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+
+<!-- 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" aria-hidden="true" tabindex="-1"></a><span class="co"># FILL THIS IN! HOW CAN PEOPLE INSTALL YOUR DEV PACKAGE?</span></span>
+<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="fu">install.packages</span>(<span class="st">"remotes"</span>)</span>
+<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" 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 which shows you how to solve a common
+problem:</p>
+<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a> <span class="fu">library</span>(simulateDCE)</span>
+<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(rlang)</span>
+<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a><span class="fu">print</span>(<span class="st">"lests"</span>)</span>
+<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a><span class="co">#> [1] "lests"</span></span>
+<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a><span class="co">#set.seed(22233)</span></span>
+<span id="cb2-8"><a href="#cb2-8" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-9"><a href="#cb2-9" aria-hidden="true" tabindex="-1"></a><span class="co"># Designpath indicates the folder where all designs that should be simulated are stored. Can be either ngd files (for NGENE) or Robjects for spdesign)</span></span>
+<span id="cb2-10"><a href="#cb2-10" aria-hidden="true" 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="cb2-11"><a href="#cb2-11" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-12"><a href="#cb2-12" aria-hidden="true" tabindex="-1"></a><span class="co"># on your computer, it would be something like</span></span>
+<span id="cb2-13"><a href="#cb2-13" aria-hidden="true" tabindex="-1"></a><span class="co"># designpath <- "c:/myfancyDCE/Designs"</span></span>
+<span id="cb2-14"><a href="#cb2-14" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-15"><a href="#cb2-15" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-16"><a href="#cb2-16" aria-hidden="true" tabindex="-1"></a><span class="co"># number of respondents</span></span>
+<span id="cb2-17"><a href="#cb2-17" aria-hidden="true" tabindex="-1"></a>resps <span class="ot">=</span><span class="dv">120</span></span>
+<span id="cb2-18"><a href="#cb2-18" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-19"><a href="#cb2-19" aria-hidden="true" tabindex="-1"></a><span class="co"># number of simulations to run (about 200 is minimum if you want to be serious -- but it takes some time. always test your code with 2 simulations, and if it runs smoothly, go for more.)</span></span>
+<span id="cb2-20"><a href="#cb2-20" aria-hidden="true" tabindex="-1"></a>nosim<span class="ot">=</span> <span class="dv">2</span> </span>
+<span id="cb2-21"><a href="#cb2-21" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-22"><a href="#cb2-22" aria-hidden="true" tabindex="-1"></a><span class="co"># If you want to use different groups of respondents, use this. The following line means that you have one group of 70% size and one group of 30% size</span></span>
+<span id="cb2-23"><a href="#cb2-23" aria-hidden="true" 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="cb2-24"><a href="#cb2-24" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-25"><a href="#cb2-25" aria-hidden="true" tabindex="-1"></a><span class="co"># set the values of the parameters you want to use in the simulation</span></span>
+<span id="cb2-26"><a href="#cb2-26" aria-hidden="true" tabindex="-1"></a>bpreis <span class="ot">=</span> <span class="sc">-</span><span class="fl">0.01</span></span>
+<span id="cb2-27"><a href="#cb2-27" aria-hidden="true" tabindex="-1"></a>blade <span class="ot">=</span> <span class="sc">-</span><span class="fl">0.07</span></span>
+<span id="cb2-28"><a href="#cb2-28" aria-hidden="true" tabindex="-1"></a>bwarte <span class="ot">=</span> <span class="fl">0.02</span></span>
+<span id="cb2-29"><a href="#cb2-29" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-30"><a href="#cb2-30" aria-hidden="true" tabindex="-1"></a><span class="co"># If you want to do some manipulations in the design before you simulate, add a list called manipulations. Here, we devide some attributes by 10</span></span>
+<span id="cb2-31"><a href="#cb2-31" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-32"><a href="#cb2-32" aria-hidden="true" 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="cb2-33"><a href="#cb2-33" aria-hidden="true" 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="cb2-34"><a href="#cb2-34" aria-hidden="true" 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="cb2-35"><a href="#cb2-35" aria-hidden="true" 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="cb2-36"><a href="#cb2-36" aria-hidden="true" tabindex="-1"></a>)</span>
+<span id="cb2-37"><a href="#cb2-37" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-38"><a href="#cb2-38" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-39"><a href="#cb2-39" aria-hidden="true" tabindex="-1"></a><span class="co">#place your utility functions here. We have two utility functions and two sets of utility functions. This is because we assume that 70% act according to the utility u1 and 30% act to the utility u2 (that is, they only decide according to the price and ignore the other attributes)</span></span>
+<span id="cb2-40"><a href="#cb2-40" aria-hidden="true" tabindex="-1"></a>u<span class="ot"><-</span><span class="fu">list</span>( <span class="at">u1 =</span></span>
+<span id="cb2-41"><a href="#cb2-41" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-42"><a href="#cb2-42" aria-hidden="true" tabindex="-1"></a> <span class="fu">list</span>(</span>
+<span id="cb2-43"><a href="#cb2-43" aria-hidden="true" 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="cb2-44"><a href="#cb2-44" aria-hidden="true" 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="cb2-45"><a href="#cb2-45" aria-hidden="true" tabindex="-1"></a> )</span>
+<span id="cb2-46"><a href="#cb2-46" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-47"><a href="#cb2-47" aria-hidden="true" tabindex="-1"></a> ,</span>
+<span id="cb2-48"><a href="#cb2-48" aria-hidden="true" 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="cb2-49"><a href="#cb2-49" aria-hidden="true" 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="cb2-50"><a href="#cb2-50" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-51"><a href="#cb2-51" aria-hidden="true" tabindex="-1"></a>)</span>
+<span id="cb2-52"><a href="#cb2-52" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-53"><a href="#cb2-53" aria-hidden="true" tabindex="-1"></a><span class="co"># specify the designtype "ngene" or "spdesign"</span></span>
+<span id="cb2-54"><a href="#cb2-54" aria-hidden="true" tabindex="-1"></a>destype<span class="ot">=</span><span class="st">"ngene"</span></span>
+<span id="cb2-55"><a href="#cb2-55" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-56"><a href="#cb2-56" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-57"><a href="#cb2-57" aria-hidden="true" tabindex="-1"></a><span class="co">#lets go</span></span>
+<span id="cb2-58"><a href="#cb2-58" aria-hidden="true" tabindex="-1"></a>sedrive <span class="ot"><-</span> simulateDCE<span class="sc">::</span><span class="fu">sim_all</span>()</span>
+<span id="cb2-59"><a href="#cb2-59" aria-hidden="true" tabindex="-1"></a><span class="co">#> Utility function used in simulation, ie the true utility: </span></span>
+<span id="cb2-60"><a href="#cb2-60" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-61"><a href="#cb2-61" aria-hidden="true" tabindex="-1"></a><span class="co">#> $u1</span></span>
+<span id="cb2-62"><a href="#cb2-62" aria-hidden="true" tabindex="-1"></a><span class="co">#> $u1$v1</span></span>
+<span id="cb2-63"><a href="#cb2-63" aria-hidden="true" tabindex="-1"></a><span class="co">#> V.1 ~ bpreis * alt1.x1 + blade * alt1.x2 + bwarte * alt1.x3</span></span>
+<span id="cb2-64"><a href="#cb2-64" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-65"><a href="#cb2-65" aria-hidden="true" tabindex="-1"></a><span class="co">#> $u1$v2</span></span>
+<span id="cb2-66"><a href="#cb2-66" aria-hidden="true" tabindex="-1"></a><span class="co">#> V.2 ~ bpreis * alt2.x1 + blade * alt2.x2 + bwarte * alt2.x3</span></span>
+<span id="cb2-67"><a href="#cb2-67" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-68"><a href="#cb2-68" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-69"><a href="#cb2-69" aria-hidden="true" tabindex="-1"></a><span class="co">#> $u2</span></span>
+<span id="cb2-70"><a href="#cb2-70" aria-hidden="true" tabindex="-1"></a><span class="co">#> $u2$v1</span></span>
+<span id="cb2-71"><a href="#cb2-71" aria-hidden="true" tabindex="-1"></a><span class="co">#> V.1 ~ bpreis * alt1.x1</span></span>
+<span id="cb2-72"><a href="#cb2-72" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-73"><a href="#cb2-73" aria-hidden="true" tabindex="-1"></a><span class="co">#> $u2$v2</span></span>
+<span id="cb2-74"><a href="#cb2-74" aria-hidden="true" tabindex="-1"></a><span class="co">#> V.2 ~ bpreis * alt2.x1</span></span>
+<span id="cb2-75"><a href="#cb2-75" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-76"><a href="#cb2-76" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-77"><a href="#cb2-77" aria-hidden="true" tabindex="-1"></a><span class="co">#> Utility function used for Logit estimation with mixl: </span></span>
+<span id="cb2-78"><a href="#cb2-78" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-79"><a href="#cb2-79" aria-hidden="true" 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="cb2-80"><a href="#cb2-80" aria-hidden="true" tabindex="-1"></a><span class="co">#> New names:</span></span>
+<span id="cb2-81"><a href="#cb2-81" aria-hidden="true" tabindex="-1"></a><span class="co">#> • `Choice situation` -></span></span>
+<span id="cb2-82"><a href="#cb2-82" aria-hidden="true" tabindex="-1"></a><span class="co">#> `Choice.situation`</span></span>
+<span id="cb2-83"><a href="#cb2-83" aria-hidden="true" tabindex="-1"></a><span class="co">#> • `` -> `...10`</span></span>
+<span id="cb2-84"><a href="#cb2-84" aria-hidden="true" tabindex="-1"></a><span class="co">#> Warning: One or more parsing issues, call</span></span>
+<span id="cb2-85"><a href="#cb2-85" aria-hidden="true" tabindex="-1"></a><span class="co">#> `problems()` on your data frame for</span></span>
+<span id="cb2-86"><a href="#cb2-86" aria-hidden="true" tabindex="-1"></a><span class="co">#> details, e.g.:</span></span>
+<span id="cb2-87"><a href="#cb2-87" aria-hidden="true" tabindex="-1"></a><span class="co">#> dat <- vroom(...)</span></span>
+<span id="cb2-88"><a href="#cb2-88" aria-hidden="true" tabindex="-1"></a><span class="co">#> problems(dat)</span></span>
+<span id="cb2-89"><a href="#cb2-89" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-90"><a href="#cb2-90" aria-hidden="true" tabindex="-1"></a><span class="co">#> does sou_gis exist: FALSE </span></span>
+<span id="cb2-91"><a href="#cb2-91" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-92"><a href="#cb2-92" aria-hidden="true" tabindex="-1"></a><span class="co">#> dataset final_set exists: FALSE </span></span>
+<span id="cb2-93"><a href="#cb2-93" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-94"><a href="#cb2-94" aria-hidden="true" tabindex="-1"></a><span class="co">#> decisiongroups exists: TRUE</span></span>
+<span id="cb2-95"><a href="#cb2-95" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 2 </span></span>
+<span id="cb2-96"><a href="#cb2-96" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1007 433 </span></span>
+<span id="cb2-97"><a href="#cb2-97" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-98"><a href="#cb2-98" aria-hidden="true" tabindex="-1"></a><span class="co">#> data has been made </span></span>
+<span id="cb2-99"><a href="#cb2-99" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-100"><a href="#cb2-100" aria-hidden="true" tabindex="-1"></a><span class="co">#> First few observations </span></span>
+<span id="cb2-101"><a href="#cb2-101" aria-hidden="true" tabindex="-1"></a><span class="co">#> ID Choice_situation alt1_x1 alt1_x2</span></span>
+<span id="cb2-102"><a href="#cb2-102" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1 7 80 2.5</span></span>
+<span id="cb2-103"><a href="#cb2-103" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1 19 20 2.5</span></span>
+<span id="cb2-104"><a href="#cb2-104" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1 30 20 10.0</span></span>
+<span id="cb2-105"><a href="#cb2-105" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 1 32 40 20.0</span></span>
+<span id="cb2-106"><a href="#cb2-106" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1 39 40 20.0</span></span>
+<span id="cb2-107"><a href="#cb2-107" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1 48 60 5.0</span></span>
+<span id="cb2-108"><a href="#cb2-108" aria-hidden="true" tabindex="-1"></a><span class="co">#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block</span></span>
+<span id="cb2-109"><a href="#cb2-109" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 10.0 60 20.0 10 1</span></span>
+<span id="cb2-110"><a href="#cb2-110" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 5.0 60 2.5 0 1</span></span>
+<span id="cb2-111"><a href="#cb2-111" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 5.0 80 5.0 10 1</span></span>
+<span id="cb2-112"><a href="#cb2-112" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 2.5 80 2.5 0 1</span></span>
+<span id="cb2-113"><a href="#cb2-113" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 0.0 80 10.0 10 1</span></span>
+<span id="cb2-114"><a href="#cb2-114" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 2.5 20 5.0 10 1</span></span>
+<span id="cb2-115"><a href="#cb2-115" aria-hidden="true" tabindex="-1"></a><span class="co">#> group V_1 V_2 e_1</span></span>
+<span id="cb2-116"><a href="#cb2-116" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1 -0.775 -1.800 2.8927045</span></span>
+<span id="cb2-117"><a href="#cb2-117" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1 -0.275 -0.775 2.1129458</span></span>
+<span id="cb2-118"><a href="#cb2-118" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1 -0.800 -0.950 -0.3070059</span></span>
+<span id="cb2-119"><a href="#cb2-119" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 1 -1.750 -0.975 0.2125815</span></span>
+<span id="cb2-120"><a href="#cb2-120" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1 -1.800 -1.300 0.5101632</span></span>
+<span id="cb2-121"><a href="#cb2-121" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1 -0.900 -0.350 -0.9494807</span></span>
+<span id="cb2-122"><a href="#cb2-122" aria-hidden="true" tabindex="-1"></a><span class="co">#> e_2 U_1 U_2 CHOICE</span></span>
+<span id="cb2-123"><a href="#cb2-123" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 0.09958433 2.117705 -1.700416 1</span></span>
+<span id="cb2-124"><a href="#cb2-124" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 3.47451776 1.837946 2.699518 2</span></span>
+<span id="cb2-125"><a href="#cb2-125" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 -0.28860974 -1.107006 -1.238610 1</span></span>
+<span id="cb2-126"><a href="#cb2-126" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 3.65240491 -1.537418 2.677405 2</span></span>
+<span id="cb2-127"><a href="#cb2-127" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 -0.14448942 -1.289837 -1.444489 1</span></span>
+<span id="cb2-128"><a href="#cb2-128" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 -1.04296995 -1.849481 -1.392970 2</span></span>
+<span id="cb2-129"><a href="#cb2-129" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-130"><a href="#cb2-130" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-131"><a href="#cb2-131" aria-hidden="true" tabindex="-1"></a><span class="co">#> This is Run number 1 </span></span>
+<span id="cb2-132"><a href="#cb2-132" aria-hidden="true" tabindex="-1"></a><span class="co">#> does sou_gis exist: FALSE </span></span>
+<span id="cb2-133"><a href="#cb2-133" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-134"><a href="#cb2-134" aria-hidden="true" tabindex="-1"></a><span class="co">#> dataset final_set exists: FALSE </span></span>
+<span id="cb2-135"><a href="#cb2-135" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-136"><a href="#cb2-136" aria-hidden="true" tabindex="-1"></a><span class="co">#> decisiongroups exists: TRUE</span></span>
+<span id="cb2-137"><a href="#cb2-137" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 2 </span></span>
+<span id="cb2-138"><a href="#cb2-138" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1007 433 </span></span>
+<span id="cb2-139"><a href="#cb2-139" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-140"><a href="#cb2-140" aria-hidden="true" tabindex="-1"></a><span class="co">#> data has been made </span></span>
+<span id="cb2-141"><a href="#cb2-141" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-142"><a href="#cb2-142" aria-hidden="true" tabindex="-1"></a><span class="co">#> First few observations </span></span>
+<span id="cb2-143"><a href="#cb2-143" aria-hidden="true" tabindex="-1"></a><span class="co">#> ID Choice_situation alt1_x1 alt1_x2</span></span>
+<span id="cb2-144"><a href="#cb2-144" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1 7 80 2.5</span></span>
+<span id="cb2-145"><a href="#cb2-145" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1 19 20 2.5</span></span>
+<span id="cb2-146"><a href="#cb2-146" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1 30 20 10.0</span></span>
+<span id="cb2-147"><a href="#cb2-147" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 1 32 40 20.0</span></span>
+<span id="cb2-148"><a href="#cb2-148" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1 39 40 20.0</span></span>
+<span id="cb2-149"><a href="#cb2-149" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1 48 60 5.0</span></span>
+<span id="cb2-150"><a href="#cb2-150" aria-hidden="true" tabindex="-1"></a><span class="co">#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block</span></span>
+<span id="cb2-151"><a href="#cb2-151" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 10.0 60 20.0 10 1</span></span>
+<span id="cb2-152"><a href="#cb2-152" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 5.0 60 2.5 0 1</span></span>
+<span id="cb2-153"><a href="#cb2-153" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 5.0 80 5.0 10 1</span></span>
+<span id="cb2-154"><a href="#cb2-154" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 2.5 80 2.5 0 1</span></span>
+<span id="cb2-155"><a href="#cb2-155" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 0.0 80 10.0 10 1</span></span>
+<span id="cb2-156"><a href="#cb2-156" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 2.5 20 5.0 10 1</span></span>
+<span id="cb2-157"><a href="#cb2-157" aria-hidden="true" tabindex="-1"></a><span class="co">#> group V_1 V_2 e_1</span></span>
+<span id="cb2-158"><a href="#cb2-158" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1 -0.775 -1.800 -0.06362638</span></span>
+<span id="cb2-159"><a href="#cb2-159" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1 -0.275 -0.775 -0.81571577</span></span>
+<span id="cb2-160"><a href="#cb2-160" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1 -0.800 -0.950 -1.09388352</span></span>
+<span id="cb2-161"><a href="#cb2-161" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 1 -1.750 -0.975 0.28996875</span></span>
+<span id="cb2-162"><a href="#cb2-162" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1 -1.800 -1.300 1.03059224</span></span>
+<span id="cb2-163"><a href="#cb2-163" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1 -0.900 -0.350 -1.10504379</span></span>
+<span id="cb2-164"><a href="#cb2-164" aria-hidden="true" tabindex="-1"></a><span class="co">#> e_2 U_1 U_2 CHOICE</span></span>
+<span id="cb2-165"><a href="#cb2-165" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 0.1958595 -0.8386264 -1.6041405 1</span></span>
+<span id="cb2-166"><a href="#cb2-166" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 0.1028995 -1.0907158 -0.6721005 2</span></span>
+<span id="cb2-167"><a href="#cb2-167" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 0.7165451 -1.8938835 -0.2334549 2</span></span>
+<span id="cb2-168"><a href="#cb2-168" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 1.4748351 -1.4600313 0.4998351 2</span></span>
+<span id="cb2-169"><a href="#cb2-169" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 4.5718398 -0.7694078 3.2718398 2</span></span>
+<span id="cb2-170"><a href="#cb2-170" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 0.8766732 -2.0050438 0.5266732 2</span></span>
+<span id="cb2-171"><a href="#cb2-171" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-172"><a href="#cb2-172" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-173"><a href="#cb2-173" aria-hidden="true" tabindex="-1"></a><span class="co">#> This is the utility functions </span></span>
+<span id="cb2-174"><a href="#cb2-174" aria-hidden="true" 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="cb2-175"><a href="#cb2-175" aria-hidden="true" tabindex="-1"></a><span class="co">#> Initial gradient value:</span></span>
+<span id="cb2-176"><a href="#cb2-176" aria-hidden="true" tabindex="-1"></a><span class="co">#> bpreis blade bwarte </span></span>
+<span id="cb2-177"><a href="#cb2-177" aria-hidden="true" tabindex="-1"></a><span class="co">#> -860.0 -1147.5 532.5 </span></span>
+<span id="cb2-178"><a href="#cb2-178" aria-hidden="true" tabindex="-1"></a><span class="co">#> initial value 998.131940 </span></span>
+<span id="cb2-179"><a href="#cb2-179" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 2 value 988.178813</span></span>
+<span id="cb2-180"><a href="#cb2-180" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 3 value 959.683236</span></span>
+<span id="cb2-181"><a href="#cb2-181" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 4 value 959.648380</span></span>
+<span id="cb2-182"><a href="#cb2-182" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 5 value 955.999179</span></span>
+<span id="cb2-183"><a href="#cb2-183" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 6 value 955.979330</span></span>
+<span id="cb2-184"><a href="#cb2-184" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 7 value 955.979295</span></span>
+<span id="cb2-185"><a href="#cb2-185" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 7 value 955.979295</span></span>
+<span id="cb2-186"><a href="#cb2-186" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 7 value 955.979295</span></span>
+<span id="cb2-187"><a href="#cb2-187" aria-hidden="true" tabindex="-1"></a><span class="co">#> final value 955.979295 </span></span>
+<span id="cb2-188"><a href="#cb2-188" aria-hidden="true" tabindex="-1"></a><span class="co">#> converged</span></span>
