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analyze_experiment_1.html
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analyze_experiment_1.html
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<!DOCTYPE html>
<html>
<head>
<style type="text/css">
.knitr .inline {
background-color: #f7f7f7;
border:solid 1px #B0B0B0;
}
.error {
font-weight: bold;
color: #FF0000;
}
.warning {
font-weight: bold;
}
.message {
font-style: italic;
}
.source, .output, .warning, .error, .message {
padding: 0 1em;
border:solid 1px #F7F7F7;
}
.source {
background-color: #f5f5f5;
}
.rimage .left {
text-align: left;
}
.rimage .right {
text-align: right;
}
.rimage .center {
text-align: center;
}
.hl.num {
color: #AF0F91;
}
.hl.str {
color: #317ECC;
}
.hl.com {
color: #AD95AF;
font-style: italic;
}
.hl.opt {
color: #000000;
}
.hl.std {
color: #585858;
}
.hl.kwa {
color: #295F94;
font-weight: bold;
}
.hl.kwb {
color: #B05A65;
}
.hl.kwc {
color: #55aa55;
}
.hl.kwd {
color: #BC5A65;
font-weight: bold;
}
</style>
<script src="https://yihui.name/media/js/center-images.js"></script>
<title>A Report Generated by knitr</title>
</head>
<body>
<p>This report is automatically generated with the R
package <a href="https://yihui.name/knitr/"><strong>knitr</strong></a>
(version <code class="knitr inline">1.20</code>)
.</p>
<div class="chunk" id="auto-report"><div class="rcode"><div class="source"><pre class="knitr r"><span class="hl kwd">library</span><span class="hl std">(lmerTest)</span>
</pre></div>
<div class="message"><pre class="knitr r">## Loading required package: lme4
</pre></div>
<div class="message"><pre class="knitr r">## Loading required package: Matrix
</pre></div>
<div class="message"><pre class="knitr r">##
## Attaching package: 'lmerTest'
</pre></div>
<div class="message"><pre class="knitr r">## The following object is masked from 'package:lme4':
##
## lmer
</pre></div>
<div class="message"><pre class="knitr r">## The following object is masked from 'package:stats':
##
## step
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl kwd">library</span><span class="hl std">(lsmeans)</span>
</pre></div>
<div class="message"><pre class="knitr r">## The 'lsmeans' package is being deprecated.
## Users are encouraged to switch to 'emmeans'.
## See help('transition') for more information, including how
## to convert 'lsmeans' objects and scripts to work with 'emmeans'.
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl kwd">library</span><span class="hl std">(scales)</span>
<span class="hl kwd">library</span><span class="hl std">(ggrepel)</span>
</pre></div>
<div class="message"><pre class="knitr r">## Loading required package: ggplot2
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl kwd">library</span><span class="hl std">(readr)</span>
</pre></div>
<div class="message"><pre class="knitr r">##
## Attaching package: 'readr'
</pre></div>
<div class="message"><pre class="knitr r">## The following object is masked from 'package:scales':
##
## col_factor
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl kwd">library</span><span class="hl std">(magrittr)</span>
<span class="hl kwd">library</span><span class="hl std">(dplyr)</span>
</pre></div>
<div class="warning"><pre class="knitr r">## Warning: package 'dplyr' was built under R version 3.5.2
</pre></div>
<div class="message"><pre class="knitr r">##
## Attaching package: 'dplyr'
</pre></div>
<div class="message"><pre class="knitr r">## The following objects are masked from 'package:stats':
##
## filter, lag
</pre></div>
<div class="message"><pre class="knitr r">## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl kwd">library</span><span class="hl std">(tidyr)</span>
</pre></div>
<div class="message"><pre class="knitr r">##
## Attaching package: 'tidyr'
</pre></div>
<div class="message"><pre class="knitr r">## The following object is masked from 'package:magrittr':
##
## extract
</pre></div>
