-772"><a href="#cb2-772" tabindex="-1"></a><span class="co">#> final value 971.113906 </span></span>
-<span id="cb2-773"><a href="#cb2-773" tabindex="-1"></a><span class="co">#> converged</span></span>
-<span id="cb2-774"><a href="#cb2-774" tabindex="-1"></a><span class="co">#> This is Run number 2 </span></span>
-<span id="cb2-775"><a href="#cb2-775" tabindex="-1"></a><span class="co">#> does sou_gis exist: FALSE </span></span>
-<span id="cb2-776"><a href="#cb2-776" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-777"><a href="#cb2-777" tabindex="-1"></a><span class="co">#> dataset final_set exists: FALSE </span></span>
-<span id="cb2-778"><a href="#cb2-778" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-779"><a href="#cb2-779" tabindex="-1"></a><span class="co">#> decisiongroups exists: TRUE</span></span>
-<span id="cb2-780"><a href="#cb2-780" tabindex="-1"></a><span class="co">#> 1 2 </span></span>
-<span id="cb2-781"><a href="#cb2-781" tabindex="-1"></a><span class="co">#> 1007 433 </span></span>
-<span id="cb2-782"><a href="#cb2-782" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-783"><a href="#cb2-783" tabindex="-1"></a><span class="co">#> data has been made </span></span>
-<span id="cb2-784"><a href="#cb2-784" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-785"><a href="#cb2-785" tabindex="-1"></a><span class="co">#> First few observations </span></span>
-<span id="cb2-786"><a href="#cb2-786" tabindex="-1"></a><span class="co">#> ID Choice_situation Block alt1_x1 alt2_x1 alt1_x2 alt2_x2 alt1_x3 alt2_x3 group V_1 V_2 e_1 e_2 U_1 U_2</span></span>
-<span id="cb2-787"><a href="#cb2-787" tabindex="-1"></a><span class="co">#> 1 1 1 1 80 20 2.5 20.0 10 5 1 -0.775 -1.500 -1.1552146 1.6918663 -1.930215 0.1918663</span></span>
-<span id="cb2-788"><a href="#cb2-788" tabindex="-1"></a><span class="co">#> 2 1 2 1 60 40 5.0 10.0 5 10 1 -0.850 -0.900 3.5255143 2.5874719 2.675514 1.6874719</span></span>
-<span id="cb2-789"><a href="#cb2-789" tabindex="-1"></a><span class="co">#> 3 1 3 1 60 20 20.0 20.0 0 10 1 -2.000 -1.400 -0.1288440 3.2280170 -2.128844 1.8280170</span></span>
-<span id="cb2-790"><a href="#cb2-790" tabindex="-1"></a><span class="co">#> 4 1 4 1 20 80 20.0 2.5 0 10 1 -1.600 -0.775 -0.3072974 -0.3710956 -1.907297 -1.1460956</span></span>
-<span id="cb2-791"><a href="#cb2-791" tabindex="-1"></a><span class="co">#> 5 1 5 1 40 80 10.0 5.0 10 5 1 -0.900 -1.050 -0.6042191 -0.1952303 -1.504219 -1.2452303</span></span>
-<span id="cb2-792"><a href="#cb2-792" tabindex="-1"></a><span class="co">#> 6 1 6 1 60 80 5.0 2.5 0 0 1 -0.950 -0.975 -0.6233011 -0.2803958 -1.573301 -1.2553958</span></span>
-<span id="cb2-793"><a href="#cb2-793" tabindex="-1"></a><span class="co">#> CHOICE</span></span>
-<span id="cb2-794"><a href="#cb2-794" tabindex="-1"></a><span class="co">#> 1 2</span></span>
-<span id="cb2-795"><a href="#cb2-795" tabindex="-1"></a><span class="co">#> 2 1</span></span>
-<span id="cb2-796"><a href="#cb2-796" tabindex="-1"></a><span class="co">#> 3 2</span></span>
-<span id="cb2-797"><a href="#cb2-797" tabindex="-1"></a><span class="co">#> 4 2</span></span>
-<span id="cb2-798"><a href="#cb2-798" tabindex="-1"></a><span class="co">#> 5 2</span></span>
-<span id="cb2-799"><a href="#cb2-799" tabindex="-1"></a><span class="co">#> 6 2</span></span>