+<span id="cb2-189"><a href="#cb2-189" aria-hidden="true" tabindex="-1"></a><span class="co">#> This is Run number 2 </span></span>
+<span id="cb2-190"><a href="#cb2-190" aria-hidden="true" tabindex="-1"></a><span class="co">#> does sou_gis exist: FALSE </span></span>
+<span id="cb2-191"><a href="#cb2-191" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-192"><a href="#cb2-192" aria-hidden="true" tabindex="-1"></a><span class="co">#> dataset final_set exists: FALSE </span></span>
+<span id="cb2-193"><a href="#cb2-193" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-194"><a href="#cb2-194" aria-hidden="true" tabindex="-1"></a><span class="co">#> decisiongroups exists: TRUE</span></span>
+<span id="cb2-195"><a href="#cb2-195" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 2 </span></span>
+<span id="cb2-196"><a href="#cb2-196" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1007 433 </span></span>
+<span id="cb2-197"><a href="#cb2-197" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-198"><a href="#cb2-198" aria-hidden="true" tabindex="-1"></a><span class="co">#> data has been made </span></span>
+<span id="cb2-199"><a href="#cb2-199" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-200"><a href="#cb2-200" aria-hidden="true" tabindex="-1"></a><span class="co">#> First few observations </span></span>
+<span id="cb2-201"><a href="#cb2-201" aria-hidden="true" tabindex="-1"></a><span class="co">#> ID Choice_situation alt1_x1 alt1_x2</span></span>
+<span id="cb2-202"><a href="#cb2-202" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1 7 80 2.5</span></span>
+<span id="cb2-203"><a href="#cb2-203" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1 19 20 2.5</span></span>
+<span id="cb2-204"><a href="#cb2-204" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1 30 20 10.0</span></span>
+<span id="cb2-205"><a href="#cb2-205" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 1 32 40 20.0</span></span>
+<span id="cb2-206"><a href="#cb2-206" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1 39 40 20.0</span></span>
+<span id="cb2-207"><a href="#cb2-207" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1 48 60 5.0</span></span>
+<span id="cb2-208"><a href="#cb2-208" aria-hidden="true" tabindex="-1"></a><span class="co">#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block</span></span>
+<span id="cb2-209"><a href="#cb2-209" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 10.0 60 20.0 10 1</span></span>
+<span id="cb2-210"><a href="#cb2-210" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 5.0 60 2.5 0 1</span></span>
+<span id="cb2-211"><a href="#cb2-211" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 5.0 80 5.0 10 1</span></span>
+<span id="cb2-212"><a href="#cb2-212" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 2.5 80 2.5 0 1</span></span>
+<span id="cb2-213"><a href="#cb2-213" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 0.0 80 10.0 10 1</span></span>
+<span id="cb2-214"><a href="#cb2-214" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 2.5 20 5.0 10 1</span></span>
+<span id="cb2-215"><a href="#cb2-215" aria-hidden="true" tabindex="-1"></a><span class="co">#> group V_1 V_2 e_1</span></span>
+<span id="cb2-216"><a href="#cb2-216" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1 -0.775 -1.800 -0.8816771</span></span>
+<span id="cb2-217"><a href="#cb2-217" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1 -0.275 -0.775 0.9004269</span></span>
+<span id="cb2-218"><a href="#cb2-218" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1 -0.800 -0.950 -0.3108731</span></span>
+<span id="cb2-219"><a href="#cb2-219" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 1 -1.750 -0.975 -0.7695269</span></span>
+<span id="cb2-220"><a href="#cb2-220" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1 -1.800 -1.300 2.8853455</span></span>
+<span id="cb2-221"><a href="#cb2-221" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1 -0.900 -0.350 -0.1098324</span></span>
+<span id="cb2-222"><a href="#cb2-222" aria-hidden="true" tabindex="-1"></a><span class="co">#> e_2 U_1 U_2</span></span>
+<span id="cb2-223"><a href="#cb2-223" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 0.6516580 -1.6566771 -1.14834197</span></span>
+<span id="cb2-224"><a href="#cb2-224" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 0.4584193 0.6254269 -0.31658066</span></span>
+<span id="cb2-225"><a href="#cb2-225" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1.2184928 -1.1108731 0.26849278</span></span>
+<span id="cb2-226"><a href="#cb2-226" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 -0.1660211 -2.5195269 -1.14102109</span></span>
+<span id="cb2-227"><a href="#cb2-227" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 -0.5943992 1.0853455 -1.89439922</span></span>
+<span id="cb2-228"><a href="#cb2-228" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 0.3193140 -1.0098324 -0.03068595</span></span>
+<span id="cb2-229"><a href="#cb2-229" aria-hidden="true" tabindex="-1"></a><span class="co">#> CHOICE</span></span>
+<span id="cb2-230"><a href="#cb2-230" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 2</span></span>
+<span id="cb2-231"><a href="#cb2-231" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1</span></span>
+<span id="cb2-232"><a href="#cb2-232" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 2</span></span>
+<span id="cb2-233"><a href="#cb2-233" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 2</span></span>
+<span id="cb2-234"><a href="#cb2-234" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1</span></span>
+<span id="cb2-235"><a href="#cb2-235" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 2</span></span>
+<span id="cb2-236"><a href="#cb2-236" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-237"><a href="#cb2-237" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-238"><a href="#cb2-238" aria-hidden="true" tabindex="-1"></a><span class="co">#> This is the utility functions </span></span>
+<span id="cb2-239"><a href="#cb2-239" aria-hidden="true" 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="cb2-240"><a href="#cb2-240" aria-hidden="true" tabindex="-1"></a><span class="co">#> Initial gradient value:</span></span>
+<span id="cb2-241"><a href="#cb2-241" aria-hidden="true" tabindex="-1"></a><span class="co">#> bpreis blade bwarte </span></span>
+<span id="cb2-242"><a href="#cb2-242" aria-hidden="true" tabindex="-1"></a><span class="co">#> 120 -655 295 </span></span>
+<span id="cb2-243"><a href="#cb2-243" aria-hidden="true" tabindex="-1"></a><span class="co">#> initial value 998.131940 </span></span>
+<span id="cb2-244"><a href="#cb2-244" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 2 value 994.305298</span></span>
+<span id="cb2-245"><a href="#cb2-245" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 3 value 990.053293</span></span>
+<span id="cb2-246"><a href="#cb2-246" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 4 value 989.940656</span></span>
+<span id="cb2-247"><a href="#cb2-247" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 5 value 987.629292</span></span>
+<span id="cb2-248"><a href="#cb2-248" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 6 value 987.628992</span></span>
+<span id="cb2-249"><a href="#cb2-249" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 6 value 987.628991</span></span>
+<span id="cb2-250"><a href="#cb2-250" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 6 value 987.628991</span></span>
+<span id="cb2-251"><a href="#cb2-251" aria-hidden="true" tabindex="-1"></a><span class="co">#> final value 987.628991 </span></span>
+<span id="cb2-252"><a href="#cb2-252" aria-hidden="true" tabindex="-1"></a><span class="co">#> converged</span></span>
+<span id="cb2-253"><a href="#cb2-253" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-254"><a href="#cb2-254" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-255"><a href="#cb2-255" aria-hidden="true" tabindex="-1"></a><span class="co">#> ================ ==== === ===== ==== ===== ===== ===== ====</span></span>
+<span id="cb2-256"><a href="#cb2-256" aria-hidden="true" tabindex="-1"></a><span class="co">#> \ vars n mean sd min max range se</span></span>
+<span id="cb2-257"><a href="#cb2-257" aria-hidden="true" tabindex="-1"></a><span class="co">#> ================ ==== === ===== ==== ===== ===== ===== ====</span></span>
+<span id="cb2-258"><a href="#cb2-258" aria-hidden="true" tabindex="-1"></a><span class="co">#> est_bpreis 1 2 -0.01 0.01 -0.01 0.00 0.01 0.00</span></span>
+<span id="cb2-259"><a href="#cb2-259" aria-hidden="true" tabindex="-1"></a><span class="co">#> est_blade 2 2 -0.04 0.02 -0.06 -0.02 0.03 0.02</span></span>
+<span id="cb2-260"><a href="#cb2-260" aria-hidden="true" tabindex="-1"></a><span class="co">#> est_bwarte 3 2 0.02 0.00 0.02 0.03 0.01 0.00</span></span>
+<span id="cb2-261"><a href="#cb2-261" aria-hidden="true" tabindex="-1"></a><span class="co">#> rob_pval0_bpreis 4 2 0.04 0.06 0.00 0.09 0.09 0.04</span></span>
+<span id="cb2-262"><a href="#cb2-262" aria-hidden="true" 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="cb2-263"><a href="#cb2-263" aria-hidden="true" tabindex="-1"></a><span class="co">#> rob_pval0_bwarte 6 2 0.04 0.03 0.02 0.06 0.04 0.02</span></span>
+<span id="cb2-264"><a href="#cb2-264" aria-hidden="true" tabindex="-1"></a><span class="co">#> ================ ==== === ===== ==== ===== ===== ===== ====</span></span>
+<span id="cb2-265"><a href="#cb2-265" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-266"><a href="#cb2-266" aria-hidden="true" tabindex="-1"></a><span class="co">#> FALSE TRUE </span></span>
+<span id="cb2-267"><a href="#cb2-267" aria-hidden="true" tabindex="-1"></a><span class="co">#> 50 50 </span></span>
+<span id="cb2-268"><a href="#cb2-268" aria-hidden="true" tabindex="-1"></a><span class="co">#> Utility function used in simulation, ie the true utility: </span></span>
+<span id="cb2-269"><a href="#cb2-269" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-270"><a href="#cb2-270" aria-hidden="true" tabindex="-1"></a><span class="co">#> $u1</span></span>
+<span id="cb2-271"><a href="#cb2-271" aria-hidden="true" tabindex="-1"></a><span class="co">#> $u1$v1</span></span>
+<span id="cb2-272"><a href="#cb2-272" aria-hidden="true" tabindex="-1"></a><span class="co">#> V.1 ~ bpreis * alt1.x1 + blade * alt1.x2 + bwarte * alt1.x3</span></span>
+<span id="cb2-273"><a href="#cb2-273" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-274"><a href="#cb2-274" aria-hidden="true" tabindex="-1"></a><span class="co">#> $u1$v2</span></span>
+<span id="cb2-275"><a href="#cb2-275" aria-hidden="true" tabindex="-1"></a><span class="co">#> V.2 ~ bpreis * alt2.x1 + blade * alt2.x2 + bwarte * alt2.x3</span></span>
+<span id="cb2-276"><a href="#cb2-276" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-277"><a href="#cb2-277" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-278"><a href="#cb2-278" aria-hidden="true" tabindex="-1"></a><span class="co">#> $u2</span></span>
+<span id="cb2-279"><a href="#cb2-279" aria-hidden="true" tabindex="-1"></a><span class="co">#> $u2$v1</span></span>
+<span id="cb2-280"><a href="#cb2-280" aria-hidden="true" tabindex="-1"></a><span class="co">#> V.1 ~ bpreis * alt1.x1</span></span>
+<span id="cb2-281"><a href="#cb2-281" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-282"><a href="#cb2-282" aria-hidden="true" tabindex="-1"></a><span class="co">#> $u2$v2</span></span>
+<span id="cb2-283"><a href="#cb2-283" aria-hidden="true" tabindex="-1"></a><span class="co">#> V.2 ~ bpreis * alt2.x1</span></span>
+<span id="cb2-284"><a href="#cb2-284" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-285"><a href="#cb2-285" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-286"><a href="#cb2-286" aria-hidden="true" tabindex="-1"></a><span class="co">#> Utility function used for Logit estimation with mixl: </span></span>
+<span id="cb2-287"><a href="#cb2-287" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-288"><a href="#cb2-288" aria-hidden="true" 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="cb2-289"><a href="#cb2-289" aria-hidden="true" tabindex="-1"></a><span class="co">#> New names:</span></span>
+<span id="cb2-290"><a href="#cb2-290" aria-hidden="true" tabindex="-1"></a><span class="co">#> • `Choice situation` -></span></span>
+<span id="cb2-291"><a href="#cb2-291" aria-hidden="true" tabindex="-1"></a><span class="co">#> `Choice.situation`</span></span>
+<span id="cb2-292"><a href="#cb2-292" aria-hidden="true" tabindex="-1"></a><span class="co">#> • `` -> `...10`</span></span>
+<span id="cb2-293"><a href="#cb2-293" aria-hidden="true" tabindex="-1"></a><span class="co">#> Warning: One or more parsing issues, call</span></span>
+<span id="cb2-294"><a href="#cb2-294" aria-hidden="true" tabindex="-1"></a><span class="co">#> `problems()` on your data frame for</span></span>
+<span id="cb2-295"><a href="#cb2-295" aria-hidden="true" tabindex="-1"></a><span class="co">#> details, e.g.:</span></span>
+<span id="cb2-296"><a href="#cb2-296" aria-hidden="true" tabindex="-1"></a><span class="co">#> dat <- vroom(...)</span></span>
+<span id="cb2-297"><a href="#cb2-297" aria-hidden="true" tabindex="-1"></a><span class="co">#> problems(dat)</span></span>
+<span id="cb2-298"><a href="#cb2-298" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-299"><a href="#cb2-299" aria-hidden="true" tabindex="-1"></a><span class="co">#> does sou_gis exist: FALSE </span></span>
+<span id="cb2-300"><a href="#cb2-300" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-301"><a href="#cb2-301" aria-hidden="true" tabindex="-1"></a><span class="co">#> dataset final_set exists: FALSE </span></span>
+<span id="cb2-302"><a href="#cb2-302" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-303"><a href="#cb2-303" aria-hidden="true" tabindex="-1"></a><span class="co">#> decisiongroups exists: TRUE</span></span>
+<span id="cb2-304"><a href="#cb2-304" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 2 </span></span>
+<span id="cb2-305"><a href="#cb2-305" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1007 433 </span></span>
+<span id="cb2-306"><a href="#cb2-306" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-307"><a href="#cb2-307" aria-hidden="true" tabindex="-1"></a><span class="co">#> data has been made </span></span>
+<span id="cb2-308"><a href="#cb2-308" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-309"><a href="#cb2-309" aria-hidden="true" tabindex="-1"></a><span class="co">#> First few observations </span></span>
+<span id="cb2-310"><a href="#cb2-310" aria-hidden="true" tabindex="-1"></a><span class="co">#> ID Choice_situation alt1_x1 alt1_x2</span></span>
+<span id="cb2-311"><a href="#cb2-311" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1 12 60 2.5</span></span>
+<span id="cb2-312"><a href="#cb2-312" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1 16 20 10.0</span></span>
+<span id="cb2-313"><a href="#cb2-313" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1 17 20 20.0</span></span>
+<span id="cb2-314"><a href="#cb2-314" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 1 25 60 5.0</span></span>
+<span id="cb2-315"><a href="#cb2-315" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1 29 20 5.0</span></span>
+<span id="cb2-316"><a href="#cb2-316" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1 32 40 10.0</span></span>
+<span id="cb2-317"><a href="#cb2-317" aria-hidden="true" tabindex="-1"></a><span class="co">#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block</span></span>
+<span id="cb2-318"><a href="#cb2-318" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 0.0 20 20.0 10 1</span></span>
+<span id="cb2-319"><a href="#cb2-319" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 5.0 40 5.0 0 1</span></span>
+<span id="cb2-320"><a href="#cb2-320" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 0.0 80 10.0 10 1</span></span>
+<span id="cb2-321"><a href="#cb2-321" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 10.0 20 20.0 5 1</span></span>
+<span id="cb2-322"><a href="#cb2-322" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 10.0 80 5.0 0 1</span></span>
+<span id="cb2-323"><a href="#cb2-323" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 2.5 80 2.5 5 1</span></span>
+<span id="cb2-324"><a href="#cb2-324" aria-hidden="true" tabindex="-1"></a><span class="co">#> group V_1 V_2 e_1</span></span>
+<span id="cb2-325"><a href="#cb2-325" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1 -0.775 -1.400 1.20580231</span></span>
+<span id="cb2-326"><a href="#cb2-326" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1 -0.800 -0.750 -0.72752412</span></span>
+<span id="cb2-327"><a href="#cb2-327" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1 -1.600 -1.300 -0.05762304</span></span>
+<span id="cb2-328"><a href="#cb2-328" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 1 -0.750 -1.500 -0.83547157</span></span>
+<span id="cb2-329"><a href="#cb2-329" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1 -0.350 -1.150 3.85444600</span></span>
+<span id="cb2-330"><a href="#cb2-330" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1 -1.050 -0.875 1.64701776</span></span>
+<span id="cb2-331"><a href="#cb2-331" aria-hidden="true" tabindex="-1"></a><span class="co">#> e_2 U_1 U_2</span></span>
+<span id="cb2-332"><a href="#cb2-332" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 -0.28691332 0.4308023 -1.6869133</span></span>
+<span id="cb2-333"><a href="#cb2-333" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 0.06648158 -1.5275241 -0.6835184</span></span>
+<span id="cb2-334"><a href="#cb2-334" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1.68916541 -1.6576230 0.3891654</span></span>
+<span id="cb2-335"><a href="#cb2-335" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 0.40357792 -1.5854716 -1.0964221</span></span>
+<span id="cb2-336"><a href="#cb2-336" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 0.13880669 3.5044460 -1.0111933</span></span>
+<span id="cb2-337"><a href="#cb2-337" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1.09745093 0.5970178 0.2224509</span></span>
+<span id="cb2-338"><a href="#cb2-338" aria-hidden="true" tabindex="-1"></a><span class="co">#> CHOICE</span></span>
+<span id="cb2-339"><a href="#cb2-339" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1</span></span>
+<span id="cb2-340"><a href="#cb2-340" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 2</span></span>
+<span id="cb2-341"><a href="#cb2-341" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 2</span></span>
+<span id="cb2-342"><a href="#cb2-342" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 2</span></span>
+<span id="cb2-343"><a href="#cb2-343" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1</span></span>
+<span id="cb2-344"><a href="#cb2-344" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1</span></span>
+<span id="cb2-345"><a href="#cb2-345" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-346"><a href="#cb2-346" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-347"><a href="#cb2-347" aria-hidden="true" tabindex="-1"></a><span class="co">#> This is Run number 1 </span></span>
+<span id="cb2-348"><a href="#cb2-348" aria-hidden="true" tabindex="-1"></a><span class="co">#> does sou_gis exist: FALSE </span></span>
+<span id="cb2-349"><a href="#cb2-349" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-350"><a href="#cb2-350" aria-hidden="true" tabindex="-1"></a><span class="co">#> dataset final_set exists: FALSE </span></span>
+<span id="cb2-351"><a href="#cb2-351" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-352"><a href="#cb2-352" aria-hidden="true" tabindex="-1"></a><span class="co">#> decisiongroups exists: TRUE</span></span>
+<span id="cb2-353"><a href="#cb2-353" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 2 </span></span>
+<span id="cb2-354"><a href="#cb2-354" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1007 433 </span></span>
+<span id="cb2-355"><a href="#cb2-355" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-356"><a href="#cb2-356" aria-hidden="true" tabindex="-1"></a><span class="co">#> data has been made </span></span>
+<span id="cb2-357"><a href="#cb2-357" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-358"><a href="#cb2-358" aria-hidden="true" tabindex="-1"></a><span class="co">#> First few observations </span></span>
+<span id="cb2-359"><a href="#cb2-359" aria-hidden="true" tabindex="-1"></a><span class="co">#> ID Choice_situation alt1_x1 alt1_x2</span></span>
+<span id="cb2-360"><a href="#cb2-360" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1 12 60 2.5</span></span>
+<span id="cb2-361"><a href="#cb2-361" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1 16 20 10.0</span></span>
+<span id="cb2-362"><a href="#cb2-362" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1 17 20 20.0</span></span>
+<span id="cb2-363"><a href="#cb2-363" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 1 25 60 5.0</span></span>
+<span id="cb2-364"><a href="#cb2-364" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1 29 20 5.0</span></span>
+<span id="cb2-365"><a href="#cb2-365" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1 32 40 10.0</span></span>
+<span id="cb2-366"><a href="#cb2-366" aria-hidden="true" tabindex="-1"></a><span class="co">#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block</span></span>
+<span id="cb2-367"><a href="#cb2-367" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 0.0 20 20.0 10 1</span></span>
+<span id="cb2-368"><a href="#cb2-368" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 5.0 40 5.0 0 1</span></span>
+<span id="cb2-369"><a href="#cb2-369" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 0.0 80 10.0 10 1</span></span>
+<span id="cb2-370"><a href="#cb2-370" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 10.0 20 20.0 5 1</span></span>
+<span id="cb2-371"><a href="#cb2-371" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 10.0 80 5.0 0 1</span></span>
+<span id="cb2-372"><a href="#cb2-372" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 2.5 80 2.5 5 1</span></span>
+<span id="cb2-373"><a href="#cb2-373" aria-hidden="true" tabindex="-1"></a><span class="co">#> group V_1 V_2 e_1</span></span>
+<span id="cb2-374"><a href="#cb2-374" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1 -0.775 -1.400 -0.09932726</span></span>
+<span id="cb2-375"><a href="#cb2-375" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1 -0.800 -0.750 2.18018219</span></span>
+<span id="cb2-376"><a href="#cb2-376" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1 -1.600 -1.300 1.30134429</span></span>
+<span id="cb2-377"><a href="#cb2-377" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 1 -0.750 -1.500 1.55197796</span></span>
+<span id="cb2-378"><a href="#cb2-378" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1 -0.350 -1.150 0.07874983</span></span>