<div class="message"><pre class="knitr r">## The following object is masked from 'package:Matrix':
##
## expand
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl kwd">library</span><span class="hl std">(ggplot2)</span>
<span class="hl kwd">library</span><span class="hl std">(lsr)</span>
<span class="hl kwd">library</span><span class="hl std">(emmeans)</span>
</pre></div>
<div class="warning"><pre class="knitr r">## Warning: package 'emmeans' was built under R version 3.5.2
</pre></div>
<div class="message"><pre class="knitr r">##
## Attaching package: 'emmeans'
</pre></div>
<div class="message"><pre class="knitr r">## The following objects are masked from 'package:lsmeans':
##
## .all.vars, .aovlist.dffun, .diag, .get.offset, .my.vcov, add_grouping,
## as.glht, contrast, get.lsm.option, lsm, lsm.options, lsmeans, lsmip, lsmobj,
## lstrends, make.tran, pmm, pmmeans, pmmip, pmmobj, pmtrends, regrid, test
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl kwd">library</span><span class="hl std">(xtable)</span>
<span class="hl kwd">theme_set</span><span class="hl std">(</span><span class="hl kwd">theme_bw</span><span class="hl std">())</span>
<span class="hl kwd">emm_options</span><span class="hl std">(</span><span class="hl kwc">pbkrtest.limit</span> <span class="hl std">=</span> <span class="hl num">12500</span><span class="hl std">)</span>
<span class="hl kwd">emm_options</span><span class="hl std">(</span><span class="hl kwc">lmerTest.limit</span> <span class="hl std">=</span> <span class="hl num">12500</span><span class="hl std">)</span>
<span class="hl kwd">source</span><span class="hl std">(</span><span class="hl str">'lib.R'</span><span class="hl std">)</span>
</pre></div>
<div class="message"><pre class="knitr r">##
## Attaching package: 'ggridges'
</pre></div>
<div class="message"><pre class="knitr r">## The following object is masked from 'package:ggplot2':
##
## scale_discrete_manual
</pre></div>
<div class="message"><pre class="knitr r">## ── Attaching packages ───────────────────────────────────────────────── tidyverse 1.2.1 ──
</pre></div>
<div class="message"><pre class="knitr r">## ✔ tibble 2.1.3 ✔ stringr 1.3.1
## ✔ purrr 0.2.5 ✔ forcats 0.3.0
</pre></div>
<div class="warning"><pre class="knitr r">## Warning: package 'tibble' was built under R version 3.5.2
</pre></div>
<div class="message"><pre class="knitr r">## ── Conflicts ──────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ readr::col_factor() masks scales::col_factor()
## ✖ purrr::discard() masks scales::discard()
## ✖ tidyr::expand() masks Matrix::expand()
## ✖ tidyr::extract() masks magrittr::extract()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ ggridges::scale_discrete_manual() masks ggplot2::scale_discrete_manual()
## ✖ purrr::set_names() masks magrittr::set_names()
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl kwa">if</span> <span class="hl std">(</span><span class="hl opt">!</span><span class="hl kwd">dir.exists</span><span class="hl std">(</span><span class="hl str">'figures/exp1'</span><span class="hl std">))</span>
<span class="hl kwd">dir.create</span><span class="hl std">(</span><span class="hl str">'figures/exp1'</span><span class="hl std">,</span> <span class="hl kwc">recursive</span> <span class="hl std">= T)</span>
<span class="hl std">my_pallete</span> <span class="hl kwb"><-</span> <span class="hl kwd">c</span><span class="hl std">(</span><span class="hl str">'#92c5de'</span><span class="hl std">,</span><span class="hl str">'#0571b0'</span><span class="hl std">,</span><span class="hl str">'#f4a582'</span><span class="hl std">,</span><span class="hl str">'#ca0020'</span><span class="hl std">,</span><span class="hl str">'#404040'</span><span class="hl std">)</span>
<span class="hl com">#######################################################</span>
<span class="hl com"># Training phase</span>
<span class="hl com">#######################################################</span>