-<span id="cb2-800"><a href="#cb2-800" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-801"><a href="#cb2-801" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-802"><a href="#cb2-802" tabindex="-1"></a><span class="co">#> This is the utility functions </span></span>
-<span id="cb2-803"><a href="#cb2-803" tabindex="-1"></a><span class="co">#> U_1 = @bpreis *$alt1_x1 + @blade *$alt1_x2 + @bwarte *$alt1_x3 ;U_2 = @bpreis *$alt2_x1 + @blade *$alt2_x2 + @bwarte *$alt2_x3 ;Initial function value: -998.1319 </span></span>
-<span id="cb2-804"><a href="#cb2-804" tabindex="-1"></a><span class="co">#> Initial gradient value:</span></span>
-<span id="cb2-805"><a href="#cb2-805" tabindex="-1"></a><span class="co">#> bpreis blade bwarte </span></span>
-<span id="cb2-806"><a href="#cb2-806" tabindex="-1"></a><span class="co">#> -1060.0 -832.5 460.0 </span></span>
-<span id="cb2-807"><a href="#cb2-807" tabindex="-1"></a><span class="co">#> initial value 998.131940 </span></span>
-<span id="cb2-808"><a href="#cb2-808" tabindex="-1"></a><span class="co">#> iter 2 value 994.307556</span></span>
-<span id="cb2-809"><a href="#cb2-809" tabindex="-1"></a><span class="co">#> iter 3 value 972.255010</span></span>
-<span id="cb2-810"><a href="#cb2-810" tabindex="-1"></a><span class="co">#> iter 4 value 972.242499</span></span>
-<span id="cb2-811"><a href="#cb2-811" tabindex="-1"></a><span class="co">#> iter 5 value 968.104609</span></span>
-<span id="cb2-812"><a href="#cb2-812" tabindex="-1"></a><span class="co">#> iter 6 value 968.103010</span></span>
-<span id="cb2-813"><a href="#cb2-813" tabindex="-1"></a><span class="co">#> iter 6 value 968.103008</span></span>
-<span id="cb2-814"><a href="#cb2-814" tabindex="-1"></a><span class="co">#> iter 6 value 968.103008</span></span>
-<span id="cb2-815"><a href="#cb2-815" tabindex="-1"></a><span class="co">#> final value 968.103008 </span></span>
-<span id="cb2-816"><a href="#cb2-816" tabindex="-1"></a><span class="co">#> converged</span></span>
-<span id="cb2-817"><a href="#cb2-817" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-818"><a href="#cb2-818" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-819"><a href="#cb2-819" tabindex="-1"></a><span class="co">#> ================ ==== === ===== ==== ===== ===== ===== ====</span></span>
-<span id="cb2-820"><a href="#cb2-820" tabindex="-1"></a><span class="co">#> \ vars n mean sd min max range se</span></span>
-<span id="cb2-821"><a href="#cb2-821" tabindex="-1"></a><span class="co">#> ================ ==== === ===== ==== ===== ===== ===== ====</span></span>
-<span id="cb2-822"><a href="#cb2-822" tabindex="-1"></a><span class="co">#> est_bpreis 1 2 -0.01 0.00 -0.01 -0.01 0.00 0.00</span></span>
-<span id="cb2-823"><a href="#cb2-823" tabindex="-1"></a><span class="co">#> est_blade 2 2 -0.05 0.00 -0.05 -0.05 0.00 0.00</span></span>
-<span id="cb2-824"><a href="#cb2-824" tabindex="-1"></a><span class="co">#> est_bwarte 3 2 0.02 0.00 0.01 0.02 0.00 0.00</span></span>
-<span id="cb2-825"><a href="#cb2-825" tabindex="-1"></a><span class="co">#> rob_pval0_bpreis 4 2 0.00 0.00 0.00 0.00 0.00 0.00</span></span>
-<span id="cb2-826"><a href="#cb2-826" tabindex="-1"></a><span class="co">#> rob_pval0_blade 5 2 0.00 0.00 0.00 0.00 0.00 0.00</span></span>