+<span id="cb2-379"><a href="#cb2-379" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1 -1.050 -0.875 -1.06565108</span></span>
+<span id="cb2-380"><a href="#cb2-380" aria-hidden="true" tabindex="-1"></a><span class="co">#> e_2 U_1 U_2</span></span>
+<span id="cb2-381"><a href="#cb2-381" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 2.2497903 -0.8743273 0.84979034</span></span>
+<span id="cb2-382"><a href="#cb2-382" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 0.3329742 1.3801822 -0.41702578</span></span>
+<span id="cb2-383"><a href="#cb2-383" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 0.9046182 -0.2986557 -0.39538182</span></span>
+<span id="cb2-384"><a href="#cb2-384" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 -1.2414809 0.8019780 -2.74148090</span></span>
+<span id="cb2-385"><a href="#cb2-385" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 -0.8624243 -0.2712502 -2.01242427</span></span>
+<span id="cb2-386"><a href="#cb2-386" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 0.9398788 -2.1156511 0.06487882</span></span>
+<span id="cb2-387"><a href="#cb2-387" aria-hidden="true" tabindex="-1"></a><span class="co">#> CHOICE</span></span>
+<span id="cb2-388"><a href="#cb2-388" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 2</span></span>
+<span id="cb2-389"><a href="#cb2-389" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1</span></span>
+<span id="cb2-390"><a href="#cb2-390" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1</span></span>
+<span id="cb2-391"><a href="#cb2-391" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 1</span></span>
+<span id="cb2-392"><a href="#cb2-392" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1</span></span>
+<span id="cb2-393"><a href="#cb2-393" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 2</span></span>
+<span id="cb2-394"><a href="#cb2-394" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-395"><a href="#cb2-395" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-396"><a href="#cb2-396" aria-hidden="true" tabindex="-1"></a><span class="co">#> This is the utility functions </span></span>
+<span id="cb2-397"><a href="#cb2-397" aria-hidden="true" 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="cb2-398"><a href="#cb2-398" aria-hidden="true" tabindex="-1"></a><span class="co">#> Initial gradient value:</span></span>
+<span id="cb2-399"><a href="#cb2-399" aria-hidden="true" tabindex="-1"></a><span class="co">#> bpreis blade bwarte </span></span>
+<span id="cb2-400"><a href="#cb2-400" aria-hidden="true" tabindex="-1"></a><span class="co">#> -340 -1095 305 </span></span>
+<span id="cb2-401"><a href="#cb2-401" aria-hidden="true" tabindex="-1"></a><span class="co">#> initial value 998.131940 </span></span>
+<span id="cb2-402"><a href="#cb2-402" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 2 value 984.073383</span></span>
+<span id="cb2-403"><a href="#cb2-403" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 3 value 978.081615</span></span>
+<span id="cb2-404"><a href="#cb2-404" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 4 value 977.767304</span></span>
+<span id="cb2-405"><a href="#cb2-405" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 5 value 971.033395</span></span>
+<span id="cb2-406"><a href="#cb2-406" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 6 value 971.027390</span></span>
+<span id="cb2-407"><a href="#cb2-407" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 6 value 971.027385</span></span>
+<span id="cb2-408"><a href="#cb2-408" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 6 value 971.027385</span></span>
+<span id="cb2-409"><a href="#cb2-409" aria-hidden="true" tabindex="-1"></a><span class="co">#> final value 971.027385 </span></span>
+<span id="cb2-410"><a href="#cb2-410" aria-hidden="true" tabindex="-1"></a><span class="co">#> converged</span></span>
+<span id="cb2-411"><a href="#cb2-411" aria-hidden="true" tabindex="-1"></a><span class="co">#> This is Run number 2 </span></span>
+<span id="cb2-412"><a href="#cb2-412" aria-hidden="true" tabindex="-1"></a><span class="co">#> does sou_gis exist: FALSE </span></span>
+<span id="cb2-413"><a href="#cb2-413" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-414"><a href="#cb2-414" aria-hidden="true" tabindex="-1"></a><span class="co">#> dataset final_set exists: FALSE </span></span>
+<span id="cb2-415"><a href="#cb2-415" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-416"><a href="#cb2-416" aria-hidden="true" tabindex="-1"></a><span class="co">#> decisiongroups exists: TRUE</span></span>
+<span id="cb2-417"><a href="#cb2-417" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 2 </span></span>
+<span id="cb2-418"><a href="#cb2-418" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1007 433 </span></span>
+<span id="cb2-419"><a href="#cb2-419" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-420"><a href="#cb2-420" aria-hidden="true" tabindex="-1"></a><span class="co">#> data has been made </span></span>
+<span id="cb2-421"><a href="#cb2-421" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-422"><a href="#cb2-422" aria-hidden="true" tabindex="-1"></a><span class="co">#> First few observations </span></span>
+<span id="cb2-423"><a href="#cb2-423" aria-hidden="true" tabindex="-1"></a><span class="co">#> ID Choice_situation alt1_x1 alt1_x2</span></span>
+<span id="cb2-424"><a href="#cb2-424" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1 12 60 2.5</span></span>
+<span id="cb2-425"><a href="#cb2-425" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1 16 20 10.0</span></span>
+<span id="cb2-426"><a href="#cb2-426" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1 17 20 20.0</span></span>
+<span id="cb2-427"><a href="#cb2-427" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 1 25 60 5.0</span></span>
+<span id="cb2-428"><a href="#cb2-428" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1 29 20 5.0</span></span>
+<span id="cb2-429"><a href="#cb2-429" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1 32 40 10.0</span></span>
+<span id="cb2-430"><a href="#cb2-430" aria-hidden="true" tabindex="-1"></a><span class="co">#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block</span></span>
+<span id="cb2-431"><a href="#cb2-431" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 0.0 20 20.0 10 1</span></span>
+<span id="cb2-432"><a href="#cb2-432" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 5.0 40 5.0 0 1</span></span>
+<span id="cb2-433"><a href="#cb2-433" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 0.0 80 10.0 10 1</span></span>
+<span id="cb2-434"><a href="#cb2-434" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 10.0 20 20.0 5 1</span></span>
+<span id="cb2-435"><a href="#cb2-435" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 10.0 80 5.0 0 1</span></span>
+<span id="cb2-436"><a href="#cb2-436" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 2.5 80 2.5 5 1</span></span>
+<span id="cb2-437"><a href="#cb2-437" aria-hidden="true" tabindex="-1"></a><span class="co">#> group V_1 V_2 e_1</span></span>
+<span id="cb2-438"><a href="#cb2-438" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1 -0.775 -1.400 0.44334136</span></span>
+<span id="cb2-439"><a href="#cb2-439" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1 -0.800 -0.750 -0.43185157</span></span>
+<span id="cb2-440"><a href="#cb2-440" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1 -1.600 -1.300 -0.09584172</span></span>
+<span id="cb2-441"><a href="#cb2-441" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 1 -0.750 -1.500 2.74658736</span></span>
+<span id="cb2-442"><a href="#cb2-442" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1 -0.350 -1.150 -0.51575280</span></span>
+<span id="cb2-443"><a href="#cb2-443" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1 -1.050 -0.875 -0.33088933</span></span>
+<span id="cb2-444"><a href="#cb2-444" aria-hidden="true" tabindex="-1"></a><span class="co">#> e_2 U_1 U_2 CHOICE</span></span>
+<span id="cb2-445"><a href="#cb2-445" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 0.3975165 -0.3316586 -1.0024835 1</span></span>
+<span id="cb2-446"><a href="#cb2-446" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1.4211569 -1.2318516 0.6711569 2</span></span>
+<span id="cb2-447"><a href="#cb2-447" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1.0034880 -1.6958417 -0.2965120 2</span></span>
+<span id="cb2-448"><a href="#cb2-448" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 0.8780181 1.9965874 -0.6219819 1</span></span>
+<span id="cb2-449"><a href="#cb2-449" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 0.9818505 -0.8657528 -0.1681495 2</span></span>
+<span id="cb2-450"><a href="#cb2-450" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1.7042698 -1.3808893 0.8292698 2</span></span>
+<span id="cb2-451"><a href="#cb2-451" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-452"><a href="#cb2-452" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-453"><a href="#cb2-453" aria-hidden="true" tabindex="-1"></a><span class="co">#> This is the utility functions </span></span>
+<span id="cb2-454"><a href="#cb2-454" aria-hidden="true" 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="cb2-455"><a href="#cb2-455" aria-hidden="true" tabindex="-1"></a><span class="co">#> Initial gradient value:</span></span>
+<span id="cb2-456"><a href="#cb2-456" aria-hidden="true" tabindex="-1"></a><span class="co">#> bpreis blade bwarte </span></span>
+<span id="cb2-457"><a href="#cb2-457" aria-hidden="true" tabindex="-1"></a><span class="co">#> -280 -905 345 </span></span>
+<span id="cb2-458"><a href="#cb2-458" aria-hidden="true" tabindex="-1"></a><span class="co">#> initial value 998.131940 </span></span>
+<span id="cb2-459"><a href="#cb2-459" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 2 value 988.003109</span></span>
+<span id="cb2-460"><a href="#cb2-460" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 3 value 983.732741</span></span>
+<span id="cb2-461"><a href="#cb2-461" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 4 value 983.724196</span></span>
+<span id="cb2-462"><a href="#cb2-462" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 5 value 979.048736</span></span>
+<span id="cb2-463"><a href="#cb2-463" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 6 value 979.044949</span></span>
+<span id="cb2-464"><a href="#cb2-464" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 6 value 979.044947</span></span>
+<span id="cb2-465"><a href="#cb2-465" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 6 value 979.044947</span></span>
+<span id="cb2-466"><a href="#cb2-466" aria-hidden="true" tabindex="-1"></a><span class="co">#> final value 979.044947 </span></span>
+<span id="cb2-467"><a href="#cb2-467" aria-hidden="true" tabindex="-1"></a><span class="co">#> converged</span></span>
+<span id="cb2-468"><a href="#cb2-468" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-469"><a href="#cb2-469" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-470"><a href="#cb2-470" aria-hidden="true" tabindex="-1"></a><span class="co">#> ================ ==== === ===== ==== ===== ===== ===== ====</span></span>
+<span id="cb2-471"><a href="#cb2-471" aria-hidden="true" tabindex="-1"></a><span class="co">#> \ vars n mean sd min max range se</span></span>
+<span id="cb2-472"><a href="#cb2-472" aria-hidden="true" tabindex="-1"></a><span class="co">#> ================ ==== === ===== ==== ===== ===== ===== ====</span></span>
+<span id="cb2-473"><a href="#cb2-473" aria-hidden="true" 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="cb2-474"><a href="#cb2-474" aria-hidden="true" tabindex="-1"></a><span class="co">#> est_blade 2 2 -0.04 0.01 -0.05 -0.04 0.01 0.01</span></span>
+<span id="cb2-475"><a href="#cb2-475" aria-hidden="true" tabindex="-1"></a><span class="co">#> est_bwarte 3 2 0.01 0.01 0.00 0.01 0.01 0.00</span></span>
+<span id="cb2-476"><a href="#cb2-476" aria-hidden="true" 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="cb2-477"><a href="#cb2-477" aria-hidden="true" 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="cb2-478"><a href="#cb2-478" aria-hidden="true" tabindex="-1"></a><span class="co">#> rob_pval0_bwarte 6 2 0.50 0.41 0.21 0.79 0.58 0.29</span></span>
+<span id="cb2-479"><a href="#cb2-479" aria-hidden="true" tabindex="-1"></a><span class="co">#> ================ ==== === ===== ==== ===== ===== ===== ====</span></span>
+<span id="cb2-480"><a href="#cb2-480" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-481"><a href="#cb2-481" aria-hidden="true" tabindex="-1"></a><span class="co">#> FALSE </span></span>
+<span id="cb2-482"><a href="#cb2-482" aria-hidden="true" tabindex="-1"></a><span class="co">#> 100 </span></span>
+<span id="cb2-483"><a href="#cb2-483" aria-hidden="true" tabindex="-1"></a><span class="co">#> Utility function used in simulation, ie the true utility: </span></span>
+<span id="cb2-484"><a href="#cb2-484" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-485"><a href="#cb2-485" aria-hidden="true" tabindex="-1"></a><span class="co">#> $u1</span></span>
+<span id="cb2-486"><a href="#cb2-486" aria-hidden="true" tabindex="-1"></a><span class="co">#> $u1$v1</span></span>
+<span id="cb2-487"><a href="#cb2-487" aria-hidden="true" tabindex="-1"></a><span class="co">#> V.1 ~ bpreis * alt1.x1 + blade * alt1.x2 + bwarte * alt1.x3</span></span>
+<span id="cb2-488"><a href="#cb2-488" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-489"><a href="#cb2-489" aria-hidden="true" tabindex="-1"></a><span class="co">#> $u1$v2</span></span>
+<span id="cb2-490"><a href="#cb2-490" aria-hidden="true" tabindex="-1"></a><span class="co">#> V.2 ~ bpreis * alt2.x1 + blade * alt2.x2 + bwarte * alt2.x3</span></span>
+<span id="cb2-491"><a href="#cb2-491" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-492"><a href="#cb2-492" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-493"><a href="#cb2-493" aria-hidden="true" tabindex="-1"></a><span class="co">#> $u2</span></span>
+<span id="cb2-494"><a href="#cb2-494" aria-hidden="true" tabindex="-1"></a><span class="co">#> $u2$v1</span></span>
+<span id="cb2-495"><a href="#cb2-495" aria-hidden="true" tabindex="-1"></a><span class="co">#> V.1 ~ bpreis * alt1.x1</span></span>
+<span id="cb2-496"><a href="#cb2-496" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-497"><a href="#cb2-497" aria-hidden="true" tabindex="-1"></a><span class="co">#> $u2$v2</span></span>
+<span id="cb2-498"><a href="#cb2-498" aria-hidden="true" tabindex="-1"></a><span class="co">#> V.2 ~ bpreis * alt2.x1</span></span>
+<span id="cb2-499"><a href="#cb2-499" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-500"><a href="#cb2-500" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-501"><a href="#cb2-501" aria-hidden="true" tabindex="-1"></a><span class="co">#> Utility function used for Logit estimation with mixl: </span></span>
+<span id="cb2-502"><a href="#cb2-502" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-503"><a href="#cb2-503" aria-hidden="true" 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="cb2-504"><a href="#cb2-504" aria-hidden="true" tabindex="-1"></a><span class="co">#> New names:</span></span>
+<span id="cb2-505"><a href="#cb2-505" aria-hidden="true" tabindex="-1"></a><span class="co">#> • `Choice situation` -></span></span>
+<span id="cb2-506"><a href="#cb2-506" aria-hidden="true" tabindex="-1"></a><span class="co">#> `Choice.situation`</span></span>
+<span id="cb2-507"><a href="#cb2-507" aria-hidden="true" tabindex="-1"></a><span class="co">#> • `` -> `...10`</span></span>
+<span id="cb2-508"><a href="#cb2-508" aria-hidden="true" tabindex="-1"></a><span class="co">#> Warning: One or more parsing issues, call</span></span>
+<span id="cb2-509"><a href="#cb2-509" aria-hidden="true" tabindex="-1"></a><span class="co">#> `problems()` on your data frame for</span></span>
+<span id="cb2-510"><a href="#cb2-510" aria-hidden="true" tabindex="-1"></a><span class="co">#> details, e.g.:</span></span>
+<span id="cb2-511"><a href="#cb2-511" aria-hidden="true" tabindex="-1"></a><span class="co">#> dat <- vroom(...)</span></span>
+<span id="cb2-512"><a href="#cb2-512" aria-hidden="true" tabindex="-1"></a><span class="co">#> problems(dat)</span></span>
+<span id="cb2-513"><a href="#cb2-513" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-514"><a href="#cb2-514" aria-hidden="true" tabindex="-1"></a><span class="co">#> does sou_gis exist: FALSE </span></span>
+<span id="cb2-515"><a href="#cb2-515" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-516"><a href="#cb2-516" aria-hidden="true" tabindex="-1"></a><span class="co">#> dataset final_set exists: FALSE </span></span>
+<span id="cb2-517"><a href="#cb2-517" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-518"><a href="#cb2-518" aria-hidden="true" tabindex="-1"></a><span class="co">#> decisiongroups exists: TRUE</span></span>
+<span id="cb2-519"><a href="#cb2-519" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 2 </span></span>
+<span id="cb2-520"><a href="#cb2-520" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1007 433 </span></span>
+<span id="cb2-521"><a href="#cb2-521" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-522"><a href="#cb2-522" aria-hidden="true" tabindex="-1"></a><span class="co">#> data has been made </span></span>
+<span id="cb2-523"><a href="#cb2-523" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-524"><a href="#cb2-524" aria-hidden="true" tabindex="-1"></a><span class="co">#> First few observations </span></span>
+<span id="cb2-525"><a href="#cb2-525" aria-hidden="true" tabindex="-1"></a><span class="co">#> ID Choice_situation alt1_x1 alt1_x2</span></span>
+<span id="cb2-526"><a href="#cb2-526" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1 3 80 5.0</span></span>
+<span id="cb2-527"><a href="#cb2-527" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1 5 60 2.5</span></span>
+<span id="cb2-528"><a href="#cb2-528" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1 10 80 2.5</span></span>
+<span id="cb2-529"><a href="#cb2-529" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 1 34 80 2.5</span></span>
+<span id="cb2-530"><a href="#cb2-530" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1 37 40 5.0</span></span>
+<span id="cb2-531"><a href="#cb2-531" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1 39 20 20.0</span></span>
+<span id="cb2-532"><a href="#cb2-532" aria-hidden="true" tabindex="-1"></a><span class="co">#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block</span></span>
+<span id="cb2-533"><a href="#cb2-533" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 0.0 20 5.0 10.0 1</span></span>
+<span id="cb2-534"><a href="#cb2-534" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 5.0 20 20.0 5.0 1</span></span>
+<span id="cb2-535"><a href="#cb2-535" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 2.5 20 20.0 0.0 1</span></span>
+<span id="cb2-536"><a href="#cb2-536" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 5.0 60 5.0 5.0 1</span></span>
+<span id="cb2-537"><a href="#cb2-537" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 10.0 60 5.0 2.5 1</span></span>
+<span id="cb2-538"><a href="#cb2-538" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 2.5 60 2.5 2.5 1</span></span>
+<span id="cb2-539"><a href="#cb2-539" aria-hidden="true" tabindex="-1"></a><span class="co">#> group V_1 V_2 e_1</span></span>
+<span id="cb2-540"><a href="#cb2-540" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1 -1.150 -0.350 -0.32663211</span></span>
+<span id="cb2-541"><a href="#cb2-541" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1 -0.675 -1.500 -0.04162689</span></span>
+<span id="cb2-542"><a href="#cb2-542" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1 -0.925 -1.600 -0.52492188</span></span>
+<span id="cb2-543"><a href="#cb2-543" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 1 -0.875 -0.850 -1.14189023</span></span>
+<span id="cb2-544"><a href="#cb2-544" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1 -0.550 -0.900 0.19650068</span></span>
+<span id="cb2-545"><a href="#cb2-545" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1 -1.550 -0.725 2.74825383</span></span>
+<span id="cb2-546"><a href="#cb2-546" aria-hidden="true" tabindex="-1"></a><span class="co">#> e_2 U_1 U_2 CHOICE</span></span>
+<span id="cb2-547"><a href="#cb2-547" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 0.2288010 -1.4766321 -0.1211990 2</span></span>
+<span id="cb2-548"><a href="#cb2-548" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1.0875948 -0.7166269 -0.4124052 2</span></span>
+<span id="cb2-549"><a href="#cb2-549" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 0.1472598 -1.4499219 -1.4527402 1</span></span>
+<span id="cb2-550"><a href="#cb2-550" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 0.5765191 -2.0168902 -0.2734809 2</span></span>
+<span id="cb2-551"><a href="#cb2-551" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 -0.5803934 -0.3534993 -1.4803934 1</span></span>
+<span id="cb2-552"><a href="#cb2-552" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 -0.8761884 1.1982538 -1.6011884 1</span></span>
+<span id="cb2-553"><a href="#cb2-553" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-554"><a href="#cb2-554" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-555"><a href="#cb2-555" aria-hidden="true" tabindex="-1"></a><span class="co">#> This is Run number 1 </span></span>
+<span id="cb2-556"><a href="#cb2-556" aria-hidden="true" tabindex="-1"></a><span class="co">#> does sou_gis exist: FALSE </span></span>
+<span id="cb2-557"><a href="#cb2-557" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-558"><a href="#cb2-558" aria-hidden="true" tabindex="-1"></a><span class="co">#> dataset final_set exists: FALSE </span></span>
+<span id="cb2-559"><a href="#cb2-559" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-560"><a href="#cb2-560" aria-hidden="true" tabindex="-1"></a><span class="co">#> decisiongroups exists: TRUE</span></span>
+<span id="cb2-561"><a href="#cb2-561" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 2 </span></span>
+<span id="cb2-562"><a href="#cb2-562" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1007 433 </span></span>
+<span id="cb2-563"><a href="#cb2-563" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-564"><a href="#cb2-564" aria-hidden="true" tabindex="-1"></a><span class="co">#> data has been made </span></span>
+<span id="cb2-565"><a href="#cb2-565" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-566"><a href="#cb2-566" aria-hidden="true" tabindex="-1"></a><span class="co">#> First few observations </span></span>
+<span id="cb2-567"><a href="#cb2-567" aria-hidden="true" tabindex="-1"></a><span class="co">#> ID Choice_situation alt1_x1 alt1_x2</span></span>
+<span id="cb2-568"><a href="#cb2-568" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1 3 80 5.0</span></span>