<span class="hl com"># read data</span>
<span class="hl std">training_responses</span> <span class="hl kwb"><-</span> <span class="hl kwd">read_csv</span><span class="hl std">(</span><span class="hl str">"data/exp1/data_training.csv"</span><span class="hl std">)</span>
</pre></div>
<div class="message"><pre class="knitr r">## Parsed with column specification:
## cols(
## worker_id = col_character(),
## condition = col_character(),
## q_id = col_integer(),
## model_pred = col_double(),
## actual_price = col_double(),
## final_pred = col_double()
## )
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl com"># recode conditions</span>
<span class="hl std">training_responses</span> <span class="hl kwb"><-</span> <span class="hl std">training_responses</span> <span class="hl opt">%>%</span>
<span class="hl kwd">mutate</span><span class="hl std">(</span><span class="hl kwc">condition</span> <span class="hl std">=</span> <span class="hl kwd">recode_factor</span><span class="hl std">(</span><span class="hl kwd">as.factor</span><span class="hl std">(condition),</span>
<span class="hl str">"C1"</span> <span class="hl std">=</span> <span class="hl str">"CLEAR-2"</span><span class="hl std">,</span>
<span class="hl str">"C3"</span> <span class="hl std">=</span> <span class="hl str">"CLEAR-8"</span><span class="hl std">,</span>
<span class="hl str">"C0"</span> <span class="hl std">=</span> <span class="hl str">"BB-2"</span><span class="hl std">,</span>
<span class="hl str">"C2"</span> <span class="hl std">=</span> <span class="hl str">"BB-8"</span><span class="hl std">,</span>
<span class="hl str">"C4"</span> <span class="hl std">=</span> <span class="hl str">"NO-MODEL"</span><span class="hl std">))</span>
<span class="hl com"># break data in two parts for histograms</span>
<span class="hl com"># update apartment indeces to start from 1</span>
<span class="hl std">training_responses1</span> <span class="hl kwb"><-</span> <span class="hl kwd">filter</span><span class="hl std">(training_responses, q_id</span> <span class="hl opt"><</span> <span class="hl num">5</span><span class="hl std">)</span>
<span class="hl std">training_responses1</span><span class="hl opt">$</span><span class="hl std">q_id</span> <span class="hl kwb"><-</span> <span class="hl std">training_responses1</span><span class="hl opt">$</span><span class="hl std">q_id</span> <span class="hl opt">+</span> <span class="hl num">1</span>
<span class="hl std">training_responses2</span> <span class="hl kwb"><-</span> <span class="hl kwd">filter</span><span class="hl std">(training_responses, q_id</span> <span class="hl opt">>=</span> <span class="hl num">5</span><span class="hl std">)</span>
<span class="hl std">training_responses2</span><span class="hl opt">$</span><span class="hl std">q_id</span> <span class="hl kwb"><-</span> <span class="hl std">training_responses2</span><span class="hl opt">$</span><span class="hl std">q_id</span> <span class="hl opt">+</span> <span class="hl num">1</span>
<span class="hl com"># plot</span>
<span class="hl std">breaks</span> <span class="hl kwb"><-</span> <span class="hl kwd">seq</span><span class="hl std">(</span><span class="hl num">0</span><span class="hl std">,</span><span class="hl num">3</span><span class="hl std">,</span><span class="hl num">1</span><span class="hl std">)</span>
<span class="hl kwd">ggplot</span><span class="hl std">(training_responses1,</span> <span class="hl kwd">aes</span><span class="hl std">(</span><span class="hl kwc">x</span> <span class="hl std">= final_pred,</span> <span class="hl kwc">fill</span><span class="hl std">=condition))</span> <span class="hl opt">+</span>
<span class="hl kwd">geom_histogram</span><span class="hl std">(</span><span class="hl kwc">binwidth</span> <span class="hl std">=</span> <span class="hl num">0.1</span><span class="hl std">,</span> <span class="hl kwc">alpha</span> <span class="hl std">=</span> <span class="hl num">0.5</span><span class="hl std">)</span> <span class="hl opt">+</span>