-<span id="cb2-827"><a href="#cb2-827" tabindex="-1"></a><span class="co">#> rob_pval0_bwarte 6 2 0.12 0.08 0.06 0.18 0.12 0.06</span></span>
-<span id="cb2-828"><a href="#cb2-828" tabindex="-1"></a><span class="co">#> ================ ==== === ===== ==== ===== ===== ===== ====</span></span>
-<span id="cb2-829"><a href="#cb2-829" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-830"><a href="#cb2-830" tabindex="-1"></a><span class="co">#> FALSE </span></span>
-<span id="cb2-831"><a href="#cb2-831" tabindex="-1"></a><span class="co">#> 100 </span></span>
-<span id="cb2-832"><a href="#cb2-832" tabindex="-1"></a><span class="co">#> 9.804 sec elapsed</span></span>
-<span id="cb2-833"><a href="#cb2-833" tabindex="-1"></a><span class="co">#> $tic</span></span>
-<span id="cb2-834"><a href="#cb2-834" tabindex="-1"></a><span class="co">#> elapsed </span></span>
-<span id="cb2-835"><a href="#cb2-835" tabindex="-1"></a><span class="co">#> 10314.36 </span></span>
-<span id="cb2-836"><a href="#cb2-836" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-837"><a href="#cb2-837" tabindex="-1"></a><span class="co">#> $toc</span></span>
-<span id="cb2-838"><a href="#cb2-838" tabindex="-1"></a><span class="co">#> elapsed </span></span>
-<span id="cb2-839"><a href="#cb2-839" tabindex="-1"></a><span class="co">#> 10324.16 </span></span>
-<span id="cb2-840"><a href="#cb2-840" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-841"><a href="#cb2-841" tabindex="-1"></a><span class="co">#> $msg</span></span>
-<span id="cb2-842"><a href="#cb2-842" tabindex="-1"></a><span class="co">#> logical(0)</span></span>
-<span id="cb2-843"><a href="#cb2-843" tabindex="-1"></a><span class="co">#> </span></span>
-<span id="cb2-844"><a href="#cb2-844" tabindex="-1"></a><span class="co">#> $callback_msg</span></span>
-<span id="cb2-845"><a href="#cb2-845" tabindex="-1"></a><span class="co">#> [1] "9.804 sec elapsed"</span></span></code></pre></div>
-<p><img src="data:image/png;base64,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" width="100%" /><img src="data:image/png;base64,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" width="100%" /><img src="data:image/png;base64,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" width="100%" /></p>
-
-</body>
-</html>
diff --git a/inst/extdata/.DS_Store b/inst/extdata/.DS_Store
index 2b6ec5c895154cd60516076fb2dfa0693bde48b2..b4e832ac874eae33be55db06a94ece91dc2369a2 100644
Binary files a/inst/extdata/.DS_Store and b/inst/extdata/.DS_Store differ
diff --git a/inst/extdata/feedadditives/orth.ngd b/inst/extdata/feedadditives/orth.ngd
deleted file mode 100644
index ca0d0aa4e1fc6a468c96165c77525759eab5e786..0000000000000000000000000000000000000000
--- a/inst/extdata/feedadditives/orth.ngd
+++ /dev/null
@@ -1,91 +0,0 @@
-Design Choice situation alt1.cow alt1.adv alt1.vet alt1.far alt1.met alt1.bon alt2.cow alt2.adv alt2.vet alt2.far alt2.met alt2.bon Block
-1 1 0 0 0 0 0 1 1 0 1 0 3 0 8
-1 2 0 0 0 0 1 6 0 1 1 1 0 5 1
-1 3 0 0 0 0 2 1 0 1 0 0 0 0 4
-1 4 0 0 0 0 3 6 0 0 0 0 2 1 5
-1 5 0 1 0 0 3 3 0 1 1 1 1 2 5
-1 6 0 1 0 0 2 0 0 1 1 1 2 5 3
-1 7 0 1 0 0 1 3 1 0 0 0 2 4 1
-1 8 0 1 0 0 0 0 0 0 0 0 1 6 6
-1 9 1 0 0 0 3 7 0 0 0 1 2 3 2
-1 10 1 0 0 0 2 4 0 0 1 1 3 7 8
-1 11 1 0 0 0 1 7 0 0 0 1 1 0 4
-1 12 1 0 0 0 0 4 0 1 1 0 3 4 4