+<span id="cb2-569"><a href="#cb2-569" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1 5 60 2.5</span></span>
+<span id="cb2-570"><a href="#cb2-570" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1 10 80 2.5</span></span>
+<span id="cb2-571"><a href="#cb2-571" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 1 34 80 2.5</span></span>
+<span id="cb2-572"><a href="#cb2-572" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1 37 40 5.0</span></span>
+<span id="cb2-573"><a href="#cb2-573" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1 39 20 20.0</span></span>
+<span id="cb2-574"><a href="#cb2-574" aria-hidden="true" tabindex="-1"></a><span class="co">#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block</span></span>
+<span id="cb2-575"><a href="#cb2-575" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 0.0 20 5.0 10.0 1</span></span>
+<span id="cb2-576"><a href="#cb2-576" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 5.0 20 20.0 5.0 1</span></span>
+<span id="cb2-577"><a href="#cb2-577" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 2.5 20 20.0 0.0 1</span></span>
+<span id="cb2-578"><a href="#cb2-578" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 5.0 60 5.0 5.0 1</span></span>
+<span id="cb2-579"><a href="#cb2-579" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 10.0 60 5.0 2.5 1</span></span>
+<span id="cb2-580"><a href="#cb2-580" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 2.5 60 2.5 2.5 1</span></span>
+<span id="cb2-581"><a href="#cb2-581" aria-hidden="true" tabindex="-1"></a><span class="co">#> group V_1 V_2 e_1</span></span>
+<span id="cb2-582"><a href="#cb2-582" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1 -1.150 -0.350 0.9214793</span></span>
+<span id="cb2-583"><a href="#cb2-583" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1 -0.675 -1.500 -0.7937151</span></span>
+<span id="cb2-584"><a href="#cb2-584" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1 -0.925 -1.600 0.5612728</span></span>
+<span id="cb2-585"><a href="#cb2-585" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 1 -0.875 -0.850 2.9230889</span></span>
+<span id="cb2-586"><a href="#cb2-586" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1 -0.550 -0.900 0.1761764</span></span>
+<span id="cb2-587"><a href="#cb2-587" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1 -1.550 -0.725 1.0340286</span></span>
+<span id="cb2-588"><a href="#cb2-588" aria-hidden="true" tabindex="-1"></a><span class="co">#> e_2 U_1 U_2</span></span>
+<span id="cb2-589"><a href="#cb2-589" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 0.09295071 -0.2285207 -0.25704929</span></span>
+<span id="cb2-590"><a href="#cb2-590" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 -0.18278050 -1.4687151 -1.68278050</span></span>
+<span id="cb2-591"><a href="#cb2-591" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 -0.24595450 -0.3637272 -1.84595450</span></span>
+<span id="cb2-592"><a href="#cb2-592" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 -0.74954312 2.0480889 -1.59954312</span></span>
+<span id="cb2-593"><a href="#cb2-593" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 -0.52864852 -0.3738236 -1.42864852</span></span>
+<span id="cb2-594"><a href="#cb2-594" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 0.69916199 -0.5159714 -0.02583801</span></span>
+<span id="cb2-595"><a href="#cb2-595" aria-hidden="true" tabindex="-1"></a><span class="co">#> CHOICE</span></span>
+<span id="cb2-596"><a href="#cb2-596" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1</span></span>
+<span id="cb2-597"><a href="#cb2-597" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1</span></span>
+<span id="cb2-598"><a href="#cb2-598" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1</span></span>
+<span id="cb2-599"><a href="#cb2-599" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 1</span></span>
+<span id="cb2-600"><a href="#cb2-600" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1</span></span>
+<span id="cb2-601"><a href="#cb2-601" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 2</span></span>
+<span id="cb2-602"><a href="#cb2-602" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-603"><a href="#cb2-603" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-604"><a href="#cb2-604" aria-hidden="true" tabindex="-1"></a><span class="co">#> This is the utility functions </span></span>
+<span id="cb2-605"><a href="#cb2-605" aria-hidden="true" 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="cb2-606"><a href="#cb2-606" aria-hidden="true" tabindex="-1"></a><span class="co">#> Initial gradient value:</span></span>
+<span id="cb2-607"><a href="#cb2-607" aria-hidden="true" tabindex="-1"></a><span class="co">#> bpreis blade bwarte </span></span>
+<span id="cb2-608"><a href="#cb2-608" aria-hidden="true" tabindex="-1"></a><span class="co">#> -2640.0 -1060.0 662.5 </span></span>
+<span id="cb2-609"><a href="#cb2-609" aria-hidden="true" tabindex="-1"></a><span class="co">#> initial value 998.131940 </span></span>
+<span id="cb2-610"><a href="#cb2-610" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 2 value 987.031183</span></span>
+<span id="cb2-611"><a href="#cb2-611" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 3 value 957.685378</span></span>
+<span id="cb2-612"><a href="#cb2-612" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 4 value 957.680370</span></span>
+<span id="cb2-613"><a href="#cb2-613" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 5 value 954.925156</span></span>
+<span id="cb2-614"><a href="#cb2-614" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 6 value 945.725076</span></span>
+<span id="cb2-615"><a href="#cb2-615" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 7 value 945.695285</span></span>
+<span id="cb2-616"><a href="#cb2-616" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 8 value 945.695175</span></span>
+<span id="cb2-617"><a href="#cb2-617" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 8 value 945.695175</span></span>
+<span id="cb2-618"><a href="#cb2-618" aria-hidden="true" tabindex="-1"></a><span class="co">#> final value 945.695175 </span></span>
+<span id="cb2-619"><a href="#cb2-619" aria-hidden="true" tabindex="-1"></a><span class="co">#> converged</span></span>
+<span id="cb2-620"><a href="#cb2-620" aria-hidden="true" tabindex="-1"></a><span class="co">#> This is Run number 2 </span></span>
+<span id="cb2-621"><a href="#cb2-621" aria-hidden="true" tabindex="-1"></a><span class="co">#> does sou_gis exist: FALSE </span></span>
+<span id="cb2-622"><a href="#cb2-622" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-623"><a href="#cb2-623" aria-hidden="true" tabindex="-1"></a><span class="co">#> dataset final_set exists: FALSE </span></span>
+<span id="cb2-624"><a href="#cb2-624" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-625"><a href="#cb2-625" aria-hidden="true" tabindex="-1"></a><span class="co">#> decisiongroups exists: TRUE</span></span>
+<span id="cb2-626"><a href="#cb2-626" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 2 </span></span>
+<span id="cb2-627"><a href="#cb2-627" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1007 433 </span></span>
+<span id="cb2-628"><a href="#cb2-628" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-629"><a href="#cb2-629" aria-hidden="true" tabindex="-1"></a><span class="co">#> data has been made </span></span>
+<span id="cb2-630"><a href="#cb2-630" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-631"><a href="#cb2-631" aria-hidden="true" tabindex="-1"></a><span class="co">#> First few observations </span></span>
+<span id="cb2-632"><a href="#cb2-632" aria-hidden="true" tabindex="-1"></a><span class="co">#> ID Choice_situation alt1_x1 alt1_x2</span></span>
+<span id="cb2-633"><a href="#cb2-633" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1 3 80 5.0</span></span>
+<span id="cb2-634"><a href="#cb2-634" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1 5 60 2.5</span></span>
+<span id="cb2-635"><a href="#cb2-635" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1 10 80 2.5</span></span>
+<span id="cb2-636"><a href="#cb2-636" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 1 34 80 2.5</span></span>
+<span id="cb2-637"><a href="#cb2-637" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1 37 40 5.0</span></span>
+<span id="cb2-638"><a href="#cb2-638" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1 39 20 20.0</span></span>
+<span id="cb2-639"><a href="#cb2-639" aria-hidden="true" tabindex="-1"></a><span class="co">#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block</span></span>
+<span id="cb2-640"><a href="#cb2-640" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 0.0 20 5.0 10.0 1</span></span>
+<span id="cb2-641"><a href="#cb2-641" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 5.0 20 20.0 5.0 1</span></span>
+<span id="cb2-642"><a href="#cb2-642" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 2.5 20 20.0 0.0 1</span></span>
+<span id="cb2-643"><a href="#cb2-643" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 5.0 60 5.0 5.0 1</span></span>
+<span id="cb2-644"><a href="#cb2-644" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 10.0 60 5.0 2.5 1</span></span>
+<span id="cb2-645"><a href="#cb2-645" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 2.5 60 2.5 2.5 1</span></span>
+<span id="cb2-646"><a href="#cb2-646" aria-hidden="true" tabindex="-1"></a><span class="co">#> group V_1 V_2 e_1</span></span>
+<span id="cb2-647"><a href="#cb2-647" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1 -1.150 -0.350 -0.8218428</span></span>
+<span id="cb2-648"><a href="#cb2-648" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1 -0.675 -1.500 0.4133131</span></span>
+<span id="cb2-649"><a href="#cb2-649" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1 -0.925 -1.600 0.4824588</span></span>
+<span id="cb2-650"><a href="#cb2-650" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 1 -0.875 -0.850 -1.2658097</span></span>
+<span id="cb2-651"><a href="#cb2-651" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1 -0.550 -0.900 -0.6930574</span></span>
+<span id="cb2-652"><a href="#cb2-652" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1 -1.550 -0.725 -0.6815915</span></span>
+<span id="cb2-653"><a href="#cb2-653" aria-hidden="true" tabindex="-1"></a><span class="co">#> e_2 U_1 U_2 CHOICE</span></span>
+<span id="cb2-654"><a href="#cb2-654" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 -0.6493651 -1.9718428 -0.9993651 2</span></span>
+<span id="cb2-655"><a href="#cb2-655" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 0.8461510 -0.2616869 -0.6538490 1</span></span>
+<span id="cb2-656"><a href="#cb2-656" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 0.3849732 -0.4425412 -1.2150268 1</span></span>
+<span id="cb2-657"><a href="#cb2-657" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 -0.2971578 -2.1408097 -1.1471578 2</span></span>
+<span id="cb2-658"><a href="#cb2-658" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 -0.8024491 -1.2430574 -1.7024491 1</span></span>
+<span id="cb2-659"><a href="#cb2-659" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 -0.4752339 -2.2315915 -1.2002339 2</span></span>
+<span id="cb2-660"><a href="#cb2-660" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-661"><a href="#cb2-661" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-662"><a href="#cb2-662" aria-hidden="true" tabindex="-1"></a><span class="co">#> This is the utility functions </span></span>
+<span id="cb2-663"><a href="#cb2-663" aria-hidden="true" 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="cb2-664"><a href="#cb2-664" aria-hidden="true" tabindex="-1"></a><span class="co">#> Initial gradient value:</span></span>
+<span id="cb2-665"><a href="#cb2-665" aria-hidden="true" tabindex="-1"></a><span class="co">#> bpreis blade bwarte </span></span>
+<span id="cb2-666"><a href="#cb2-666" aria-hidden="true" tabindex="-1"></a><span class="co">#> -1320.0 -1027.5 537.5 </span></span>
+<span id="cb2-667"><a href="#cb2-667" aria-hidden="true" tabindex="-1"></a><span class="co">#> initial value 998.131940 </span></span>
+<span id="cb2-668"><a href="#cb2-668" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 2 value 992.731937</span></span>
+<span id="cb2-669"><a href="#cb2-669" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 3 value 967.306984</span></span>
+<span id="cb2-670"><a href="#cb2-670" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 4 value 967.287995</span></span>
+<span id="cb2-671"><a href="#cb2-671" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 5 value 964.318376</span></span>
+<span id="cb2-672"><a href="#cb2-672" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 6 value 964.313823</span></span>
+<span id="cb2-673"><a href="#cb2-673" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 6 value 964.313820</span></span>
+<span id="cb2-674"><a href="#cb2-674" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 6 value 964.313820</span></span>
+<span id="cb2-675"><a href="#cb2-675" aria-hidden="true" tabindex="-1"></a><span class="co">#> final value 964.313820 </span></span>
+<span id="cb2-676"><a href="#cb2-676" aria-hidden="true" tabindex="-1"></a><span class="co">#> converged</span></span>
+<span id="cb2-677"><a href="#cb2-677" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-678"><a href="#cb2-678" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-679"><a href="#cb2-679" aria-hidden="true" tabindex="-1"></a><span class="co">#> ================ ==== === ===== ==== ===== ===== ===== ====</span></span>
+<span id="cb2-680"><a href="#cb2-680" aria-hidden="true" tabindex="-1"></a><span class="co">#> \ vars n mean sd min max range se</span></span>
+<span id="cb2-681"><a href="#cb2-681" aria-hidden="true" tabindex="-1"></a><span class="co">#> ================ ==== === ===== ==== ===== ===== ===== ====</span></span>
+<span id="cb2-682"><a href="#cb2-682" aria-hidden="true" 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="cb2-683"><a href="#cb2-683" aria-hidden="true" tabindex="-1"></a><span class="co">#> est_blade 2 2 -0.05 0.01 -0.06 -0.05 0.01 0.01</span></span>
+<span id="cb2-684"><a href="#cb2-684" aria-hidden="true" tabindex="-1"></a><span class="co">#> est_bwarte 3 2 0.02 0.00 0.02 0.02 0.00 0.00</span></span>
+<span id="cb2-685"><a href="#cb2-685" aria-hidden="true" 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="cb2-686"><a href="#cb2-686" aria-hidden="true" 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="cb2-687"><a href="#cb2-687" aria-hidden="true" tabindex="-1"></a><span class="co">#> rob_pval0_bwarte 6 2 0.06 0.01 0.06 0.07 0.01 0.01</span></span>
+<span id="cb2-688"><a href="#cb2-688" aria-hidden="true" tabindex="-1"></a><span class="co">#> ================ ==== === ===== ==== ===== ===== ===== ====</span></span>
+<span id="cb2-689"><a href="#cb2-689" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-690"><a href="#cb2-690" aria-hidden="true" tabindex="-1"></a><span class="co">#> FALSE </span></span>
+<span id="cb2-691"><a href="#cb2-691" aria-hidden="true" tabindex="-1"></a><span class="co">#> 100 </span></span>
+<span id="cb2-692"><a href="#cb2-692" aria-hidden="true" tabindex="-1"></a><span class="co">#> Utility function used in simulation, ie the true utility: </span></span>
+<span id="cb2-693"><a href="#cb2-693" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-694"><a href="#cb2-694" aria-hidden="true" tabindex="-1"></a><span class="co">#> $u1</span></span>
+<span id="cb2-695"><a href="#cb2-695" aria-hidden="true" tabindex="-1"></a><span class="co">#> $u1$v1</span></span>
+<span id="cb2-696"><a href="#cb2-696" aria-hidden="true" tabindex="-1"></a><span class="co">#> V.1 ~ bpreis * alt1.x1 + blade * alt1.x2 + bwarte * alt1.x3</span></span>
+<span id="cb2-697"><a href="#cb2-697" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-698"><a href="#cb2-698" aria-hidden="true" tabindex="-1"></a><span class="co">#> $u1$v2</span></span>
+<span id="cb2-699"><a href="#cb2-699" aria-hidden="true" tabindex="-1"></a><span class="co">#> V.2 ~ bpreis * alt2.x1 + blade * alt2.x2 + bwarte * alt2.x3</span></span>
+<span id="cb2-700"><a href="#cb2-700" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-701"><a href="#cb2-701" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-702"><a href="#cb2-702" aria-hidden="true" tabindex="-1"></a><span class="co">#> $u2</span></span>
+<span id="cb2-703"><a href="#cb2-703" aria-hidden="true" tabindex="-1"></a><span class="co">#> $u2$v1</span></span>
+<span id="cb2-704"><a href="#cb2-704" aria-hidden="true" tabindex="-1"></a><span class="co">#> V.1 ~ bpreis * alt1.x1</span></span>
+<span id="cb2-705"><a href="#cb2-705" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-706"><a href="#cb2-706" aria-hidden="true" tabindex="-1"></a><span class="co">#> $u2$v2</span></span>
+<span id="cb2-707"><a href="#cb2-707" aria-hidden="true" tabindex="-1"></a><span class="co">#> V.2 ~ bpreis * alt2.x1</span></span>
+<span id="cb2-708"><a href="#cb2-708" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-709"><a href="#cb2-709" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-710"><a href="#cb2-710" aria-hidden="true" tabindex="-1"></a><span class="co">#> Utility function used for Logit estimation with mixl: </span></span>
+<span id="cb2-711"><a href="#cb2-711" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-712"><a href="#cb2-712" aria-hidden="true" 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="cb2-713"><a href="#cb2-713" aria-hidden="true" tabindex="-1"></a><span class="co">#> New names:</span></span>
+<span id="cb2-714"><a href="#cb2-714" aria-hidden="true" tabindex="-1"></a><span class="co">#> • `Choice situation` -></span></span>
+<span id="cb2-715"><a href="#cb2-715" aria-hidden="true" tabindex="-1"></a><span class="co">#> `Choice.situation`</span></span>
+<span id="cb2-716"><a href="#cb2-716" aria-hidden="true" tabindex="-1"></a><span class="co">#> • `` -> `...10`</span></span>
+<span id="cb2-717"><a href="#cb2-717" aria-hidden="true" tabindex="-1"></a><span class="co">#> Warning: One or more parsing issues, call</span></span>
+<span id="cb2-718"><a href="#cb2-718" aria-hidden="true" tabindex="-1"></a><span class="co">#> `problems()` on your data frame for</span></span>
+<span id="cb2-719"><a href="#cb2-719" aria-hidden="true" tabindex="-1"></a><span class="co">#> details, e.g.:</span></span>
+<span id="cb2-720"><a href="#cb2-720" aria-hidden="true" tabindex="-1"></a><span class="co">#> dat <- vroom(...)</span></span>
+<span id="cb2-721"><a href="#cb2-721" aria-hidden="true" tabindex="-1"></a><span class="co">#> problems(dat)</span></span>
+<span id="cb2-722"><a href="#cb2-722" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-723"><a href="#cb2-723" aria-hidden="true" tabindex="-1"></a><span class="co">#> does sou_gis exist: FALSE </span></span>
+<span id="cb2-724"><a href="#cb2-724" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-725"><a href="#cb2-725" aria-hidden="true" tabindex="-1"></a><span class="co">#> dataset final_set exists: FALSE </span></span>
+<span id="cb2-726"><a href="#cb2-726" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-727"><a href="#cb2-727" aria-hidden="true" tabindex="-1"></a><span class="co">#> decisiongroups exists: TRUE</span></span>
+<span id="cb2-728"><a href="#cb2-728" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 2 </span></span>
+<span id="cb2-729"><a href="#cb2-729" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1007 433 </span></span>
+<span id="cb2-730"><a href="#cb2-730" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-731"><a href="#cb2-731" aria-hidden="true" tabindex="-1"></a><span class="co">#> data has been made </span></span>
+<span id="cb2-732"><a href="#cb2-732" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-733"><a href="#cb2-733" aria-hidden="true" tabindex="-1"></a><span class="co">#> First few observations </span></span>
+<span id="cb2-734"><a href="#cb2-734" aria-hidden="true" tabindex="-1"></a><span class="co">#> ID Choice_situation alt1_x1 alt1_x2</span></span>
+<span id="cb2-735"><a href="#cb2-735" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1 9 80 5.0</span></span>
+<span id="cb2-736"><a href="#cb2-736" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1 12 60 2.5</span></span>
+<span id="cb2-737"><a href="#cb2-737" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1 13 20 20.0</span></span>
+<span id="cb2-738"><a href="#cb2-738" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 1 70 80 5.0</span></span>
+<span id="cb2-739"><a href="#cb2-739" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1 71 60 20.0</span></span>
+<span id="cb2-740"><a href="#cb2-740" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1 73 60 10.0</span></span>
+<span id="cb2-741"><a href="#cb2-741" aria-hidden="true" tabindex="-1"></a><span class="co">#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block</span></span>
+<span id="cb2-742"><a href="#cb2-742" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 0 60 20.0 10.0 1</span></span>
+<span id="cb2-743"><a href="#cb2-743" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 10 40 20.0 0.0 1</span></span>
+<span id="cb2-744"><a href="#cb2-744" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 10 80 2.5 0.0 1</span></span>
+<span id="cb2-745"><a href="#cb2-745" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 10 20 20.0 2.5 1</span></span>
+<span id="cb2-746"><a href="#cb2-746" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 10 80 10.0 0.0 1</span></span>
+<span id="cb2-747"><a href="#cb2-747" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 0 40 20.0 10.0 1</span></span>
+<span id="cb2-748"><a href="#cb2-748" aria-hidden="true" tabindex="-1"></a><span class="co">#> group V_1 V_2 e_1</span></span>
+<span id="cb2-749"><a href="#cb2-749" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1 -1.150 -1.800 0.4772651</span></span>
+<span id="cb2-750"><a href="#cb2-750" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1 -0.575 -1.800 -1.0611813</span></span>
+<span id="cb2-751"><a href="#cb2-751" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1 -1.400 -0.975 -0.4549814</span></span>
+<span id="cb2-752"><a href="#cb2-752" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 1 -0.950 -1.550 1.0741179</span></span>
+<span id="cb2-753"><a href="#cb2-753" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1 -1.800 -1.500 0.6850764</span></span>
+<span id="cb2-754"><a href="#cb2-754" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1 -1.300 -1.600 2.1581413</span></span>
+<span id="cb2-755"><a href="#cb2-755" aria-hidden="true" tabindex="-1"></a><span class="co">#> e_2 U_1 U_2</span></span>
+<span id="cb2-756"><a href="#cb2-756" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 -0.58862455 -0.6727349 -2.3886245</span></span>