<span class="hl kwd">geom_vline</span><span class="hl std">(</span><span class="hl kwc">alpha</span><span class="hl std">=</span><span class="hl num">.2</span><span class="hl std">,</span> <span class="hl kwc">size</span><span class="hl std">=</span><span class="hl num">1</span><span class="hl std">,</span> <span class="hl kwd">aes</span><span class="hl std">(</span><span class="hl kwc">xintercept</span> <span class="hl std">= model_pred,</span> <span class="hl kwc">linetype</span><span class="hl std">=</span><span class="hl str">"Model's prediction"</span><span class="hl std">))</span><span class="hl opt">+</span>
<span class="hl kwd">geom_vline</span><span class="hl std">(</span><span class="hl kwc">alpha</span><span class="hl std">=</span><span class="hl num">.2</span><span class="hl std">,</span> <span class="hl kwc">size</span><span class="hl std">=</span><span class="hl num">1</span><span class="hl std">,</span> <span class="hl kwd">aes</span><span class="hl std">(</span><span class="hl kwc">xintercept</span> <span class="hl std">= actual_price,</span> <span class="hl kwc">linetype</span><span class="hl std">=</span><span class="hl str">"Actual price"</span><span class="hl std">))</span><span class="hl opt">+</span>
<span class="hl kwd">scale_fill_manual</span><span class="hl std">(</span><span class="hl kwc">guide</span> <span class="hl std">=</span> <span class="hl num">FALSE</span><span class="hl std">,</span><span class="hl kwc">values</span><span class="hl std">=my_pallete)</span> <span class="hl opt">+</span> <span class="hl kwd">scale_colour_manual</span><span class="hl std">(</span><span class="hl kwc">guide</span> <span class="hl std">=</span> <span class="hl num">FALSE</span><span class="hl std">,</span> <span class="hl kwc">values</span><span class="hl std">=my_pallete)</span><span class="hl opt">+</span>
<span class="hl kwd">facet_grid</span><span class="hl std">(</span><span class="hl kwd">vars</span><span class="hl std">(condition),</span> <span class="hl kwd">vars</span><span class="hl std">(q_id),</span> <span class="hl kwc">scale</span><span class="hl std">=</span><span class="hl str">'free'</span><span class="hl std">)</span><span class="hl opt">+</span>
<span class="hl kwd">xlab</span><span class="hl std">(</span><span class="hl str">"Participants' price prediction"</span><span class="hl std">)</span><span class="hl opt">+</span>
<span class="hl kwd">ylab</span><span class="hl std">(</span><span class="hl str">"Number of participants"</span><span class="hl std">)</span><span class="hl opt">+</span>
<span class="hl kwd">scale_x_continuous</span><span class="hl std">(</span><span class="hl kwc">breaks</span> <span class="hl std">= breaks,</span> <span class="hl kwc">labels</span> <span class="hl std">=</span> <span class="hl kwd">sprintf</span><span class="hl std">(</span><span class="hl str">'$%dM'</span><span class="hl std">, breaks))</span> <span class="hl opt">+</span>
<span class="hl kwd">theme</span><span class="hl std">(</span><span class="hl kwc">panel.grid.major</span> <span class="hl std">=</span> <span class="hl kwd">element_blank</span><span class="hl std">(),</span> <span class="hl kwc">panel.grid.minor</span> <span class="hl std">=</span> <span class="hl kwd">element_blank</span><span class="hl std">(),</span>
<span class="hl kwc">legend.position</span><span class="hl std">=</span><span class="hl str">"top"</span><span class="hl std">,</span> <span class="hl kwc">legend.title</span> <span class="hl std">=</span> <span class="hl kwd">element_blank</span><span class="hl std">(),</span> <span class="hl kwc">legend.key</span> <span class="hl std">=</span> <span class="hl kwd">element_rect</span><span class="hl std">(</span><span class="hl kwc">size</span> <span class="hl std">=</span> <span class="hl num">10</span><span class="hl std">),</span>