-1 13 1 1 0 0 0 5 0 0 1 0 2 2 3
-1 14 1 1 0 0 1 2 1 0 1 1 3 6 2
-1 15 1 1 0 0 2 5 1 0 1 1 0 1 5
-1 16 1 1 0 0 3 2 1 0 0 1 2 2 7
-1 17 0 0 1 0 0 2 1 0 1 1 1 6 4
-1 18 0 0 1 0 1 5 0 1 1 0 0 7 1
-1 19 0 0 1 0 2 2 0 1 0 0 3 3 7
-1 20 0 0 1 0 3 5 1 0 0 1 3 5 2
-1 21 0 1 1 0 3 4 1 1 1 0 3 1 5
-1 22 0 1 1 0 2 7 1 0 1 0 0 3 1
-1 23 0 1 1 0 1 4 0 0 0 0 0 1 5
-1 24 0 1 1 0 0 7 0 1 1 1 3 2 8
-1 25 1 0 1 0 3 0 1 0 1 0 2 3 4
-1 26 1 0 1 0 2 3 1 0 0 1 1 5 6
-1 27 1 0 1 0 1 0 1 0 0 0 0 4 4
-1 28 1 0 1 0 0 3 0 1 0 0 1 3 6
-1 29 1 1 1 0 0 6 1 1 0 0 3 2 6
-1 30 1 1 1 0 1 1 0 0 1 0 3 5 7
-1 31 1 1 1 0 2 6 1 0 0 0 1 7 3
-1 32 1 1 1 0 3 1 1 1 1 1 2 0 7
-1 33 0 0 0 1 3 0 1 1 1 1 3 3 8
-1 34 0 0 0 1 2 3 0 0 1 1 1 7 5
-1 35 0 0 0 1 1 0 0 0 1 1 2 4 3
-1 36 0 0 0 1 0 3 1 1 0 1 3 4 2
-1 37 0 1 0 1 0 6 0 0 0 0 3 6 8
-1 38 0 1 0 1 1 1 0 0 1 0 1 5 6
-1 39 0 1 0 1 2 6 1 0 1 1 2 1 2
-1 40 0 1 0 1 3 1 1 1 1 0 0 6 8
-1 41 1 0 0 1 0 2 0 1 0 1 2 6 5
-1 42 1 0 0 1 1 5 1 1 0 1 0 7 7
-1 43 1 0 0 1 2 2 0 1 1 0 2 7 1
-1 44 1 0 0 1 3 5 1 1 1 0 1 1 8
-1 45 1 1 0 1 3 4 0 1 1 0 1 4 3
-1 46 1 1 0 1 2 7 0 0 0 1 3 0 4
-1 47 1 1 0 1 1 4 1 0 1 0 1 0 7
-1 48 1 1 0 1 0 7 0 0 1 0 0 2 3
-1 49 0 0 1 1 3 7 1 1 1 0 2 6 3
-1 50 0 0 1 1 2 4 1 0 0 0 3 7 1
-1 51 0 0 1 1 1 7 0 1 0 1 0 6 7
-1 52 0 0 1 1 0 4 1 1 0 0 1 2 4
-1 53 0 1 1 1 0 5 1 1 0 0 2 5 5
-1 54 0 1 1 1 1 2 0 1 0 1 1 1 2
-1 55 0 1 1 1 2 5 0 0 1 1 0 4 8
-1 56 0 1 1 1 3 2 1 1 0 1 1 4 6
-1 57 1 0 1 1 0 1 1 0 0 1 0 2 3
-1 58 1 0 1 1 1 6 1 1 0 0 0 5 6
-1 59 1 0 1 1 2 1 1 1 1 1 1 3 1
-1 60 1 0 1 1 3 6 0 0 0 1 0 3 6
-1 61 1 1 1 1 3 3 1 1 1 1 0 0 2
-1 62 1 1 1 1 2 0 0 1 0 0 2 0 1
-1 63 1 1 1 1 1 3 0 1 0 1 3 1 2
-1 64 1 1 1 1 0 0 1 1 0 1 2 7 7
-||||||||||
-design
- ;alts = alt1*, alt2*, alt3
- ;orth = seq
- ;rows = 64
- ;block = 8
- ;model:
- U(alt1) = b1[0.2] * COW[0,1]
- + b2[0.2] * ADV[0,1]
- + b3[0.2] * VET[0,1]
- + b4[0.2] * FAR[0,1]
- + b5.dummy[0.1|0.2|0.3] * MET[1,2,3,0]
- + b6.dummy[0.3|0.5|0.65|0.75|0.8|0.83|0.85] * BON[1,2,3,4,5,6,7,0]
- + i1[0] * COW.dummy[0] * VET.dummy[1]
- /
- U(alt2) = b1 * COW
- + b2 * ADV
- + b3 * VET
- + b4 * FAR
- + b5 * MET
- + b6 * BON
- + i1 * COW.dummy[0] * VET.dummy[1]
-/
- U(alt3) = asc3[0.2]
-;
-$
\ No newline at end of file
diff --git a/man/sim_all.Rd b/man/sim_all.Rd
index d0c7086f8156f59640d0c4bebaf6ef68329eef0f..d4cc165e2f87d63d5e0e68c8701c914a81d477b9 100644
--- a/man/sim_all.Rd
+++ b/man/sim_all.Rd
@@ -5,7 +5,15 @@
\title{Is a wrapper for sim_choice executing the simulation over all designs stored in a specific folder
update}
\usage{
-sim_all(nosim = 2, resps, destype = "ngene", designpath, u, bcoeff)
+sim_all(
+ nosim = 2,
+ resps,
+ destype = "ngene",
+ designpath,
+ u,
+ bcoeff,
+ decisiongroups = c(0, 1)
+)
}
\arguments{
\item{nosim}{Number of runs or simulations. For testing use 2 but once you go serious, use at least 200, for better results use 2000.}
@@ -18,7 +26,9 @@ sim_all(nosim = 2, resps, destype = "ngene", designpath, u, bcoeff)
\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"}
-\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{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}
+
+\item{decisiongroups}{A vector showing how decision groups are numerically distributed}
}
\value{
A list, with all information on the simulation. This list an be easily processed by the user and in the rmarkdown template.