+<span id="cb2-757"><a href="#cb2-757" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1.67391615 -1.6361813 -0.1260839</span></span>
+<span id="cb2-758"><a href="#cb2-758" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 0.08433351 -1.8549814 -0.8906665</span></span>
+<span id="cb2-759"><a href="#cb2-759" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 0.16471135 0.1241179 -1.3852887</span></span>
+<span id="cb2-760"><a href="#cb2-760" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 -0.80503749 -1.1149236 -2.3050375</span></span>
+<span id="cb2-761"><a href="#cb2-761" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 -0.78193942 0.8581413 -2.3819394</span></span>
+<span id="cb2-762"><a href="#cb2-762" aria-hidden="true" tabindex="-1"></a><span class="co">#> CHOICE</span></span>
+<span id="cb2-763"><a href="#cb2-763" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1</span></span>
+<span id="cb2-764"><a href="#cb2-764" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 2</span></span>
+<span id="cb2-765"><a href="#cb2-765" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 2</span></span>
+<span id="cb2-766"><a href="#cb2-766" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 1</span></span>
+<span id="cb2-767"><a href="#cb2-767" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1</span></span>
+<span id="cb2-768"><a href="#cb2-768" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1</span></span>
+<span id="cb2-769"><a href="#cb2-769" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-770"><a href="#cb2-770" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-771"><a href="#cb2-771" aria-hidden="true" tabindex="-1"></a><span class="co">#> This is Run number 1 </span></span>
+<span id="cb2-772"><a href="#cb2-772" aria-hidden="true" tabindex="-1"></a><span class="co">#> does sou_gis exist: FALSE </span></span>
+<span id="cb2-773"><a href="#cb2-773" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-774"><a href="#cb2-774" aria-hidden="true" tabindex="-1"></a><span class="co">#> dataset final_set exists: FALSE </span></span>
+<span id="cb2-775"><a href="#cb2-775" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-776"><a href="#cb2-776" aria-hidden="true" tabindex="-1"></a><span class="co">#> decisiongroups exists: TRUE</span></span>
+<span id="cb2-777"><a href="#cb2-777" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 2 </span></span>
+<span id="cb2-778"><a href="#cb2-778" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1007 433 </span></span>
+<span id="cb2-779"><a href="#cb2-779" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-780"><a href="#cb2-780" aria-hidden="true" tabindex="-1"></a><span class="co">#> data has been made </span></span>
+<span id="cb2-781"><a href="#cb2-781" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-782"><a href="#cb2-782" aria-hidden="true" tabindex="-1"></a><span class="co">#> First few observations </span></span>
+<span id="cb2-783"><a href="#cb2-783" aria-hidden="true" tabindex="-1"></a><span class="co">#> ID Choice_situation alt1_x1 alt1_x2</span></span>
+<span id="cb2-784"><a href="#cb2-784" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1 9 80 5.0</span></span>
+<span id="cb2-785"><a href="#cb2-785" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1 12 60 2.5</span></span>
+<span id="cb2-786"><a href="#cb2-786" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1 13 20 20.0</span></span>
+<span id="cb2-787"><a href="#cb2-787" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 1 70 80 5.0</span></span>
+<span id="cb2-788"><a href="#cb2-788" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1 71 60 20.0</span></span>
+<span id="cb2-789"><a href="#cb2-789" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1 73 60 10.0</span></span>
+<span id="cb2-790"><a href="#cb2-790" aria-hidden="true" tabindex="-1"></a><span class="co">#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block</span></span>
+<span id="cb2-791"><a href="#cb2-791" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 0 60 20.0 10.0 1</span></span>
+<span id="cb2-792"><a href="#cb2-792" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 10 40 20.0 0.0 1</span></span>
+<span id="cb2-793"><a href="#cb2-793" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 10 80 2.5 0.0 1</span></span>
+<span id="cb2-794"><a href="#cb2-794" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 10 20 20.0 2.5 1</span></span>
+<span id="cb2-795"><a href="#cb2-795" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 10 80 10.0 0.0 1</span></span>
+<span id="cb2-796"><a href="#cb2-796" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 0 40 20.0 10.0 1</span></span>
+<span id="cb2-797"><a href="#cb2-797" aria-hidden="true" tabindex="-1"></a><span class="co">#> group V_1 V_2 e_1</span></span>
+<span id="cb2-798"><a href="#cb2-798" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1 -1.150 -1.800 -0.284096565</span></span>
+<span id="cb2-799"><a href="#cb2-799" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1 -0.575 -1.800 -0.020855208</span></span>
+<span id="cb2-800"><a href="#cb2-800" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1 -1.400 -0.975 2.808193631</span></span>
+<span id="cb2-801"><a href="#cb2-801" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 1 -0.950 -1.550 1.512635398</span></span>
+<span id="cb2-802"><a href="#cb2-802" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1 -1.800 -1.500 -0.869856696</span></span>
+<span id="cb2-803"><a href="#cb2-803" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1 -1.300 -1.600 0.001496538</span></span>
+<span id="cb2-804"><a href="#cb2-804" aria-hidden="true" tabindex="-1"></a><span class="co">#> e_2 U_1 U_2 CHOICE</span></span>
+<span id="cb2-805"><a href="#cb2-805" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 3.7852439 -1.4340966 1.9852439 2</span></span>
+<span id="cb2-806"><a href="#cb2-806" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 2.5441347 -0.5958552 0.7441347 2</span></span>
+<span id="cb2-807"><a href="#cb2-807" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 -0.1408644 1.4081936 -1.1158644 1</span></span>
+<span id="cb2-808"><a href="#cb2-808" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 -0.2739250 0.5626354 -1.8239250 1</span></span>
+<span id="cb2-809"><a href="#cb2-809" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 -0.2920285 -2.6698567 -1.7920285 2</span></span>
+<span id="cb2-810"><a href="#cb2-810" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 0.9243727 -1.2985035 -0.6756273 2</span></span>
+<span id="cb2-811"><a href="#cb2-811" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-812"><a href="#cb2-812" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-813"><a href="#cb2-813" aria-hidden="true" tabindex="-1"></a><span class="co">#> This is the utility functions </span></span>
+<span id="cb2-814"><a href="#cb2-814" aria-hidden="true" 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="cb2-815"><a href="#cb2-815" aria-hidden="true" tabindex="-1"></a><span class="co">#> Initial gradient value:</span></span>
+<span id="cb2-816"><a href="#cb2-816" aria-hidden="true" tabindex="-1"></a><span class="co">#> bpreis blade bwarte </span></span>
+<span id="cb2-817"><a href="#cb2-817" aria-hidden="true" tabindex="-1"></a><span class="co">#> -2400 -3680 1320 </span></span>
+<span id="cb2-818"><a href="#cb2-818" aria-hidden="true" tabindex="-1"></a><span class="co">#> initial value 998.131940 </span></span>
+<span id="cb2-819"><a href="#cb2-819" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 2 value 956.785003</span></span>
+<span id="cb2-820"><a href="#cb2-820" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 3 value 912.039295</span></span>
+<span id="cb2-821"><a href="#cb2-821" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 4 value 911.870417</span></span>
+<span id="cb2-822"><a href="#cb2-822" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 5 value 885.881709</span></span>
+<span id="cb2-823"><a href="#cb2-823" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 6 value 885.187568</span></span>
+<span id="cb2-824"><a href="#cb2-824" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 7 value 885.171492</span></span>
+<span id="cb2-825"><a href="#cb2-825" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 8 value 885.171476</span></span>
+<span id="cb2-826"><a href="#cb2-826" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 8 value 885.171476</span></span>
+<span id="cb2-827"><a href="#cb2-827" aria-hidden="true" tabindex="-1"></a><span class="co">#> final value 885.171476 </span></span>
+<span id="cb2-828"><a href="#cb2-828" aria-hidden="true" tabindex="-1"></a><span class="co">#> converged</span></span>
+<span id="cb2-829"><a href="#cb2-829" aria-hidden="true" tabindex="-1"></a><span class="co">#> This is Run number 2 </span></span>
+<span id="cb2-830"><a href="#cb2-830" aria-hidden="true" tabindex="-1"></a><span class="co">#> does sou_gis exist: FALSE </span></span>
+<span id="cb2-831"><a href="#cb2-831" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-832"><a href="#cb2-832" aria-hidden="true" tabindex="-1"></a><span class="co">#> dataset final_set exists: FALSE </span></span>
+<span id="cb2-833"><a href="#cb2-833" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-834"><a href="#cb2-834" aria-hidden="true" tabindex="-1"></a><span class="co">#> decisiongroups exists: TRUE</span></span>
+<span id="cb2-835"><a href="#cb2-835" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 2 </span></span>
+<span id="cb2-836"><a href="#cb2-836" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1007 433 </span></span>
+<span id="cb2-837"><a href="#cb2-837" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-838"><a href="#cb2-838" aria-hidden="true" tabindex="-1"></a><span class="co">#> data has been made </span></span>
+<span id="cb2-839"><a href="#cb2-839" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-840"><a href="#cb2-840" aria-hidden="true" tabindex="-1"></a><span class="co">#> First few observations </span></span>
+<span id="cb2-841"><a href="#cb2-841" aria-hidden="true" tabindex="-1"></a><span class="co">#> ID Choice_situation alt1_x1 alt1_x2</span></span>
+<span id="cb2-842"><a href="#cb2-842" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1 9 80 5.0</span></span>
+<span id="cb2-843"><a href="#cb2-843" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1 12 60 2.5</span></span>
+<span id="cb2-844"><a href="#cb2-844" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1 13 20 20.0</span></span>
+<span id="cb2-845"><a href="#cb2-845" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 1 70 80 5.0</span></span>
+<span id="cb2-846"><a href="#cb2-846" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1 71 60 20.0</span></span>
+<span id="cb2-847"><a href="#cb2-847" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1 73 60 10.0</span></span>
+<span id="cb2-848"><a href="#cb2-848" aria-hidden="true" tabindex="-1"></a><span class="co">#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block</span></span>
+<span id="cb2-849"><a href="#cb2-849" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 0 60 20.0 10.0 1</span></span>
+<span id="cb2-850"><a href="#cb2-850" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 10 40 20.0 0.0 1</span></span>
+<span id="cb2-851"><a href="#cb2-851" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 10 80 2.5 0.0 1</span></span>
+<span id="cb2-852"><a href="#cb2-852" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 10 20 20.0 2.5 1</span></span>
+<span id="cb2-853"><a href="#cb2-853" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 10 80 10.0 0.0 1</span></span>
+<span id="cb2-854"><a href="#cb2-854" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 0 40 20.0 10.0 1</span></span>
+<span id="cb2-855"><a href="#cb2-855" aria-hidden="true" tabindex="-1"></a><span class="co">#> group V_1 V_2 e_1</span></span>
+<span id="cb2-856"><a href="#cb2-856" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1 -1.150 -1.800 0.6645192</span></span>
+<span id="cb2-857"><a href="#cb2-857" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1 -0.575 -1.800 -0.8450051</span></span>
+<span id="cb2-858"><a href="#cb2-858" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1 -1.400 -0.975 0.1125148</span></span>
+<span id="cb2-859"><a href="#cb2-859" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 1 -0.950 -1.550 1.0543183</span></span>
+<span id="cb2-860"><a href="#cb2-860" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1 -1.800 -1.500 1.1168013</span></span>
+<span id="cb2-861"><a href="#cb2-861" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1 -1.300 -1.600 -0.1311416</span></span>
+<span id="cb2-862"><a href="#cb2-862" aria-hidden="true" tabindex="-1"></a><span class="co">#> e_2 U_1 U_2 CHOICE</span></span>
+<span id="cb2-863"><a href="#cb2-863" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 2.3304233 -0.4854808 0.5304233 2</span></span>
+<span id="cb2-864"><a href="#cb2-864" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 0.2022020 -1.4200051 -1.5977980 1</span></span>
+<span id="cb2-865"><a href="#cb2-865" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 -0.1148274 -1.2874852 -1.0898274 2</span></span>
+<span id="cb2-866"><a href="#cb2-866" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 -1.3880265 0.1043183 -2.9380265 1</span></span>
+<span id="cb2-867"><a href="#cb2-867" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 0.1356148 -0.6831987 -1.3643852 1</span></span>
+<span id="cb2-868"><a href="#cb2-868" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 0.9455601 -1.4311416 -0.6544399 2</span></span>
+<span id="cb2-869"><a href="#cb2-869" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-870"><a href="#cb2-870" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-871"><a href="#cb2-871" aria-hidden="true" tabindex="-1"></a><span class="co">#> This is the utility functions </span></span>
+<span id="cb2-872"><a href="#cb2-872" aria-hidden="true" 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="cb2-873"><a href="#cb2-873" aria-hidden="true" tabindex="-1"></a><span class="co">#> Initial gradient value:</span></span>
+<span id="cb2-874"><a href="#cb2-874" aria-hidden="true" tabindex="-1"></a><span class="co">#> bpreis blade bwarte </span></span>
+<span id="cb2-875"><a href="#cb2-875" aria-hidden="true" tabindex="-1"></a><span class="co">#> -3200.0 -2932.5 1142.5 </span></span>
+<span id="cb2-876"><a href="#cb2-876" aria-hidden="true" tabindex="-1"></a><span class="co">#> initial value 998.131940 </span></span>
+<span id="cb2-877"><a href="#cb2-877" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 2 value 965.989359</span></span>
+<span id="cb2-878"><a href="#cb2-878" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 3 value 962.943975</span></span>
+<span id="cb2-879"><a href="#cb2-879" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 4 value 962.790350</span></span>
+<span id="cb2-880"><a href="#cb2-880" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 5 value 915.909913</span></span>
+<span id="cb2-881"><a href="#cb2-881" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 6 value 915.781694</span></span>
+<span id="cb2-882"><a href="#cb2-882" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 7 value 915.780836</span></span>
+<span id="cb2-883"><a href="#cb2-883" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 7 value 915.780833</span></span>
+<span id="cb2-884"><a href="#cb2-884" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 7 value 915.780833</span></span>
+<span id="cb2-885"><a href="#cb2-885" aria-hidden="true" tabindex="-1"></a><span class="co">#> final value 915.780833 </span></span>
+<span id="cb2-886"><a href="#cb2-886" aria-hidden="true" tabindex="-1"></a><span class="co">#> converged</span></span>
+<span id="cb2-887"><a href="#cb2-887" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-888"><a href="#cb2-888" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-889"><a href="#cb2-889" aria-hidden="true" tabindex="-1"></a><span class="co">#> ================ ==== === ===== ==== ===== ===== ===== ====</span></span>
+<span id="cb2-890"><a href="#cb2-890" aria-hidden="true" tabindex="-1"></a><span class="co">#> \ vars n mean sd min max range se</span></span>
+<span id="cb2-891"><a href="#cb2-891" aria-hidden="true" tabindex="-1"></a><span class="co">#> ================ ==== === ===== ==== ===== ===== ===== ====</span></span>
+<span id="cb2-892"><a href="#cb2-892" aria-hidden="true" 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="cb2-893"><a href="#cb2-893" aria-hidden="true" tabindex="-1"></a><span class="co">#> est_blade 2 2 -0.05 0.01 -0.05 -0.04 0.01 0.00</span></span>
+<span id="cb2-894"><a href="#cb2-894" aria-hidden="true" tabindex="-1"></a><span class="co">#> est_bwarte 3 2 0.02 0.00 0.02 0.02 0.00 0.00</span></span>
+<span id="cb2-895"><a href="#cb2-895" aria-hidden="true" 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="cb2-896"><a href="#cb2-896" aria-hidden="true" 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="cb2-897"><a href="#cb2-897" aria-hidden="true" tabindex="-1"></a><span class="co">#> rob_pval0_bwarte 6 2 0.01 0.02 0.00 0.03 0.03 0.01</span></span>
+<span id="cb2-898"><a href="#cb2-898" aria-hidden="true" tabindex="-1"></a><span class="co">#> ================ ==== === ===== ==== ===== ===== ===== ====</span></span>
+<span id="cb2-899"><a href="#cb2-899" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-900"><a href="#cb2-900" aria-hidden="true" tabindex="-1"></a><span class="co">#> TRUE </span></span>
+<span id="cb2-901"><a href="#cb2-901" aria-hidden="true" tabindex="-1"></a><span class="co">#> 100 </span></span>
+<span id="cb2-902"><a href="#cb2-902" aria-hidden="true" tabindex="-1"></a><span class="co">#> Utility function used in simulation, ie the true utility: </span></span>
+<span id="cb2-903"><a href="#cb2-903" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-904"><a href="#cb2-904" aria-hidden="true" tabindex="-1"></a><span class="co">#> $u1</span></span>
+<span id="cb2-905"><a href="#cb2-905" aria-hidden="true" tabindex="-1"></a><span class="co">#> $u1$v1</span></span>
+<span id="cb2-906"><a href="#cb2-906" aria-hidden="true" tabindex="-1"></a><span class="co">#> V.1 ~ bpreis * alt1.x1 + blade * alt1.x2 + bwarte * alt1.x3</span></span>
+<span id="cb2-907"><a href="#cb2-907" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-908"><a href="#cb2-908" aria-hidden="true" tabindex="-1"></a><span class="co">#> $u1$v2</span></span>
+<span id="cb2-909"><a href="#cb2-909" aria-hidden="true" tabindex="-1"></a><span class="co">#> V.2 ~ bpreis * alt2.x1 + blade * alt2.x2 + bwarte * alt2.x3</span></span>
+<span id="cb2-910"><a href="#cb2-910" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-911"><a href="#cb2-911" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-912"><a href="#cb2-912" aria-hidden="true" tabindex="-1"></a><span class="co">#> $u2</span></span>
+<span id="cb2-913"><a href="#cb2-913" aria-hidden="true" tabindex="-1"></a><span class="co">#> $u2$v1</span></span>
+<span id="cb2-914"><a href="#cb2-914" aria-hidden="true" tabindex="-1"></a><span class="co">#> V.1 ~ bpreis * alt1.x1</span></span>
+<span id="cb2-915"><a href="#cb2-915" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-916"><a href="#cb2-916" aria-hidden="true" tabindex="-1"></a><span class="co">#> $u2$v2</span></span>
+<span id="cb2-917"><a href="#cb2-917" aria-hidden="true" tabindex="-1"></a><span class="co">#> V.2 ~ bpreis * alt2.x1</span></span>
+<span id="cb2-918"><a href="#cb2-918" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-919"><a href="#cb2-919" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-920"><a href="#cb2-920" aria-hidden="true" tabindex="-1"></a><span class="co">#> Utility function used for Logit estimation with mixl: </span></span>
+<span id="cb2-921"><a href="#cb2-921" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-922"><a href="#cb2-922" aria-hidden="true" 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="cb2-923"><a href="#cb2-923" aria-hidden="true" tabindex="-1"></a><span class="co">#> New names:</span></span>
+<span id="cb2-924"><a href="#cb2-924" aria-hidden="true" tabindex="-1"></a><span class="co">#> • `Choice situation` -></span></span>
+<span id="cb2-925"><a href="#cb2-925" aria-hidden="true" tabindex="-1"></a><span class="co">#> `Choice.situation`</span></span>
+<span id="cb2-926"><a href="#cb2-926" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-927"><a href="#cb2-927" aria-hidden="true" tabindex="-1"></a><span class="co">#> does sou_gis exist: FALSE </span></span>
+<span id="cb2-928"><a href="#cb2-928" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-929"><a href="#cb2-929" aria-hidden="true" tabindex="-1"></a><span class="co">#> dataset final_set exists: FALSE </span></span>
+<span id="cb2-930"><a href="#cb2-930" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-931"><a href="#cb2-931" aria-hidden="true" tabindex="-1"></a><span class="co">#> decisiongroups exists: TRUE</span></span>
+<span id="cb2-932"><a href="#cb2-932" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 2 </span></span>
+<span id="cb2-933"><a href="#cb2-933" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1007 433 </span></span>
+<span id="cb2-934"><a href="#cb2-934" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-935"><a href="#cb2-935" aria-hidden="true" tabindex="-1"></a><span class="co">#> data has been made </span></span>
+<span id="cb2-936"><a href="#cb2-936" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-937"><a href="#cb2-937" aria-hidden="true" tabindex="-1"></a><span class="co">#> First few observations </span></span>
+<span id="cb2-938"><a href="#cb2-938" aria-hidden="true" tabindex="-1"></a><span class="co">#> ID Choice_situation Block alt1_x1</span></span>
+<span id="cb2-939"><a href="#cb2-939" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1 1 1 80</span></span>
+<span id="cb2-940"><a href="#cb2-940" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1 2 1 60</span></span>
+<span id="cb2-941"><a href="#cb2-941" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1 3 1 60</span></span>
+<span id="cb2-942"><a href="#cb2-942" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 1 4 1 20</span></span>
+<span id="cb2-943"><a href="#cb2-943" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1 5 1 40</span></span>
+<span id="cb2-944"><a href="#cb2-944" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1 6 1 60</span></span>
+<span id="cb2-945"><a href="#cb2-945" aria-hidden="true" tabindex="-1"></a><span class="co">#> alt2_x1 alt1_x2 alt2_x2 alt1_x3 alt2_x3</span></span>
+<span id="cb2-946"><a href="#cb2-946" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 20 2.5 20.0 10 5</span></span>
+<span id="cb2-947"><a href="#cb2-947" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 40 5.0 10.0 5 10</span></span>