<span class="hl kwc">legend.key.size</span> <span class="hl std">=</span> <span class="hl kwd">unit</span><span class="hl std">(</span><span class="hl num">1.5</span><span class="hl std">,</span> <span class="hl str">'lines'</span><span class="hl std">),</span> <span class="hl kwc">legend.text</span><span class="hl std">=</span><span class="hl kwd">element_text</span><span class="hl std">(</span><span class="hl kwc">size</span><span class="hl std">=</span><span class="hl num">12</span><span class="hl std">),</span><span class="hl kwc">axis.title.x</span> <span class="hl std">=</span> <span class="hl kwd">element_text</span><span class="hl std">(</span><span class="hl kwc">size</span><span class="hl std">=</span><span class="hl num">20</span><span class="hl std">),</span>
<span class="hl kwc">axis.title.y</span> <span class="hl std">=</span> <span class="hl kwd">element_text</span><span class="hl std">(</span><span class="hl kwc">size</span><span class="hl std">=</span><span class="hl num">18</span><span class="hl std">),</span> <span class="hl kwc">axis.text.x</span> <span class="hl std">=</span> <span class="hl kwd">element_text</span><span class="hl std">(</span><span class="hl kwc">angle</span> <span class="hl std">=</span> <span class="hl num">45</span><span class="hl std">,</span> <span class="hl kwc">size</span><span class="hl std">=</span><span class="hl num">10</span><span class="hl std">),</span>
<span class="hl kwc">axis.text.y</span> <span class="hl std">=</span> <span class="hl kwd">element_text</span><span class="hl std">(</span><span class="hl kwc">size</span><span class="hl std">=</span><span class="hl num">14</span><span class="hl std">),</span> <span class="hl kwc">strip.text</span> <span class="hl std">=</span> <span class="hl kwd">element_text</span><span class="hl std">(</span><span class="hl kwc">size</span><span class="hl std">=</span><span class="hl num">14</span><span class="hl std">))</span>
</pre></div>
</div><div class="rimage center"><img src="figure/analyze-experiment-1-Rhtmlauto-report-1.png" title="plot of chunk auto-report" alt="plot of chunk auto-report" class="plot" /></div><div class="rcode">
<div class="source"><pre class="knitr r"><span class="hl kwd">ggsave</span><span class="hl std">(</span><span class="hl kwc">filename</span> <span class="hl std">=</span> <span class="hl str">"figures/exp1/dist_training_final_prediction_1.pdf"</span><span class="hl std">,</span> <span class="hl kwc">height</span><span class="hl std">=</span><span class="hl num">10</span><span class="hl std">,</span> <span class="hl kwc">width</span><span class="hl std">=</span><span class="hl num">10</span><span class="hl std">)</span>
<span class="hl kwd">ggplot</span><span class="hl std">(training_responses2,</span> <span class="hl kwd">aes</span><span class="hl std">(</span><span class="hl kwc">x</span> <span class="hl std">= final_pred,</span> <span class="hl kwc">fill</span><span class="hl std">=condition))</span> <span class="hl opt">+</span>
<span class="hl kwd">geom_histogram</span><span class="hl std">(</span><span class="hl kwc">binwidth</span> <span class="hl std">=</span> <span class="hl num">0.1</span><span class="hl std">,</span> <span class="hl kwc">alpha</span> <span class="hl std">=</span> <span class="hl num">0.5</span><span class="hl std">)</span> <span class="hl opt">+</span>
<span class="hl kwd">geom_vline</span><span class="hl std">(</span><span class="hl kwc">alpha</span><span class="hl std">=</span><span class="hl num">.2</span><span class="hl std">,</span> <span class="hl kwc">size</span><span class="hl std">=</span><span class="hl num">1</span><span class="hl std">,</span> <span class="hl kwd">aes</span><span class="hl std">(</span><span class="hl kwc">xintercept</span> <span class="hl std">= model_pred,</span> <span class="hl kwc">linetype</span><span class="hl std">=</span><span class="hl str">"Model's prediction"</span><span class="hl std">))</span><span class="hl opt">+</span>