@@ -33,7 +43,8 @@ update
resps =240 # number of respondents
nosim=2 # number of simulations to run (about 500 is minimum)
- bcoeff <-list(bsq=0.00, # hypothesized beta coefficients for individual terms of the utility function
+
+ bcoeff <-list(bsq=0.00,
bredkite=-0.05,
bdistance=0.50,
bcost=-0.05,
diff --git a/man/sim_choice.Rd b/man/sim_choice.Rd
index 1b6ecbcf5d37b30e4016e8c1e7e2b0076f86da97..02579d3f35ae374c0b37b7c48c0b421f7ae5bbae 100644
--- a/man/sim_choice.Rd
+++ b/man/sim_choice.Rd
@@ -10,7 +10,8 @@ sim_choice(
respondents = 330,
ut,
destype = destype,
- bcoefficients
+ bcoefficients,
+ decisiongroups = c(0, 1)
)
}
\arguments{
@@ -23,6 +24,10 @@ 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}
}
\value{
a list with all information on the run
diff --git a/man/simulate_choices.Rd b/man/simulate_choices.Rd
index 17e91e638bbd0d839b21fe6db696bb9b4269975f..6163827bb54cf372ee1826918ed1732650cd15f5 100644
--- a/man/simulate_choices.Rd
+++ b/man/simulate_choices.Rd
@@ -4,7 +4,14 @@
\alias{simulate_choices}
\title{Simulate choices based on a dataframe with a design}
\usage{
-simulate_choices(data, utility, setspp, destype, bcoefficients)
+simulate_choices(
+ data,
+ utility,
+ setspp,
+ destype,
+ bcoefficients,
+ decisiongroups = c(0, 1)
+)
}
\arguments{
\item{data}{a dataframe that includes a design repeated for the number of observations}
@@ -14,6 +21,10 @@ simulate_choices(data, utility, setspp, destype, bcoefficients)
\item{setspp}{an integer, the number of choice sets per person}
\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{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}
}
\value{
a dataframe that includes simulated choices and a design
diff --git a/tests/manual-tests/SE-Agri.R b/tests/manual-tests/SE-Agri.R
index c86bfa1304a59714a899d643aec6f3ffecad6225..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 100644
--- a/tests/manual-tests/SE-Agri.R
+++ b/tests/manual-tests/SE-Agri.R
@@ -1,45 +0,0 @@
-rm(list=ls())
-devtools::load_all()
-
-
-library(rlang)
-
-designpath<- system.file("extdata","SE_AGRI", package = "simulateDCE")
-
-destype = 'ngene'
-resps =360 # number of respondents
-nosim=2 # number of simulations to run (about 500 is minimum)
-
-#betacoefficients should not include "-"
-bcoeff <- list(
- basc = 4.2, ## very high asc
- bprof = 0.3,
- bexp = 0.3,
- bdomestic = 0.3,
- bforeign = 0.3,
- bdamage = 0.6,
- bprice = 0.2)
-
-
-
-manipulations = list(alt1.professional= expr(alt1.initiator==1),
- alt2.professional= expr(alt2.initiator==1),
- alt1.expert = expr(alt1.initiator==2),
- alt2.expert = expr(alt2.initiator==2),
- alt1.domestic = expr(alt1.funding==1),
- alt2.domestic = expr(alt2.funding==1),
- alt1.foreign = expr(alt1.funding==2),
- alt2.foreign = expr(alt2.funding==2))
-
-
-#place your utility functions here
-ul<- list(u1=
- list(
- v1 =V.1 ~ bprof*alt1.professional+ bexp * alt1.expert + bdomestic * alt1.domestic + bforeign * alt1.foreign + bdamage*alt1.damage + bprice * alt1.compensation,
- v2 =V.2 ~ bprof*alt2.professional + bexp * alt2.expert + bdomestic * alt2.domestic + bforeign * alt2.foreign + bdamage*alt2.damage + bprice * alt2.compensation,
- v3 =V.3 ~ basc)
-)
-
-seagri <- sim_all(nosim = nosim, resps=resps, destype = destype,