+<span id="cb2-948"><a href="#cb2-948" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 20 20.0 20.0 0 10</span></span>
+<span id="cb2-949"><a href="#cb2-949" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 80 20.0 2.5 0 10</span></span>
+<span id="cb2-950"><a href="#cb2-950" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 80 10.0 5.0 10 5</span></span>
+<span id="cb2-951"><a href="#cb2-951" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 80 5.0 2.5 0 0</span></span>
+<span id="cb2-952"><a href="#cb2-952" aria-hidden="true" tabindex="-1"></a><span class="co">#> group V_1 V_2 e_1</span></span>
+<span id="cb2-953"><a href="#cb2-953" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1 -0.775 -1.500 0.53504757</span></span>
+<span id="cb2-954"><a href="#cb2-954" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1 -0.850 -0.900 -0.93293876</span></span>
+<span id="cb2-955"><a href="#cb2-955" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1 -2.000 -1.400 -1.97083982</span></span>
+<span id="cb2-956"><a href="#cb2-956" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 1 -1.600 -0.775 -0.09847358</span></span>
+<span id="cb2-957"><a href="#cb2-957" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1 -0.900 -1.050 -0.91059496</span></span>
+<span id="cb2-958"><a href="#cb2-958" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1 -0.950 -0.975 -0.27261150</span></span>
+<span id="cb2-959"><a href="#cb2-959" aria-hidden="true" tabindex="-1"></a><span class="co">#> e_2 U_1 U_2 CHOICE</span></span>
+<span id="cb2-960"><a href="#cb2-960" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 0.9131705 -0.2399524 -0.5868295 1</span></span>
+<span id="cb2-961"><a href="#cb2-961" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 -1.5528907 -1.7829388 -2.4528907 1</span></span>
+<span id="cb2-962"><a href="#cb2-962" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 -0.2159494 -3.9708398 -1.6159494 2</span></span>
+<span id="cb2-963"><a href="#cb2-963" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 0.1685500 -1.6984736 -0.6064500 2</span></span>
+<span id="cb2-964"><a href="#cb2-964" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1.6256604 -1.8105950 0.5756604 2</span></span>
+<span id="cb2-965"><a href="#cb2-965" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1.5055143 -1.2226115 0.5305143 2</span></span>
+<span id="cb2-966"><a href="#cb2-966" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-967"><a href="#cb2-967" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-968"><a href="#cb2-968" aria-hidden="true" tabindex="-1"></a><span class="co">#> This is Run number 1 </span></span>
+<span id="cb2-969"><a href="#cb2-969" aria-hidden="true" tabindex="-1"></a><span class="co">#> does sou_gis exist: FALSE </span></span>
+<span id="cb2-970"><a href="#cb2-970" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-971"><a href="#cb2-971" aria-hidden="true" tabindex="-1"></a><span class="co">#> dataset final_set exists: FALSE </span></span>
+<span id="cb2-972"><a href="#cb2-972" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-973"><a href="#cb2-973" aria-hidden="true" tabindex="-1"></a><span class="co">#> decisiongroups exists: TRUE</span></span>
+<span id="cb2-974"><a href="#cb2-974" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 2 </span></span>
+<span id="cb2-975"><a href="#cb2-975" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1007 433 </span></span>
+<span id="cb2-976"><a href="#cb2-976" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-977"><a href="#cb2-977" aria-hidden="true" tabindex="-1"></a><span class="co">#> data has been made </span></span>
+<span id="cb2-978"><a href="#cb2-978" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-979"><a href="#cb2-979" aria-hidden="true" tabindex="-1"></a><span class="co">#> First few observations </span></span>
+<span id="cb2-980"><a href="#cb2-980" aria-hidden="true" tabindex="-1"></a><span class="co">#> ID Choice_situation Block alt1_x1</span></span>
+<span id="cb2-981"><a href="#cb2-981" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1 1 1 80</span></span>
+<span id="cb2-982"><a href="#cb2-982" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1 2 1 60</span></span>
+<span id="cb2-983"><a href="#cb2-983" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1 3 1 60</span></span>
+<span id="cb2-984"><a href="#cb2-984" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 1 4 1 20</span></span>
+<span id="cb2-985"><a href="#cb2-985" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1 5 1 40</span></span>
+<span id="cb2-986"><a href="#cb2-986" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1 6 1 60</span></span>
+<span id="cb2-987"><a href="#cb2-987" aria-hidden="true" tabindex="-1"></a><span class="co">#> alt2_x1 alt1_x2 alt2_x2 alt1_x3 alt2_x3</span></span>
+<span id="cb2-988"><a href="#cb2-988" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 20 2.5 20.0 10 5</span></span>
+<span id="cb2-989"><a href="#cb2-989" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 40 5.0 10.0 5 10</span></span>
+<span id="cb2-990"><a href="#cb2-990" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 20 20.0 20.0 0 10</span></span>
+<span id="cb2-991"><a href="#cb2-991" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 80 20.0 2.5 0 10</span></span>
+<span id="cb2-992"><a href="#cb2-992" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 80 10.0 5.0 10 5</span></span>
+<span id="cb2-993"><a href="#cb2-993" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 80 5.0 2.5 0 0</span></span>
+<span id="cb2-994"><a href="#cb2-994" aria-hidden="true" tabindex="-1"></a><span class="co">#> group V_1 V_2 e_1</span></span>
+<span id="cb2-995"><a href="#cb2-995" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1 -0.775 -1.500 -0.2361754</span></span>
+<span id="cb2-996"><a href="#cb2-996" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1 -0.850 -0.900 1.2985628</span></span>
+<span id="cb2-997"><a href="#cb2-997" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1 -2.000 -1.400 2.6517108</span></span>
+<span id="cb2-998"><a href="#cb2-998" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 1 -1.600 -0.775 -0.3215271</span></span>
+<span id="cb2-999"><a href="#cb2-999" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1 -0.900 -1.050 -1.1880836</span></span>
+<span id="cb2-1000"><a href="#cb2-1000" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1 -0.950 -0.975 0.9386790</span></span>
+<span id="cb2-1001"><a href="#cb2-1001" aria-hidden="true" tabindex="-1"></a><span class="co">#> e_2 U_1 U_2</span></span>
+<span id="cb2-1002"><a href="#cb2-1002" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 -0.2249671 -1.01117540 -1.72496708</span></span>
+<span id="cb2-1003"><a href="#cb2-1003" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 0.4231642 0.44856278 -0.47683584</span></span>
+<span id="cb2-1004"><a href="#cb2-1004" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 0.4632492 0.65171082 -0.93675077</span></span>
+<span id="cb2-1005"><a href="#cb2-1005" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 0.6960098 -1.92152712 -0.07899021</span></span>
+<span id="cb2-1006"><a href="#cb2-1006" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1.0360301 -2.08808358 -0.01396992</span></span>
+<span id="cb2-1007"><a href="#cb2-1007" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 -0.1024565 -0.01132103 -1.07745654</span></span>
+<span id="cb2-1008"><a href="#cb2-1008" aria-hidden="true" tabindex="-1"></a><span class="co">#> CHOICE</span></span>
+<span id="cb2-1009"><a href="#cb2-1009" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1</span></span>
+<span id="cb2-1010"><a href="#cb2-1010" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1</span></span>
+<span id="cb2-1011"><a href="#cb2-1011" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1</span></span>
+<span id="cb2-1012"><a href="#cb2-1012" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 2</span></span>
+<span id="cb2-1013"><a href="#cb2-1013" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 2</span></span>
+<span id="cb2-1014"><a href="#cb2-1014" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1</span></span>
+<span id="cb2-1015"><a href="#cb2-1015" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-1016"><a href="#cb2-1016" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-1017"><a href="#cb2-1017" aria-hidden="true" tabindex="-1"></a><span class="co">#> This is the utility functions </span></span>
+<span id="cb2-1018"><a href="#cb2-1018" aria-hidden="true" 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="cb2-1019"><a href="#cb2-1019" aria-hidden="true" tabindex="-1"></a><span class="co">#> Initial gradient value:</span></span>
+<span id="cb2-1020"><a href="#cb2-1020" aria-hidden="true" tabindex="-1"></a><span class="co">#> bpreis blade bwarte </span></span>
+<span id="cb2-1021"><a href="#cb2-1021" aria-hidden="true" tabindex="-1"></a><span class="co">#> -140.0 -935.0 332.5 </span></span>
+<span id="cb2-1022"><a href="#cb2-1022" aria-hidden="true" tabindex="-1"></a><span class="co">#> initial value 998.131940 </span></span>
+<span id="cb2-1023"><a href="#cb2-1023" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 2 value 978.973745</span></span>
+<span id="cb2-1024"><a href="#cb2-1024" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 3 value 978.139237</span></span>
+<span id="cb2-1025"><a href="#cb2-1025" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 4 value 978.053388</span></span>
+<span id="cb2-1026"><a href="#cb2-1026" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 5 value 974.539684</span></span>
+<span id="cb2-1027"><a href="#cb2-1027" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 6 value 974.530921</span></span>
+<span id="cb2-1028"><a href="#cb2-1028" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 6 value 974.530913</span></span>
+<span id="cb2-1029"><a href="#cb2-1029" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 6 value 974.530913</span></span>
+<span id="cb2-1030"><a href="#cb2-1030" aria-hidden="true" tabindex="-1"></a><span class="co">#> final value 974.530913 </span></span>
+<span id="cb2-1031"><a href="#cb2-1031" aria-hidden="true" tabindex="-1"></a><span class="co">#> converged</span></span>
+<span id="cb2-1032"><a href="#cb2-1032" aria-hidden="true" tabindex="-1"></a><span class="co">#> This is Run number 2 </span></span>
+<span id="cb2-1033"><a href="#cb2-1033" aria-hidden="true" tabindex="-1"></a><span class="co">#> does sou_gis exist: FALSE </span></span>
+<span id="cb2-1034"><a href="#cb2-1034" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-1035"><a href="#cb2-1035" aria-hidden="true" tabindex="-1"></a><span class="co">#> dataset final_set exists: FALSE </span></span>
+<span id="cb2-1036"><a href="#cb2-1036" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-1037"><a href="#cb2-1037" aria-hidden="true" tabindex="-1"></a><span class="co">#> decisiongroups exists: TRUE</span></span>
+<span id="cb2-1038"><a href="#cb2-1038" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 2 </span></span>
+<span id="cb2-1039"><a href="#cb2-1039" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1007 433 </span></span>
+<span id="cb2-1040"><a href="#cb2-1040" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-1041"><a href="#cb2-1041" aria-hidden="true" tabindex="-1"></a><span class="co">#> data has been made </span></span>
+<span id="cb2-1042"><a href="#cb2-1042" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-1043"><a href="#cb2-1043" aria-hidden="true" tabindex="-1"></a><span class="co">#> First few observations </span></span>
+<span id="cb2-1044"><a href="#cb2-1044" aria-hidden="true" tabindex="-1"></a><span class="co">#> ID Choice_situation Block alt1_x1</span></span>
+<span id="cb2-1045"><a href="#cb2-1045" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1 1 1 80</span></span>
+<span id="cb2-1046"><a href="#cb2-1046" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1 2 1 60</span></span>
+<span id="cb2-1047"><a href="#cb2-1047" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1 3 1 60</span></span>
+<span id="cb2-1048"><a href="#cb2-1048" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 1 4 1 20</span></span>
+<span id="cb2-1049"><a href="#cb2-1049" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1 5 1 40</span></span>
+<span id="cb2-1050"><a href="#cb2-1050" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1 6 1 60</span></span>
+<span id="cb2-1051"><a href="#cb2-1051" aria-hidden="true" tabindex="-1"></a><span class="co">#> alt2_x1 alt1_x2 alt2_x2 alt1_x3 alt2_x3</span></span>
+<span id="cb2-1052"><a href="#cb2-1052" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 20 2.5 20.0 10 5</span></span>
+<span id="cb2-1053"><a href="#cb2-1053" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 40 5.0 10.0 5 10</span></span>
+<span id="cb2-1054"><a href="#cb2-1054" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 20 20.0 20.0 0 10</span></span>
+<span id="cb2-1055"><a href="#cb2-1055" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 80 20.0 2.5 0 10</span></span>
+<span id="cb2-1056"><a href="#cb2-1056" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 80 10.0 5.0 10 5</span></span>
+<span id="cb2-1057"><a href="#cb2-1057" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 80 5.0 2.5 0 0</span></span>
+<span id="cb2-1058"><a href="#cb2-1058" aria-hidden="true" tabindex="-1"></a><span class="co">#> group V_1 V_2 e_1</span></span>
+<span id="cb2-1059"><a href="#cb2-1059" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1 -0.775 -1.500 0.2982044</span></span>
+<span id="cb2-1060"><a href="#cb2-1060" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1 -0.850 -0.900 3.4745400</span></span>
+<span id="cb2-1061"><a href="#cb2-1061" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1 -2.000 -1.400 3.5031943</span></span>
+<span id="cb2-1062"><a href="#cb2-1062" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 1 -1.600 -0.775 0.8386792</span></span>
+<span id="cb2-1063"><a href="#cb2-1063" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1 -0.900 -1.050 1.8279937</span></span>
+<span id="cb2-1064"><a href="#cb2-1064" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 1 -0.950 -0.975 -1.1295965</span></span>
+<span id="cb2-1065"><a href="#cb2-1065" aria-hidden="true" tabindex="-1"></a><span class="co">#> e_2 U_1 U_2</span></span>
+<span id="cb2-1066"><a href="#cb2-1066" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 0.85521723 -0.4767956 -0.6447828</span></span>
+<span id="cb2-1067"><a href="#cb2-1067" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 2.20601106 2.6245400 1.3060111</span></span>
+<span id="cb2-1068"><a href="#cb2-1068" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 -0.03275998 1.5031943 -1.4327600</span></span>
+<span id="cb2-1069"><a href="#cb2-1069" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 0.87875516 -0.7613208 0.1037552</span></span>
+<span id="cb2-1070"><a href="#cb2-1070" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 -0.45114524 0.9279937 -1.5011452</span></span>
+<span id="cb2-1071"><a href="#cb2-1071" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 -0.63521469 -2.0795965 -1.6102147</span></span>
+<span id="cb2-1072"><a href="#cb2-1072" aria-hidden="true" tabindex="-1"></a><span class="co">#> CHOICE</span></span>
+<span id="cb2-1073"><a href="#cb2-1073" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1</span></span>
+<span id="cb2-1074"><a href="#cb2-1074" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 1</span></span>
+<span id="cb2-1075"><a href="#cb2-1075" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 1</span></span>
+<span id="cb2-1076"><a href="#cb2-1076" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 2</span></span>
+<span id="cb2-1077"><a href="#cb2-1077" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 1</span></span>
+<span id="cb2-1078"><a href="#cb2-1078" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 2</span></span>
+<span id="cb2-1079"><a href="#cb2-1079" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-1080"><a href="#cb2-1080" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-1081"><a href="#cb2-1081" aria-hidden="true" tabindex="-1"></a><span class="co">#> This is the utility functions </span></span>
+<span id="cb2-1082"><a href="#cb2-1082" aria-hidden="true" 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="cb2-1083"><a href="#cb2-1083" aria-hidden="true" tabindex="-1"></a><span class="co">#> Initial gradient value:</span></span>
+<span id="cb2-1084"><a href="#cb2-1084" aria-hidden="true" tabindex="-1"></a><span class="co">#> bpreis blade bwarte </span></span>
+<span id="cb2-1085"><a href="#cb2-1085" aria-hidden="true" tabindex="-1"></a><span class="co">#> -660.0 -925.0 442.5 </span></span>
+<span id="cb2-1086"><a href="#cb2-1086" aria-hidden="true" tabindex="-1"></a><span class="co">#> initial value 998.131940 </span></span>
+<span id="cb2-1087"><a href="#cb2-1087" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 2 value 990.452175</span></span>
+<span id="cb2-1088"><a href="#cb2-1088" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 3 value 972.395315</span></span>
+<span id="cb2-1089"><a href="#cb2-1089" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 4 value 972.382101</span></span>
+<span id="cb2-1090"><a href="#cb2-1090" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 5 value 968.290249</span></span>
+<span id="cb2-1091"><a href="#cb2-1091" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 6 value 968.286828</span></span>
+<span id="cb2-1092"><a href="#cb2-1092" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 6 value 968.286823</span></span>
+<span id="cb2-1093"><a href="#cb2-1093" aria-hidden="true" tabindex="-1"></a><span class="co">#> iter 6 value 968.286823</span></span>
+<span id="cb2-1094"><a href="#cb2-1094" aria-hidden="true" tabindex="-1"></a><span class="co">#> final value 968.286823 </span></span>
+<span id="cb2-1095"><a href="#cb2-1095" aria-hidden="true" tabindex="-1"></a><span class="co">#> converged</span></span>
+<span id="cb2-1096"><a href="#cb2-1096" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-1097"><a href="#cb2-1097" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-1098"><a href="#cb2-1098" aria-hidden="true" tabindex="-1"></a><span class="co">#> ================ ==== === ===== ==== ===== ===== ===== ====</span></span>
+<span id="cb2-1099"><a href="#cb2-1099" aria-hidden="true" tabindex="-1"></a><span class="co">#> \ vars n mean sd min max range se</span></span>
+<span id="cb2-1100"><a href="#cb2-1100" aria-hidden="true" tabindex="-1"></a><span class="co">#> ================ ==== === ===== ==== ===== ===== ===== ====</span></span>
+<span id="cb2-1101"><a href="#cb2-1101" aria-hidden="true" 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="cb2-1102"><a href="#cb2-1102" aria-hidden="true" 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="cb2-1103"><a href="#cb2-1103" aria-hidden="true" tabindex="-1"></a><span class="co">#> est_bwarte 3 2 0.01 0.01 0.01 0.02 0.01 0.00</span></span>
+<span id="cb2-1104"><a href="#cb2-1104" aria-hidden="true" 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="cb2-1105"><a href="#cb2-1105" aria-hidden="true" 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="cb2-1106"><a href="#cb2-1106" aria-hidden="true" tabindex="-1"></a><span class="co">#> rob_pval0_bwarte 6 2 0.20 0.13 0.10 0.29 0.19 0.09</span></span>
+<span id="cb2-1107"><a href="#cb2-1107" aria-hidden="true" tabindex="-1"></a><span class="co">#> ================ ==== === ===== ==== ===== ===== ===== ====</span></span>
+<span id="cb2-1108"><a href="#cb2-1108" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-1109"><a href="#cb2-1109" aria-hidden="true" tabindex="-1"></a><span class="co">#> FALSE </span></span>
+<span id="cb2-1110"><a href="#cb2-1110" aria-hidden="true" tabindex="-1"></a><span class="co">#> 100 </span></span>
+<span id="cb2-1111"><a href="#cb2-1111" aria-hidden="true" tabindex="-1"></a><span class="co">#> 34.002 sec elapsed</span></span>
+<span id="cb2-1112"><a href="#cb2-1112" aria-hidden="true" tabindex="-1"></a><span class="co">#> $tic</span></span>
+<span id="cb2-1113"><a href="#cb2-1113" aria-hidden="true" tabindex="-1"></a><span class="co">#> elapsed </span></span>
+<span id="cb2-1114"><a href="#cb2-1114" aria-hidden="true" tabindex="-1"></a><span class="co">#> 672.76 </span></span>
+<span id="cb2-1115"><a href="#cb2-1115" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-1116"><a href="#cb2-1116" aria-hidden="true" tabindex="-1"></a><span class="co">#> $toc</span></span>
+<span id="cb2-1117"><a href="#cb2-1117" aria-hidden="true" tabindex="-1"></a><span class="co">#> elapsed </span></span>
+<span id="cb2-1118"><a href="#cb2-1118" aria-hidden="true" tabindex="-1"></a><span class="co">#> 706.762 </span></span>
+<span id="cb2-1119"><a href="#cb2-1119" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-1120"><a href="#cb2-1120" aria-hidden="true" tabindex="-1"></a><span class="co">#> $msg</span></span>
+<span id="cb2-1121"><a href="#cb2-1121" aria-hidden="true" tabindex="-1"></a><span class="co">#> logical(0)</span></span>
+<span id="cb2-1122"><a href="#cb2-1122" aria-hidden="true" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb2-1123"><a href="#cb2-1123" aria-hidden="true" tabindex="-1"></a><span class="co">#> $callback_msg</span></span>
+<span id="cb2-1124"><a href="#cb2-1124" aria-hidden="true" tabindex="-1"></a><span class="co">#> [1] "34.002 sec elapsed"</span></span></code></pre></div>
+<p><img 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" 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" 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" width="100%" /></p>
+
+</body>
+</html>
diff --git a/README.md b/README.md
index c60ae4a269354e1020f50cd8ed08e360e9527014..42e8da0c18dcdd841de649e9d9c7c64df88cebd8 100644
--- a/README.md
+++ b/README.md
@@ -46,6 +46,1130 @@ remotes::install_gitlab(repo = "dj44vuri/simulateDCE" , host = "https://git.idiv
This is a basic example which shows you how to solve a common problem:
``` r
-library(simulateDCE)
-## basic example code
+ library(simulateDCE)
+library(rlang)
+
+print("lests")
+#> [1] "lests"
+
+#set.seed(22233)
+
+# Designpath indicates the folder where all designs that should be simulated are stored. Can be either ngd files (for NGENE) or Robjects for spdesign)
+designpath<- system.file("extdata","SE_DRIVE" ,package = "simulateDCE")
+
+# on your computer, it would be something like
+# designpath <- "c:/myfancyDCE/Designs"
+
+
+# number of respondents
+resps =120
+
+# number of simulations to run (about 200 is minimum if you want to be serious -- but it takes some time. always test your code with 2 simulations, and if it runs smoothly, go for more.)