<span class="hl kwd">geom_vline</span><span class="hl std">(</span><span class="hl kwc">alpha</span><span class="hl std">=</span><span class="hl num">.2</span><span class="hl std">,</span> <span class="hl kwc">size</span><span class="hl std">=</span><span class="hl num">1</span><span class="hl std">,</span> <span class="hl kwd">aes</span><span class="hl std">(</span><span class="hl kwc">xintercept</span> <span class="hl std">= actual_price,</span> <span class="hl kwc">linetype</span><span class="hl std">=</span><span class="hl str">"Actual price"</span><span class="hl std">))</span><span class="hl opt">+</span>
<span class="hl kwd">scale_fill_manual</span><span class="hl std">(</span><span class="hl kwc">guide</span> <span class="hl std">=</span> <span class="hl num">FALSE</span><span class="hl std">,</span><span class="hl kwc">values</span><span class="hl std">=my_pallete)</span> <span class="hl opt">+</span> <span class="hl kwd">scale_colour_manual</span><span class="hl std">(</span><span class="hl kwc">guide</span> <span class="hl std">=</span> <span class="hl num">FALSE</span><span class="hl std">,</span> <span class="hl kwc">values</span><span class="hl std">=my_pallete)</span><span class="hl opt">+</span>
<span class="hl kwd">facet_grid</span><span class="hl std">(</span><span class="hl kwd">vars</span><span class="hl std">(condition),</span> <span class="hl kwd">vars</span><span class="hl std">(q_id),</span> <span class="hl kwc">scale</span><span class="hl std">=</span><span class="hl str">'free'</span><span class="hl std">)</span><span class="hl opt">+</span>
<span class="hl kwd">xlab</span><span class="hl std">(</span><span class="hl str">"Participants' price prediction"</span><span class="hl std">)</span><span class="hl opt">+</span>
<span class="hl kwd">ylab</span><span class="hl std">(</span><span class="hl str">"Number of participants"</span><span class="hl std">)</span><span class="hl opt">+</span>
<span class="hl kwd">scale_x_continuous</span><span class="hl std">(</span><span class="hl kwc">breaks</span> <span class="hl std">= breaks,</span> <span class="hl kwc">labels</span> <span class="hl std">=</span> <span class="hl kwd">sprintf</span><span class="hl std">(</span><span class="hl str">'$%dM'</span><span class="hl std">, breaks))</span> <span class="hl opt">+</span>
<span class="hl kwd">theme</span><span class="hl std">(</span><span class="hl kwc">panel.grid.major</span> <span class="hl std">=</span> <span class="hl kwd">element_blank</span><span class="hl std">(),</span> <span class="hl kwc">panel.grid.minor</span> <span class="hl std">=</span> <span class="hl kwd">element_blank</span><span class="hl std">(),</span>
<span class="hl kwc">legend.position</span><span class="hl std">=</span><span class="hl str">"top"</span><span class="hl std">,</span> <span class="hl kwc">legend.title</span> <span class="hl std">=</span> <span class="hl kwd">element_blank</span><span class="hl std">(),</span> <span class="hl kwc">legend.key</span> <span class="hl std">=</span> <span class="hl kwd">element_rect</span><span class="hl std">(</span><span class="hl kwc">size</span> <span class="hl std">=</span> <span class="hl num">10</span><span class="hl std">),</span><span class="hl kwc">legend.key.size</span> <span class="hl std">=</span> <span class="hl kwd">unit</span><span class="hl std">(</span><span class="hl num">1.5</span><span class="hl std">,</span> <span class="hl str">'lines'</span><span class="hl std">),</span> <span class="hl kwc">legend.text</span><span class="hl std">=</span><span class="hl kwd">element_text</span><span class="hl std">(</span><span class="hl kwc">size</span><span class="hl std">=</span><span class="hl num">12</span><span class="hl std">),</span>