- designpath = designpath, u=ul, bcoeff = bcoeff)
-
diff --git a/tests/manual-tests/SE_Drive.R b/tests/manual-tests/SE_Drive.R
index e5d644deea9838a0f7cd0cb9aa96fa70df0a13cb..06a6c1f88eccdc3d6eb41224b5c88fb92a4ba90b 100644
--- a/tests/manual-tests/SE_Drive.R
+++ b/tests/manual-tests/SE_Drive.R
@@ -51,4 +51,4 @@ ul<-list( u1 =
destype="ngene"
sedrive <- sim_all(nosim = nosim, resps=resps, destype = destype,
- designpath = designpath, u=ul, bcoeff = bcoeff)
+ designpath = designpath, u=ul, bcoeff = bcoeff, decisiongroups = decisiongroups)
diff --git a/tests/manual-tests/csa.RDS b/tests/manual-tests/csa.RDS
index 2a4e75affafc7970136eff5c2c3914e3766ed77f..6702f9c0bb182b736079b134c311beb042947848 100644
Binary files a/tests/manual-tests/csa.RDS and b/tests/manual-tests/csa.RDS differ
diff --git a/tests/manual-tests/feedadditives.R b/tests/manual-tests/feedadditives.R
index 3ea5e06bf56dfc28379bebe0bde2fe5816ac8290..38854bc5abe4b738fbc58d5fc34c9024b79b7904 100644
--- a/tests/manual-tests/feedadditives.R
+++ b/tests/manual-tests/feedadditives.R
@@ -45,5 +45,5 @@ ul<- list(u1= list(
v3 =V.3 ~ basc)
)
-sedrive <- sim_all(nosim = nosim, resps=resps, destype = destype,
- designpath = designpath, u=ul, bcoeff = bcoeff)
+feedadditives <- sim_all(nosim = nosim, resps=resps, destype = destype,
+ designpath = designpath, u=ul, bcoeff = bcoeff, decisiongroups = decisiongroups)
diff --git a/tests/testthat/Rplots.pdf b/tests/testthat/Rplots.pdf
index 9b70e176fb2a5eae21b9608d833b66ae35a70572..8f53f6855765053227695cbbd3a1b7bc48d017cc 100644
Binary files a/tests/testthat/Rplots.pdf and b/tests/testthat/Rplots.pdf differ
diff --git a/tests/testthat/test-sim_all.R b/tests/testthat/test-sim_all.R
index 2df09e8a54318089c2382219306a12f144e6aa61..87acddbf84daeff0cf0875c4d760b0d9723a6935 100644
--- a/tests/testthat/test-sim_all.R
+++ b/tests/testthat/test-sim_all.R
@@ -1,4 +1,6 @@
+library(rlang)
+library(formula.tools)
designpath<- system.file("extdata","Rbook" ,package = "simulateDCE")
@@ -195,8 +197,7 @@ test_that("Simulation results are reasonable", {
sprintf("Variable est_%s does not exist in coeffNestedOutput", variable))
# Check if variable is numeric
- expect_type(coeffNestedOutput[[paste0("est_", variable)]], "numeric",
- sprintf("Variable est_%s in coeffNestedOutput is not numeric", variable))
+ ## expect_is(coeffNestedOutput[[paste0("est_", variable)]], "numeric", sprintf("Variable est_%s in coeffNestedOutput is not numeric", variable))
# Check if each entry in the variable column is numeric
expect_true(all(sapply(coeffNestedOutput[[paste0("est_", variable)]], is.numeric)),
diff --git a/vignettes/SE_Agri-vignette.Rmd b/vignettes/SE_Agri-vignette.Rmd
deleted file mode 100644
index a1bdb9b71c8aedc3301c1f9d84d9890f8dc1f2de..0000000000000000000000000000000000000000
--- a/vignettes/SE_Agri-vignette.Rmd
+++ /dev/null
@@ -1,148 +0,0 @@
----
-title: "SE_Agri-vignette"
-output: rmarkdown::html_vignette
-vignette: >
- %\VignetteIndexEntry{SE_Agri-vignette}
- %\VignetteEngine{knitr::rmarkdown}
- %\VignetteEncoding{UTF-8}
----
-
-<!-- There is an issue when creating these vignettes using usethis::use_vignette and
-devtools::build_vignettes() where the compilied vignette HTML files are placed in /doc
-rather than inst/doc
-
-Best Practice is to follow these steps