+nosim= 2
+
+# If you want to use different groups of respondents, use this. The following line means that you have one group of 70% size and one group of 30% size
+decisiongroups=c(0,0.7,1)
+
+# set the values of the parameters you want to use in the simulation
+bpreis = -0.01
+blade = -0.07
+bwarte = 0.02
+
+# If you want to do some manipulations in the design before you simulate, add a list called manipulations. Here, we devide some attributes by 10
+
+manipulations = list(alt1.x2= expr(alt1.x2/10),
+ alt1.x3= expr(alt1.x3/10),
+ alt2.x2= expr(alt2.x2/10),
+ alt2.x3= expr(alt2.x3/10)
+)
+
+
+#place your utility functions here. We have two utility functions and two sets of utility functions. This is because we assume that 70% act according to the utility u1 and 30% act to the utility u2 (that is, they only decide according to the price and ignore the other attributes)
+u<-list( u1 =
+
+ list(
+ v1 =V.1~ bpreis * alt1.x1 + blade*alt1.x2 + bwarte*alt1.x3 ,
+ v2 =V.2~ bpreis * alt2.x1 + blade*alt2.x2 + bwarte*alt2.x3
+ )
+
+ ,
+ u2 = list( v1 =V.1~ bpreis * alt1.x1 ,
+ v2 =V.2~ bpreis * alt2.x1)
+
+)
+
+# specify the designtype "ngene" or "spdesign"
+destype="ngene"
+
+
+#lets go
+sedrive <- simulateDCE::sim_all()
+#> Utility function used in simulation, ie the true utility:
+#>
+#> $u1
+#> $u1$v1
+#> V.1 ~ bpreis * alt1.x1 + blade * alt1.x2 + bwarte * alt1.x3
+#>
+#> $u1$v2
+#> V.2 ~ bpreis * alt2.x1 + blade * alt2.x2 + bwarte * alt2.x3
+#>
+#>
+#> $u2
+#> $u2$v1
+#> V.1 ~ bpreis * alt1.x1
+#>
+#> $u2$v2
+#> V.2 ~ bpreis * alt2.x1
+#>
+#>
+#> Utility function used for Logit estimation with mixl:
+#>
+#> [1] "U_1 = @bpreis *$alt1_x1 + @blade *$alt1_x2 + @bwarte *$alt1_x3 ;U_2 = @bpreis *$alt2_x1 + @blade *$alt2_x2 + @bwarte *$alt2_x3 ;"
+#> New names:
+#> • `Choice situation` ->
+#> `Choice.situation`
+#> • `` -> `...10`
+#> Warning: One or more parsing issues, call
+#> `problems()` on your data frame for
+#> details, e.g.:
+#> dat <- vroom(...)
+#> problems(dat)
+#>
+#> does sou_gis exist: FALSE
+#>
+#> dataset final_set exists: FALSE
+#>
+#> decisiongroups exists: TRUE
+#> 1 2
+#> 1007 433
+#>
+#> data has been made
+#>
+#> First few observations
+#> ID Choice_situation alt1_x1 alt1_x2
+#> 1 1 7 80 2.5
+#> 2 1 19 20 2.5
+#> 3 1 30 20 10.0
+#> 4 1 32 40 20.0
+#> 5 1 39 40 20.0
+#> 6 1 48 60 5.0
+#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
+#> 1 10.0 60 20.0 10 1
+#> 2 5.0 60 2.5 0 1
+#> 3 5.0 80 5.0 10 1
+#> 4 2.5 80 2.5 0 1
+#> 5 0.0 80 10.0 10 1
+#> 6 2.5 20 5.0 10 1
+#> group V_1 V_2 e_1
+#> 1 1 -0.775 -1.800 2.8927045
+#> 2 1 -0.275 -0.775 2.1129458
+#> 3 1 -0.800 -0.950 -0.3070059
+#> 4 1 -1.750 -0.975 0.2125815
+#> 5 1 -1.800 -1.300 0.5101632
+#> 6 1 -0.900 -0.350 -0.9494807
+#> e_2 U_1 U_2 CHOICE
+#> 1 0.09958433 2.117705 -1.700416 1
+#> 2 3.47451776 1.837946 2.699518 2
+#> 3 -0.28860974 -1.107006 -1.238610 1
+#> 4 3.65240491 -1.537418 2.677405 2
+#> 5 -0.14448942 -1.289837 -1.444489 1
+#> 6 -1.04296995 -1.849481 -1.392970 2
+#>
+#>
+#> This is Run number 1
+#> does sou_gis exist: FALSE
+#>
+#> dataset final_set exists: FALSE
+#>
+#> decisiongroups exists: TRUE
+#> 1 2
+#> 1007 433
+#>
+#> data has been made
+#>
+#> First few observations
+#> ID Choice_situation alt1_x1 alt1_x2
+#> 1 1 7 80 2.5
+#> 2 1 19 20 2.5
+#> 3 1 30 20 10.0
+#> 4 1 32 40 20.0
+#> 5 1 39 40 20.0
+#> 6 1 48 60 5.0
+#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
+#> 1 10.0 60 20.0 10 1
+#> 2 5.0 60 2.5 0 1
+#> 3 5.0 80 5.0 10 1
+#> 4 2.5 80 2.5 0 1
+#> 5 0.0 80 10.0 10 1
+#> 6 2.5 20 5.0 10 1
+#> group V_1 V_2 e_1
+#> 1 1 -0.775 -1.800 -0.06362638
+#> 2 1 -0.275 -0.775 -0.81571577
+#> 3 1 -0.800 -0.950 -1.09388352
+#> 4 1 -1.750 -0.975 0.28996875
+#> 5 1 -1.800 -1.300 1.03059224
+#> 6 1 -0.900 -0.350 -1.10504379
+#> e_2 U_1 U_2 CHOICE
+#> 1 0.1958595 -0.8386264 -1.6041405 1
+#> 2 0.1028995 -1.0907158 -0.6721005 2
+#> 3 0.7165451 -1.8938835 -0.2334549 2
+#> 4 1.4748351 -1.4600313 0.4998351 2
+#> 5 4.5718398 -0.7694078 3.2718398 2
+#> 6 0.8766732 -2.0050438 0.5266732 2
+#>
+#>
+#> This is the utility functions
+#> 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
+#> Initial gradient value:
+#> bpreis blade bwarte
+#> -860.0 -1147.5 532.5
+#> initial value 998.131940
+#> iter 2 value 988.178813
+#> iter 3 value 959.683236
+#> iter 4 value 959.648380
+#> iter 5 value 955.999179
+#> iter 6 value 955.979330
+#> iter 7 value 955.979295
+#> iter 7 value 955.979295
+#> iter 7 value 955.979295
+#> final value 955.979295
+#> converged
+#> This is Run number 2
+#> does sou_gis exist: FALSE
+#>
+#> dataset final_set exists: FALSE
+#>
+#> decisiongroups exists: TRUE
+#> 1 2
+#> 1007 433
+#>
+#> data has been made
+#>
+#> First few observations
+#> ID Choice_situation alt1_x1 alt1_x2
+#> 1 1 7 80 2.5
+#> 2 1 19 20 2.5
+#> 3 1 30 20 10.0
+#> 4 1 32 40 20.0
+#> 5 1 39 40 20.0
+#> 6 1 48 60 5.0
+#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
+#> 1 10.0 60 20.0 10 1
+#> 2 5.0 60 2.5 0 1
+#> 3 5.0 80 5.0 10 1
+#> 4 2.5 80 2.5 0 1
+#> 5 0.0 80 10.0 10 1
+#> 6 2.5 20 5.0 10 1
+#> group V_1 V_2 e_1
+#> 1 1 -0.775 -1.800 -0.8816771
+#> 2 1 -0.275 -0.775 0.9004269
+#> 3 1 -0.800 -0.950 -0.3108731
+#> 4 1 -1.750 -0.975 -0.7695269
+#> 5 1 -1.800 -1.300 2.8853455
+#> 6 1 -0.900 -0.350 -0.1098324
+#> e_2 U_1 U_2
+#> 1 0.6516580 -1.6566771 -1.14834197
+#> 2 0.4584193 0.6254269 -0.31658066
+#> 3 1.2184928 -1.1108731 0.26849278
+#> 4 -0.1660211 -2.5195269 -1.14102109
+#> 5 -0.5943992 1.0853455 -1.89439922
+#> 6 0.3193140 -1.0098324 -0.03068595
+#> CHOICE
+#> 1 2
+#> 2 1
+#> 3 2
+#> 4 2
+#> 5 1
+#> 6 2
+#>
+#>
+#> This is the utility functions
+#> 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
+#> Initial gradient value:
+#> bpreis blade bwarte
+#> 120 -655 295
+#> initial value 998.131940
+#> iter 2 value 994.305298
+#> iter 3 value 990.053293
+#> iter 4 value 989.940656
+#> iter 5 value 987.629292
+#> iter 6 value 987.628992
+#> iter 6 value 987.628991
+#> iter 6 value 987.628991
+#> final value 987.628991
+#> converged
+#>
+#>
+#> ================ ==== === ===== ==== ===== ===== ===== ====
+#> \ vars n mean sd min max range se
+#> ================ ==== === ===== ==== ===== ===== ===== ====
+#> est_bpreis 1 2 -0.01 0.01 -0.01 0.00 0.01 0.00
+#> est_blade 2 2 -0.04 0.02 -0.06 -0.02 0.03 0.02
+#> est_bwarte 3 2 0.02 0.00 0.02 0.03 0.01 0.00
+#> rob_pval0_bpreis 4 2 0.04 0.06 0.00 0.09 0.09 0.04
+#> rob_pval0_blade 5 2 0.00 0.00 0.00 0.00 0.00 0.00
+#> rob_pval0_bwarte 6 2 0.04 0.03 0.02 0.06 0.04 0.02
+#> ================ ==== === ===== ==== ===== ===== ===== ====
+#>
+#> FALSE TRUE
+#> 50 50
+#> Utility function used in simulation, ie the true utility:
+#>
+#> $u1
+#> $u1$v1
+#> V.1 ~ bpreis * alt1.x1 + blade * alt1.x2 + bwarte * alt1.x3
+#>
+#> $u1$v2
+#> V.2 ~ bpreis * alt2.x1 + blade * alt2.x2 + bwarte * alt2.x3
+#>
+#>
+#> $u2
+#> $u2$v1
+#> V.1 ~ bpreis * alt1.x1
+#>
+#> $u2$v2
+#> V.2 ~ bpreis * alt2.x1
+#>
+#>
+#> Utility function used for Logit estimation with mixl:
+#>
+#> [1] "U_1 = @bpreis *$alt1_x1 + @blade *$alt1_x2 + @bwarte *$alt1_x3 ;U_2 = @bpreis *$alt2_x1 + @blade *$alt2_x2 + @bwarte *$alt2_x3 ;"
+#> New names:
+#> • `Choice situation` ->
+#> `Choice.situation`
+#> • `` -> `...10`
+#> Warning: One or more parsing issues, call
+#> `problems()` on your data frame for
+#> details, e.g.:
+#> dat <- vroom(...)
+#> problems(dat)
+#>
+#> does sou_gis exist: FALSE
+#>
+#> dataset final_set exists: FALSE
+#>
+#> decisiongroups exists: TRUE
+#> 1 2
+#> 1007 433
+#>
+#> data has been made
+#>
+#> First few observations
+#> ID Choice_situation alt1_x1 alt1_x2
+#> 1 1 12 60 2.5
+#> 2 1 16 20 10.0
+#> 3 1 17 20 20.0
+#> 4 1 25 60 5.0
+#> 5 1 29 20 5.0
+#> 6 1 32 40 10.0
+#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
+#> 1 0.0 20 20.0 10 1
+#> 2 5.0 40 5.0 0 1
+#> 3 0.0 80 10.0 10 1
+#> 4 10.0 20 20.0 5 1
+#> 5 10.0 80 5.0 0 1
+#> 6 2.5 80 2.5 5 1
+#> group V_1 V_2 e_1
+#> 1 1 -0.775 -1.400 1.20580231
+#> 2 1 -0.800 -0.750 -0.72752412
+#> 3 1 -1.600 -1.300 -0.05762304
+#> 4 1 -0.750 -1.500 -0.83547157
+#> 5 1 -0.350 -1.150 3.85444600
+#> 6 1 -1.050 -0.875 1.64701776
+#> e_2 U_1 U_2
+#> 1 -0.28691332 0.4308023 -1.6869133
+#> 2 0.06648158 -1.5275241 -0.6835184
+#> 3 1.68916541 -1.6576230 0.3891654
+#> 4 0.40357792 -1.5854716 -1.0964221
+#> 5 0.13880669 3.5044460 -1.0111933
+#> 6 1.09745093 0.5970178 0.2224509
+#> CHOICE
+#> 1 1
+#> 2 2
+#> 3 2
+#> 4 2
+#> 5 1
+#> 6 1
+#>
+#>
+#> This is Run number 1
+#> does sou_gis exist: FALSE
+#>
+#> dataset final_set exists: FALSE
+#>
+#> decisiongroups exists: TRUE
+#> 1 2
+#> 1007 433
+#>
+#> data has been made
+#>
+#> First few observations
+#> ID Choice_situation alt1_x1 alt1_x2
+#> 1 1 12 60 2.5
+#> 2 1 16 20 10.0
+#> 3 1 17 20 20.0
+#> 4 1 25 60 5.0
+#> 5 1 29 20 5.0
+#> 6 1 32 40 10.0
+#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
+#> 1 0.0 20 20.0 10 1
+#> 2 5.0 40 5.0 0 1
+#> 3 0.0 80 10.0 10 1
+#> 4 10.0 20 20.0 5 1
+#> 5 10.0 80 5.0 0 1
+#> 6 2.5 80 2.5 5 1
+#> group V_1 V_2 e_1
+#> 1 1 -0.775 -1.400 -0.09932726
+#> 2 1 -0.800 -0.750 2.18018219
+#> 3 1 -1.600 -1.300 1.30134429
+#> 4 1 -0.750 -1.500 1.55197796
+#> 5 1 -0.350 -1.150 0.07874983
+#> 6 1 -1.050 -0.875 -1.06565108
+#> e_2 U_1 U_2
+#> 1 2.2497903 -0.8743273 0.84979034
+#> 2 0.3329742 1.3801822 -0.41702578
+#> 3 0.9046182 -0.2986557 -0.39538182
+#> 4 -1.2414809 0.8019780 -2.74148090
+#> 5 -0.8624243 -0.2712502 -2.01242427
+#> 6 0.9398788 -2.1156511 0.06487882
+#> CHOICE
+#> 1 2
+#> 2 1
+#> 3 1
+#> 4 1
+#> 5 1
+#> 6 2
+#>
+#>
+#> This is the utility functions
+#> 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
+#> Initial gradient value:
+#> bpreis blade bwarte
+#> -340 -1095 305
+#> initial value 998.131940
+#> iter 2 value 984.073383
+#> iter 3 value 978.081615
+#> iter 4 value 977.767304
+#> iter 5 value 971.033395
+#> iter 6 value 971.027390
+#> iter 6 value 971.027385
+#> iter 6 value 971.027385
+#> final value 971.027385
+#> converged
+#> This is Run number 2
+#> does sou_gis exist: FALSE
+#>
+#> dataset final_set exists: FALSE
+#>
+#> decisiongroups exists: TRUE
+#> 1 2
+#> 1007 433
+#>
+#> data has been made
+#>
+#> First few observations
+#> ID Choice_situation alt1_x1 alt1_x2
+#> 1 1 12 60 2.5
+#> 2 1 16 20 10.0
+#> 3 1 17 20 20.0
+#> 4 1 25 60 5.0
+#> 5 1 29 20 5.0
+#> 6 1 32 40 10.0
+#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
+#> 1 0.0 20 20.0 10 1
+#> 2 5.0 40 5.0 0 1
+#> 3 0.0 80 10.0 10 1
+#> 4 10.0 20 20.0 5 1
+#> 5 10.0 80 5.0 0 1
+#> 6 2.5 80 2.5 5 1
+#> group V_1 V_2 e_1
+#> 1 1 -0.775 -1.400 0.44334136
+#> 2 1 -0.800 -0.750 -0.43185157
+#> 3 1 -1.600 -1.300 -0.09584172
+#> 4 1 -0.750 -1.500 2.74658736
+#> 5 1 -0.350 -1.150 -0.51575280
+#> 6 1 -1.050 -0.875 -0.33088933
+#> e_2 U_1 U_2 CHOICE
+#> 1 0.3975165 -0.3316586 -1.0024835 1
+#> 2 1.4211569 -1.2318516 0.6711569 2
+#> 3 1.0034880 -1.6958417 -0.2965120 2
+#> 4 0.8780181 1.9965874 -0.6219819 1
+#> 5 0.9818505 -0.8657528 -0.1681495 2
+#> 6 1.7042698 -1.3808893 0.8292698 2
+#>
+#>
+#> This is the utility functions
+#> 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
+#> Initial gradient value:
+#> bpreis blade bwarte
+#> -280 -905 345
+#> initial value 998.131940
+#> iter 2 value 988.003109
+#> iter 3 value 983.732741
+#> iter 4 value 983.724196
+#> iter 5 value 979.048736
+#> iter 6 value 979.044949
+#> iter 6 value 979.044947
+#> iter 6 value 979.044947
+#> final value 979.044947
+#> converged
+#>
+#>
+#> ================ ==== === ===== ==== ===== ===== ===== ====
+#> \ vars n mean sd min max range se
+#> ================ ==== === ===== ==== ===== ===== ===== ====
+#> est_bpreis 1 2 -0.01 0.00 -0.01 -0.01 0.00 0.00
+#> est_blade 2 2 -0.04 0.01 -0.05 -0.04 0.01 0.01
+#> est_bwarte 3 2 0.01 0.01 0.00 0.01 0.01 0.00
+#> rob_pval0_bpreis 4 2 0.00 0.00 0.00 0.00 0.00 0.00
+#> rob_pval0_blade 5 2 0.00 0.00 0.00 0.00 0.00 0.00
+#> rob_pval0_bwarte 6 2 0.50 0.41 0.21 0.79 0.58 0.29
+#> ================ ==== === ===== ==== ===== ===== ===== ====
+#>
+#> FALSE
+#> 100
+#> Utility function used in simulation, ie the true utility:
+#>
+#> $u1
+#> $u1$v1
+#> V.1 ~ bpreis * alt1.x1 + blade * alt1.x2 + bwarte * alt1.x3
+#>
+#> $u1$v2
+#> V.2 ~ bpreis * alt2.x1 + blade * alt2.x2 + bwarte * alt2.x3
+#>
+#>
+#> $u2
+#> $u2$v1
+#> V.1 ~ bpreis * alt1.x1
+#>
+#> $u2$v2
+#> V.2 ~ bpreis * alt2.x1
+#>
+#>
+#> Utility function used for Logit estimation with mixl:
+#>
+#> [1] "U_1 = @bpreis *$alt1_x1 + @blade *$alt1_x2 + @bwarte *$alt1_x3 ;U_2 = @bpreis *$alt2_x1 + @blade *$alt2_x2 + @bwarte *$alt2_x3 ;"
+#> New names:
+#> • `Choice situation` ->
+#> `Choice.situation`
+#> • `` -> `...10`
+#> Warning: One or more parsing issues, call
+#> `problems()` on your data frame for
+#> details, e.g.:
+#> dat <- vroom(...)