-1. Create vignette using usethis::use_vignette("my-vignette")
-2. After making changes, then run devtools::build_vignettes()
-3. Rebuild using devtools::install(build_vignettes = TRUE)
-4. Check that it is in the vignette environment using browseVigettes()
-
-If vignette does not appear in gitHub, it is possibly due to a file heirarchy problem where rendered files
-appear in /doc instead of /inst/doc
-
-To avoid this run:
-tools::buildVignettes(dir = ".", tangle=TRUE)
-dir.create("inst/doc")
-file.copy(dir("vignettes", full.names=TRUE), "inst/doc", overwrite=TRUE)
-
-More info here: https://community.rstudio.com/t/browsevignettes-mypackage-saying-no-vignettes-found/68656/7
--->
-
-```{r, include = FALSE}
-knitr::opts_chunk$set(
- collapse = TRUE,
- comment = "#>"
-)
-```
-
-# Introduction
-
-This vignette demonstrates how to use simulate situations in which different utility functions apply to diffferent subsets of respondents. We will use a sample dataset and utility functions to generate simulated data and analyze the results. First off it is good practice remove all objects (variables, functions, etc.) from the current workspace and load all R files in the package directory into the current R session.
-
-```{r setup}
-library(simulateDCE)
-
-rm(list=ls())
-devtools::load_all()
-
-
-library(rlang)
-```
-
-# Inititalize Variables
-
-sim_all is the highest level function in the package and will run simulations for all designs contained in the specified design folder. Accordingly, this is generally the function the user will want to call. To prepare for using this function, a hypothesized utility function with corresponding beta coefficients representing the weight of each term must be declared in R.
-
-The manipulation variable allows the user to assign different terms based on the values of columns in the experimental design file
-
-```{r initialize}
-bcoeff <- list(
- basc = 4.2, ## very high asc
- bprof = 0.3,
- bexp = 0.3,
- bdomestic = 0.3,
- bforeign = 0.3,
- bdamage = 0.6,
- bprice = 0.2)
-
-
-
-manipulations = list(alt1.professional= expr(alt1.initiator==1),
- alt2.professional= expr(alt2.initiator==1),
- alt1.expert = expr(alt1.initiator==2),
- alt2.expert = expr(alt2.initiator==2),
- alt1.domestic = expr(alt1.funding==1),
- alt2.domestic = expr(alt2.funding==1),
- alt1.foreign = expr(alt1.funding==2),
- alt2.foreign = expr(alt2.funding==2))
-
-
-#place your utility functions here
-ul<- list(u1=
- list(
- v1 =V.1 ~ bprof*alt1.professional+ bexp * alt1.expert + bdomestic * alt1.domestic + bforeign * alt1.foreign + bdamage*alt1.damage + bprice * alt1.compensation,
- v2 =V.2 ~ bprof*alt2.professional + bexp * alt2.expert + bdomestic * alt2.domestic + bforeign * alt2.foreign + bdamage*alt2.damage + bprice * alt2.compensation,
- v3 =V.3 ~ basc)
-)
-```
-# Other parameters
-
-Besides these arguments the user must also specify the number of respondents in the simulated survey and the number of times to run the model. The number of respondents (resps) should be selected based on experimental design parameters, while the number of simulations (nosim) should be large enough to glean statistically significant data. It is best to use a small number for this while learning to use the package and a large number (at least 500) once the other parameters have been settled.
-
-The simulation can be ran using spdesign or NGENE design files which will be contained in the design path. The design path and design type, must also be specified as strings:
-
-```{r other}
-
-designpath<- system.file("extdata","se_AGRI" ,package = "simulateDCE")
-## can also be specified using relative path eg. designpath<- "Projects/CSA/Designs/"
-
-#notes <- "This design consists of different heuristics. One group did not attend the methan attribute, another group only decided based on the payment"
-
-notes <- "No Heuristics"
-
-resps =240 # number of respondents
-nosim=2 # number of simulations to run (about 500 is minimum)
-
-## design type must be either 'spdesign' or 'ngene'
-destype <- "spdesign"
-```
-# Randomness
-
-As several part of the simulation rely on random values within experimentally defined bounds, the output of a given simulation call using sim_all will vary each time it is called. Unless the seed for R's randome number generator is set like so:
-
-```{r random}
-
-set.seed(3393)
-
-```
-
-# Output
-
-The sim_all function returns a multidimensional R list containing graphs, simulated observations and a dataframe containing sumaries of estimated b coefficients. In general these will be printed to the console, but the entire results can also be assigned to an r list object.
-
-```{r output}
-csa <- simulateDCE::sim_all(nosim = nosim, resps=resps, destype = destype,
- designpath = designpath, u= ul, bcoeff = bcoeff)
-
-
-
-```
-
-# Accessing the Output in R
-
-The beta cofficients for each simulation are contained in a dataframe called coeffs located within {result}->olddesign->coefs. and a summary table, which displays
-statistics of these b coefficients across all simulations is contained in ->olddesign->summary.
-
-You can also save the results to disk using saveRDS(csa,file = "tests/manual-tests/csa.RDS")
-
-
-```{r accessOutput}
-## nested results are hard coded, if the design changes this must aswell
-simulationCoeff <- csa$olddesign$coefs
-coeffSummary <- csa$olddesign$summary
-
-print(simulationCoeff)
-print(coeffSummary)
-
-## saveRDS(csa,file = "tests/manual-tests/csa.RDS")
-```
diff --git a/vignettes/SE_drive-vignette.Rmd b/vignettes/SE_drive-vignette.Rmd
index 2b4d4ef6ece09ac2665ad305860fd6f242f8c781..952c92933f6a2fa8be0974a58ae4b48c364ed820 100644
--- a/vignettes/SE_drive-vignette.Rmd
+++ b/vignettes/SE_drive-vignette.Rmd
@@ -40,12 +40,8 @@ This vignette demonstrates how to use the `simulateDCE` package to simulate disc
```{r setup}
library(simulateDCE)
-
-rm(list=ls())
-devtools::load_all()
-
-
library(rlang)
+library(formula.tools)
```
# Inititalize Variables
@@ -108,7 +104,7 @@ The sim_all function returns a multidimensional R list containing graphs, simula
```{r output}
sedrive <- sim_all(nosim = nosim, resps=resps, destype = destype,
- designpath = designpath, u=ul, bcoeff = bcoeff)
+ designpath = designpath, u=ul, bcoeff = bcoeff, decisiongroups = decisiongroups)
diff --git a/vignettes/csa-vignette.Rmd b/vignettes/csa-vignette.Rmd
index 4162a3123061d12790fe7bfb4d741435b947b61f..6550c3e3ac9dbc52ec073b966898e887c64bf4a6 100644
--- a/vignettes/csa-vignette.Rmd
+++ b/vignettes/csa-vignette.Rmd
@@ -15,7 +15,7 @@ Best Practice is to follow these steps
1. Create vignette using usethis::use_vignette("my-vignette")
2. After making changes, then run devtools::build_vignettes()
3. Rebuild using devtools::install(build_vignettes = TRUE)
-4. Check that it is in the vignette environment using browseVigettes()
+4. Check that it is in the vignette environment using browseVignettes()
If vignette does not appear in gitHub, it is possibly due to a file heirarchy problem where rendered files
appear in /doc instead of /inst/doc
@@ -40,12 +40,8 @@ This vignette demonstrates how to use the `simulateDCE` package to simulate disc
```{r setup}
library(simulateDCE)
-
-rm(list=ls())
-devtools::load_all()
-
-
library(rlang)
+library(formula.tools)
```
# Inititalize Variables