+#> problems(dat)
+#>
+#> does sou_gis exist: FALSE
+#>
+#> dataset final_set exists: FALSE
+#>
+#> decisiongroups exists: TRUE
+#> 1 2
+#> 1007 433
+#>
+#> data has been made
+#>
+#> First few observations
+#> ID Choice_situation alt1_x1 alt1_x2
+#> 1 1 3 80 5.0
+#> 2 1 5 60 2.5
+#> 3 1 10 80 2.5
+#> 4 1 34 80 2.5
+#> 5 1 37 40 5.0
+#> 6 1 39 20 20.0
+#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
+#> 1 0.0 20 5.0 10.0 1
+#> 2 5.0 20 20.0 5.0 1
+#> 3 2.5 20 20.0 0.0 1
+#> 4 5.0 60 5.0 5.0 1
+#> 5 10.0 60 5.0 2.5 1
+#> 6 2.5 60 2.5 2.5 1
+#> group V_1 V_2 e_1
+#> 1 1 -1.150 -0.350 -0.32663211
+#> 2 1 -0.675 -1.500 -0.04162689
+#> 3 1 -0.925 -1.600 -0.52492188
+#> 4 1 -0.875 -0.850 -1.14189023
+#> 5 1 -0.550 -0.900 0.19650068
+#> 6 1 -1.550 -0.725 2.74825383
+#> e_2 U_1 U_2 CHOICE
+#> 1 0.2288010 -1.4766321 -0.1211990 2
+#> 2 1.0875948 -0.7166269 -0.4124052 2
+#> 3 0.1472598 -1.4499219 -1.4527402 1
+#> 4 0.5765191 -2.0168902 -0.2734809 2
+#> 5 -0.5803934 -0.3534993 -1.4803934 1
+#> 6 -0.8761884 1.1982538 -1.6011884 1
+#>
+#>
+#> This is Run number 1
+#> does sou_gis exist: FALSE
+#>
+#> dataset final_set exists: FALSE
+#>
+#> decisiongroups exists: TRUE
+#> 1 2
+#> 1007 433
+#>
+#> data has been made
+#>
+#> First few observations
+#> ID Choice_situation alt1_x1 alt1_x2
+#> 1 1 3 80 5.0
+#> 2 1 5 60 2.5
+#> 3 1 10 80 2.5
+#> 4 1 34 80 2.5
+#> 5 1 37 40 5.0
+#> 6 1 39 20 20.0
+#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
+#> 1 0.0 20 5.0 10.0 1
+#> 2 5.0 20 20.0 5.0 1
+#> 3 2.5 20 20.0 0.0 1
+#> 4 5.0 60 5.0 5.0 1
+#> 5 10.0 60 5.0 2.5 1
+#> 6 2.5 60 2.5 2.5 1
+#> group V_1 V_2 e_1
+#> 1 1 -1.150 -0.350 0.9214793
+#> 2 1 -0.675 -1.500 -0.7937151
+#> 3 1 -0.925 -1.600 0.5612728
+#> 4 1 -0.875 -0.850 2.9230889
+#> 5 1 -0.550 -0.900 0.1761764
+#> 6 1 -1.550 -0.725 1.0340286
+#> e_2 U_1 U_2
+#> 1 0.09295071 -0.2285207 -0.25704929
+#> 2 -0.18278050 -1.4687151 -1.68278050
+#> 3 -0.24595450 -0.3637272 -1.84595450
+#> 4 -0.74954312 2.0480889 -1.59954312
+#> 5 -0.52864852 -0.3738236 -1.42864852
+#> 6 0.69916199 -0.5159714 -0.02583801
+#> CHOICE
+#> 1 1
+#> 2 1
+#> 3 1
+#> 4 1
+#> 5 1
+#> 6 2
+#>
+#>
+#> This is the utility functions
+#> 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
+#> Initial gradient value:
+#> bpreis blade bwarte
+#> -2640.0 -1060.0 662.5
+#> initial value 998.131940
+#> iter 2 value 987.031183
+#> iter 3 value 957.685378
+#> iter 4 value 957.680370
+#> iter 5 value 954.925156
+#> iter 6 value 945.725076
+#> iter 7 value 945.695285
+#> iter 8 value 945.695175
+#> iter 8 value 945.695175
+#> final value 945.695175
+#> converged
+#> This is Run number 2
+#> does sou_gis exist: FALSE
+#>
+#> dataset final_set exists: FALSE
+#>
+#> decisiongroups exists: TRUE
+#> 1 2
+#> 1007 433
+#>
+#> data has been made
+#>
+#> First few observations
+#> ID Choice_situation alt1_x1 alt1_x2
+#> 1 1 3 80 5.0
+#> 2 1 5 60 2.5
+#> 3 1 10 80 2.5
+#> 4 1 34 80 2.5
+#> 5 1 37 40 5.0
+#> 6 1 39 20 20.0
+#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
+#> 1 0.0 20 5.0 10.0 1
+#> 2 5.0 20 20.0 5.0 1
+#> 3 2.5 20 20.0 0.0 1
+#> 4 5.0 60 5.0 5.0 1
+#> 5 10.0 60 5.0 2.5 1
+#> 6 2.5 60 2.5 2.5 1
+#> group V_1 V_2 e_1
+#> 1 1 -1.150 -0.350 -0.8218428
+#> 2 1 -0.675 -1.500 0.4133131
+#> 3 1 -0.925 -1.600 0.4824588
+#> 4 1 -0.875 -0.850 -1.2658097
+#> 5 1 -0.550 -0.900 -0.6930574
+#> 6 1 -1.550 -0.725 -0.6815915
+#> e_2 U_1 U_2 CHOICE
+#> 1 -0.6493651 -1.9718428 -0.9993651 2
+#> 2 0.8461510 -0.2616869 -0.6538490 1
+#> 3 0.3849732 -0.4425412 -1.2150268 1
+#> 4 -0.2971578 -2.1408097 -1.1471578 2
+#> 5 -0.8024491 -1.2430574 -1.7024491 1
+#> 6 -0.4752339 -2.2315915 -1.2002339 2
+#>
+#>
+#> This is the utility functions
+#> 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
+#> Initial gradient value:
+#> bpreis blade bwarte
+#> -1320.0 -1027.5 537.5
+#> initial value 998.131940
+#> iter 2 value 992.731937
+#> iter 3 value 967.306984
+#> iter 4 value 967.287995
+#> iter 5 value 964.318376
+#> iter 6 value 964.313823
+#> iter 6 value 964.313820
+#> iter 6 value 964.313820
+#> final value 964.313820
+#> converged
+#>
+#>
+#> ================ ==== === ===== ==== ===== ===== ===== ====
+#> \ vars n mean sd min max range se
+#> ================ ==== === ===== ==== ===== ===== ===== ====
+#> est_bpreis 1 2 -0.01 0.00 -0.01 -0.01 0.00 0.00
+#> est_blade 2 2 -0.05 0.01 -0.06 -0.05 0.01 0.01
+#> est_bwarte 3 2 0.02 0.00 0.02 0.02 0.00 0.00
+#> rob_pval0_bpreis 4 2 0.00 0.00 0.00 0.00 0.00 0.00
+#> rob_pval0_blade 5 2 0.00 0.00 0.00 0.00 0.00 0.00
+#> rob_pval0_bwarte 6 2 0.06 0.01 0.06 0.07 0.01 0.01
+#> ================ ==== === ===== ==== ===== ===== ===== ====
+#>
+#> FALSE
+#> 100
+#> Utility function used in simulation, ie the true utility:
+#>
+#> $u1
+#> $u1$v1
+#> V.1 ~ bpreis * alt1.x1 + blade * alt1.x2 + bwarte * alt1.x3
+#>
+#> $u1$v2
+#> V.2 ~ bpreis * alt2.x1 + blade * alt2.x2 + bwarte * alt2.x3
+#>
+#>
+#> $u2
+#> $u2$v1
+#> V.1 ~ bpreis * alt1.x1
+#>
+#> $u2$v2
+#> V.2 ~ bpreis * alt2.x1
+#>
+#>
+#> Utility function used for Logit estimation with mixl:
+#>
+#> [1] "U_1 = @bpreis *$alt1_x1 + @blade *$alt1_x2 + @bwarte *$alt1_x3 ;U_2 = @bpreis *$alt2_x1 + @blade *$alt2_x2 + @bwarte *$alt2_x3 ;"
+#> New names:
+#> • `Choice situation` ->
+#> `Choice.situation`
+#> • `` -> `...10`
+#> Warning: One or more parsing issues, call
+#> `problems()` on your data frame for
+#> details, e.g.:
+#> dat <- vroom(...)
+#> problems(dat)
+#>
+#> does sou_gis exist: FALSE
+#>
+#> dataset final_set exists: FALSE
+#>
+#> decisiongroups exists: TRUE
+#> 1 2
+#> 1007 433
+#>
+#> data has been made
+#>
+#> First few observations
+#> ID Choice_situation alt1_x1 alt1_x2
+#> 1 1 9 80 5.0
+#> 2 1 12 60 2.5
+#> 3 1 13 20 20.0
+#> 4 1 70 80 5.0
+#> 5 1 71 60 20.0
+#> 6 1 73 60 10.0
+#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
+#> 1 0 60 20.0 10.0 1
+#> 2 10 40 20.0 0.0 1
+#> 3 10 80 2.5 0.0 1
+#> 4 10 20 20.0 2.5 1
+#> 5 10 80 10.0 0.0 1
+#> 6 0 40 20.0 10.0 1
+#> group V_1 V_2 e_1
+#> 1 1 -1.150 -1.800 0.4772651
+#> 2 1 -0.575 -1.800 -1.0611813
+#> 3 1 -1.400 -0.975 -0.4549814
+#> 4 1 -0.950 -1.550 1.0741179
+#> 5 1 -1.800 -1.500 0.6850764
+#> 6 1 -1.300 -1.600 2.1581413
+#> e_2 U_1 U_2
+#> 1 -0.58862455 -0.6727349 -2.3886245
+#> 2 1.67391615 -1.6361813 -0.1260839
+#> 3 0.08433351 -1.8549814 -0.8906665
+#> 4 0.16471135 0.1241179 -1.3852887
+#> 5 -0.80503749 -1.1149236 -2.3050375
+#> 6 -0.78193942 0.8581413 -2.3819394
+#> CHOICE
+#> 1 1
+#> 2 2
+#> 3 2
+#> 4 1
+#> 5 1
+#> 6 1
+#>
+#>
+#> This is Run number 1
+#> does sou_gis exist: FALSE
+#>
+#> dataset final_set exists: FALSE
+#>
+#> decisiongroups exists: TRUE
+#> 1 2
+#> 1007 433
+#>
+#> data has been made
+#>
+#> First few observations
+#> ID Choice_situation alt1_x1 alt1_x2
+#> 1 1 9 80 5.0
+#> 2 1 12 60 2.5
+#> 3 1 13 20 20.0
+#> 4 1 70 80 5.0
+#> 5 1 71 60 20.0
+#> 6 1 73 60 10.0
+#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
+#> 1 0 60 20.0 10.0 1
+#> 2 10 40 20.0 0.0 1
+#> 3 10 80 2.5 0.0 1
+#> 4 10 20 20.0 2.5 1
+#> 5 10 80 10.0 0.0 1
+#> 6 0 40 20.0 10.0 1
+#> group V_1 V_2 e_1
+#> 1 1 -1.150 -1.800 -0.284096565
+#> 2 1 -0.575 -1.800 -0.020855208
+#> 3 1 -1.400 -0.975 2.808193631
+#> 4 1 -0.950 -1.550 1.512635398
+#> 5 1 -1.800 -1.500 -0.869856696
+#> 6 1 -1.300 -1.600 0.001496538
+#> e_2 U_1 U_2 CHOICE
+#> 1 3.7852439 -1.4340966 1.9852439 2
+#> 2 2.5441347 -0.5958552 0.7441347 2
+#> 3 -0.1408644 1.4081936 -1.1158644 1
+#> 4 -0.2739250 0.5626354 -1.8239250 1
+#> 5 -0.2920285 -2.6698567 -1.7920285 2
+#> 6 0.9243727 -1.2985035 -0.6756273 2
+#>
+#>
+#> This is the utility functions
+#> 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
+#> Initial gradient value:
+#> bpreis blade bwarte
+#> -2400 -3680 1320
+#> initial value 998.131940
+#> iter 2 value 956.785003
+#> iter 3 value 912.039295
+#> iter 4 value 911.870417
+#> iter 5 value 885.881709
+#> iter 6 value 885.187568
+#> iter 7 value 885.171492
+#> iter 8 value 885.171476
+#> iter 8 value 885.171476
+#> final value 885.171476
+#> converged
+#> This is Run number 2
+#> does sou_gis exist: FALSE
+#>
+#> dataset final_set exists: FALSE
+#>
+#> decisiongroups exists: TRUE
+#> 1 2
+#> 1007 433
+#>
+#> data has been made
+#>
+#> First few observations
+#> ID Choice_situation alt1_x1 alt1_x2
+#> 1 1 9 80 5.0
+#> 2 1 12 60 2.5
+#> 3 1 13 20 20.0
+#> 4 1 70 80 5.0
+#> 5 1 71 60 20.0
+#> 6 1 73 60 10.0
+#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
+#> 1 0 60 20.0 10.0 1
+#> 2 10 40 20.0 0.0 1
+#> 3 10 80 2.5 0.0 1
+#> 4 10 20 20.0 2.5 1
+#> 5 10 80 10.0 0.0 1
+#> 6 0 40 20.0 10.0 1
+#> group V_1 V_2 e_1
+#> 1 1 -1.150 -1.800 0.6645192
+#> 2 1 -0.575 -1.800 -0.8450051
+#> 3 1 -1.400 -0.975 0.1125148
+#> 4 1 -0.950 -1.550 1.0543183
+#> 5 1 -1.800 -1.500 1.1168013
+#> 6 1 -1.300 -1.600 -0.1311416
+#> e_2 U_1 U_2 CHOICE
+#> 1 2.3304233 -0.4854808 0.5304233 2
+#> 2 0.2022020 -1.4200051 -1.5977980 1
+#> 3 -0.1148274 -1.2874852 -1.0898274 2
+#> 4 -1.3880265 0.1043183 -2.9380265 1
+#> 5 0.1356148 -0.6831987 -1.3643852 1
+#> 6 0.9455601 -1.4311416 -0.6544399 2
+#>
+#>
+#> This is the utility functions
+#> 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
+#> Initial gradient value:
+#> bpreis blade bwarte
+#> -3200.0 -2932.5 1142.5
+#> initial value 998.131940
+#> iter 2 value 965.989359
+#> iter 3 value 962.943975
+#> iter 4 value 962.790350
+#> iter 5 value 915.909913
+#> iter 6 value 915.781694
+#> iter 7 value 915.780836
+#> iter 7 value 915.780833
+#> iter 7 value 915.780833
+#> final value 915.780833
+#> converged
+#>
+#>
+#> ================ ==== === ===== ==== ===== ===== ===== ====
+#> \ vars n mean sd min max range se
+#> ================ ==== === ===== ==== ===== ===== ===== ====
+#> est_bpreis 1 2 -0.01 0.00 -0.01 -0.01 0.00 0.00
+#> est_blade 2 2 -0.05 0.01 -0.05 -0.04 0.01 0.00
+#> est_bwarte 3 2 0.02 0.00 0.02 0.02 0.00 0.00
+#> rob_pval0_bpreis 4 2 0.00 0.00 0.00 0.00 0.00 0.00
+#> rob_pval0_blade 5 2 0.00 0.00 0.00 0.00 0.00 0.00
+#> rob_pval0_bwarte 6 2 0.01 0.02 0.00 0.03 0.03 0.01
+#> ================ ==== === ===== ==== ===== ===== ===== ====
+#>
+#> TRUE
+#> 100
+#> Utility function used in simulation, ie the true utility:
+#>
+#> $u1
+#> $u1$v1
+#> V.1 ~ bpreis * alt1.x1 + blade * alt1.x2 + bwarte * alt1.x3
+#>
+#> $u1$v2
+#> V.2 ~ bpreis * alt2.x1 + blade * alt2.x2 + bwarte * alt2.x3
+#>
+#>
+#> $u2
+#> $u2$v1
+#> V.1 ~ bpreis * alt1.x1
+#>
+#> $u2$v2
+#> V.2 ~ bpreis * alt2.x1
+#>
+#>
+#> Utility function used for Logit estimation with mixl:
+#>
+#> [1] "U_1 = @bpreis *$alt1_x1 + @blade *$alt1_x2 + @bwarte *$alt1_x3 ;U_2 = @bpreis *$alt2_x1 + @blade *$alt2_x2 + @bwarte *$alt2_x3 ;"
+#> New names:
+#> • `Choice situation` ->
+#> `Choice.situation`
+#>
+#> does sou_gis exist: FALSE
+#>
+#> dataset final_set exists: FALSE
+#>
+#> decisiongroups exists: TRUE
+#> 1 2
+#> 1007 433
+#>
+#> data has been made
+#>
+#> First few observations
+#> ID Choice_situation Block alt1_x1
+#> 1 1 1 1 80
+#> 2 1 2 1 60
+#> 3 1 3 1 60
+#> 4 1 4 1 20
+#> 5 1 5 1 40
+#> 6 1 6 1 60
+#> alt2_x1 alt1_x2 alt2_x2 alt1_x3 alt2_x3
+#> 1 20 2.5 20.0 10 5
+#> 2 40 5.0 10.0 5 10
+#> 3 20 20.0 20.0 0 10
+#> 4 80 20.0 2.5 0 10
+#> 5 80 10.0 5.0 10 5
+#> 6 80 5.0 2.5 0 0
+#> group V_1 V_2 e_1
+#> 1 1 -0.775 -1.500 0.53504757
+#> 2 1 -0.850 -0.900 -0.93293876
+#> 3 1 -2.000 -1.400 -1.97083982
+#> 4 1 -1.600 -0.775 -0.09847358
+#> 5 1 -0.900 -1.050 -0.91059496
+#> 6 1 -0.950 -0.975 -0.27261150
+#> e_2 U_1 U_2 CHOICE
+#> 1 0.9131705 -0.2399524 -0.5868295 1
+#> 2 -1.5528907 -1.7829388 -2.4528907 1
+#> 3 -0.2159494 -3.9708398 -1.6159494 2
+#> 4 0.1685500 -1.6984736 -0.6064500 2
+#> 5 1.6256604 -1.8105950 0.5756604 2
+#> 6 1.5055143 -1.2226115 0.5305143 2
+#>
+#>
+#> This is Run number 1
+#> does sou_gis exist: FALSE
+#>
+#> dataset final_set exists: FALSE
+#>
+#> decisiongroups exists: TRUE
+#> 1 2
+#> 1007 433
+#>
+#> data has been made
+#>
+#> First few observations
+#> ID Choice_situation Block alt1_x1
+#> 1 1 1 1 80
+#> 2 1 2 1 60
+#> 3 1 3 1 60
+#> 4 1 4 1 20
+#> 5 1 5 1 40
+#> 6 1 6 1 60
+#> alt2_x1 alt1_x2 alt2_x2 alt1_x3 alt2_x3
+#> 1 20 2.5 20.0 10 5
+#> 2 40 5.0 10.0 5 10
+#> 3 20 20.0 20.0 0 10
+#> 4 80 20.0 2.5 0 10
+#> 5 80 10.0 5.0 10 5
+#> 6 80 5.0 2.5 0 0
+#> group V_1 V_2 e_1
+#> 1 1 -0.775 -1.500 -0.2361754
+#> 2 1 -0.850 -0.900 1.2985628
+#> 3 1 -2.000 -1.400 2.6517108
+#> 4 1 -1.600 -0.775 -0.3215271
+#> 5 1 -0.900 -1.050 -1.1880836
+#> 6 1 -0.950 -0.975 0.9386790
+#> e_2 U_1 U_2
+#> 1 -0.2249671 -1.01117540 -1.72496708
+#> 2 0.4231642 0.44856278 -0.47683584
+#> 3 0.4632492 0.65171082 -0.93675077
+#> 4 0.6960098 -1.92152712 -0.07899021
+#> 5 1.0360301 -2.08808358 -0.01396992
+#> 6 -0.1024565 -0.01132103 -1.07745654
+#> CHOICE
+#> 1 1
+#> 2 1
+#> 3 1
+#> 4 2
+#> 5 2
+#> 6 1
+#>
+#>
+#> This is the utility functions
+#> 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
+#> Initial gradient value:
+#> bpreis blade bwarte
+#> -140.0 -935.0 332.5
+#> initial value 998.131940
+#> iter 2 value 978.973745
+#> iter 3 value 978.139237
+#> iter 4 value 978.053388
+#> iter 5 value 974.539684
+#> iter 6 value 974.530921
+#> iter 6 value 974.530913
+#> iter 6 value 974.530913
+#> final value 974.530913
+#> converged
+#> This is Run number 2
+#> does sou_gis exist: FALSE
+#>
+#> dataset final_set exists: FALSE
+#>
+#> decisiongroups exists: TRUE
+#> 1 2
+#> 1007 433
+#>
+#> data has been made
+#>
+#> First few observations
+#> ID Choice_situation Block alt1_x1
+#> 1 1 1 1 80
+#> 2 1 2 1 60
+#> 3 1 3 1 60
+#> 4 1 4 1 20
+#> 5 1 5 1 40
+#> 6 1 6 1 60
+#> alt2_x1 alt1_x2 alt2_x2 alt1_x3 alt2_x3
+#> 1 20 2.5 20.0 10 5
+#> 2 40 5.0 10.0 5 10
+#> 3 20 20.0 20.0 0 10
+#> 4 80 20.0 2.5 0 10
+#> 5 80 10.0 5.0 10 5
+#> 6 80 5.0 2.5 0 0
+#> group V_1 V_2 e_1
+#> 1 1 -0.775 -1.500 0.2982044
+#> 2 1 -0.850 -0.900 3.4745400
+#> 3 1 -2.000 -1.400 3.5031943
+#> 4 1 -1.600 -0.775 0.8386792
+#> 5 1 -0.900 -1.050 1.8279937
+#> 6 1 -0.950 -0.975 -1.1295965
+#> e_2 U_1 U_2
+#> 1 0.85521723 -0.4767956 -0.6447828
+#> 2 2.20601106 2.6245400 1.3060111
+#> 3 -0.03275998 1.5031943 -1.4327600
+#> 4 0.87875516 -0.7613208 0.1037552
+#> 5 -0.45114524 0.9279937 -1.5011452
+#> 6 -0.63521469 -2.0795965 -1.6102147
+#> CHOICE
+#> 1 1
+#> 2 1
+#> 3 1
+#> 4 2
+#> 5 1
+#> 6 2
+#>
+#>
+#> This is the utility functions
+#> 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
+#> Initial gradient value:
+#> bpreis blade bwarte
+#> -660.0 -925.0 442.5
+#> initial value 998.131940
+#> iter 2 value 990.452175
+#> iter 3 value 972.395315
+#> iter 4 value 972.382101
+#> iter 5 value 968.290249
+#> iter 6 value 968.286828
+#> iter 6 value 968.286823
+#> iter 6 value 968.286823
+#> final value 968.286823
+#> converged
+#>
+#>
+#> ================ ==== === ===== ==== ===== ===== ===== ====
+#> \ vars n mean sd min max range se
+#> ================ ==== === ===== ==== ===== ===== ===== ====
+#> est_bpreis 1 2 -0.01 0.00 -0.01 -0.01 0.00 0.00
+#> est_blade 2 2 -0.05 0.00 -0.05 -0.05 0.00 0.00
+#> est_bwarte 3 2 0.01 0.01 0.01 0.02 0.01 0.00
+#> rob_pval0_bpreis 4 2 0.00 0.00 0.00 0.00 0.00 0.00
+#> rob_pval0_blade 5 2 0.00 0.00 0.00 0.00 0.00 0.00
+#> rob_pval0_bwarte 6 2 0.20 0.13 0.10 0.29 0.19 0.09
+#> ================ ==== === ===== ==== ===== ===== ===== ====
+#>
+#> FALSE
+#> 100
+#> 34.002 sec elapsed
+#> $tic
+#> elapsed
+#> 672.76
+#>
+#> $toc
+#> elapsed
+#> 706.762
+#>
+#> $msg
+#> logical(0)
+#>
+#> $callback_msg
+#> [1] "34.002 sec elapsed"
```
+
+<img src="man/figures/README-example-1.png" width="100%" /><img src="man/figures/README-example-2.png" width="100%" /><img src="man/figures/README-example-3.png" width="100%" />
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