<span class="hl kwc">axis.title.x</span> <span class="hl std">=</span> <span class="hl kwd">element_text</span><span class="hl std">(</span><span class="hl kwc">size</span><span class="hl std">=</span><span class="hl num">20</span><span class="hl std">),</span> <span class="hl kwc">axis.title.y</span> <span class="hl std">=</span> <span class="hl kwd">element_text</span><span class="hl std">(</span><span class="hl kwc">size</span><span class="hl std">=</span><span class="hl num">18</span><span class="hl std">),</span> <span class="hl kwc">axis.text.x</span> <span class="hl std">=</span> <span class="hl kwd">element_text</span><span class="hl std">(</span><span class="hl kwc">angle</span> <span class="hl std">=</span> <span class="hl num">45</span><span class="hl std">,</span> <span class="hl kwc">size</span><span class="hl std">=</span><span class="hl num">10</span><span class="hl std">),</span> <span class="hl kwc">axis.text.y</span> <span class="hl std">=</span> <span class="hl kwd">element_text</span><span class="hl std">(</span><span class="hl kwc">size</span><span class="hl std">=</span><span class="hl num">14</span><span class="hl std">),</span>
<span class="hl kwc">strip.text</span> <span class="hl std">=</span> <span class="hl kwd">element_text</span><span class="hl std">(</span><span class="hl kwc">size</span><span class="hl std">=</span><span class="hl num">14</span><span class="hl std">))</span>
</pre></div>
</div><div class="rimage center"><img src="figure/analyze-experiment-1-Rhtmlauto-report-2.png" title="plot of chunk auto-report" alt="plot of chunk auto-report" class="plot" /></div><div class="rcode">
<div class="source"><pre class="knitr r"><span class="hl kwd">ggsave</span><span class="hl std">(</span><span class="hl kwc">filename</span> <span class="hl std">=</span> <span class="hl str">"figures/exp1/dist_training_final_prediction_2.pdf"</span><span class="hl std">,</span> <span class="hl kwc">height</span><span class="hl std">=</span><span class="hl num">10</span><span class="hl std">,</span> <span class="hl kwc">width</span><span class="hl std">=</span><span class="hl num">10</span><span class="hl std">)</span>
<span class="hl com">#######################################################</span>
<span class="hl com"># Testing phase</span>
<span class="hl com">#######################################################</span>
<span class="hl com"># read data</span>
<span class="hl std">responses</span> <span class="hl kwb"><-</span> <span class="hl kwd">read_csv</span><span class="hl std">(</span><span class="hl str">"data/exp1/data.csv"</span><span class="hl std">)</span>
</pre></div>
<div class="message"><pre class="knitr r">## Parsed with column specification:
## cols(
## worker_id = col_character(),
## condition = col_character(),
## q_id = col_integer(),
## model_pred = col_double(),
## actual_price = col_double(),
## user_model_pred = col_double(),
## user_model_pred_conf = col_integer(),
## model_conf = col_integer(),
## final_pred = col_double(),
## user_final_price_conf = col_integer(),
## step1_time = col_integer(),
## step2_time = col_integer(),
## step3_time = col_integer()
## )
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl com"># recode conditions and compute outcome measures</span>
<span class="hl std">responses</span> <span class="hl kwb"><-</span> <span class="hl std">responses</span> <span class="hl opt">%>%</span>
<span class="hl kwd">mutate</span><span class="hl std">(</span><span class="hl kwc">condition</span> <span class="hl std">=</span> <span class="hl kwd">recode_factor</span><span class="hl std">(</span><span class="hl kwd">as.factor</span><span class="hl std">(condition),</span>
<span class="hl str">"C1"</span> <span class="hl std">=</span> <span class="hl str">"CLEAR-2"</span><span class="hl std">,</span>
<span class="hl str">"C3"</span> <span class="hl std">=</span> <span class="hl str">"CLEAR-8"</span><span class="hl std">,</span>
<span class="hl str">"C0"</span> <span class="hl std">=</span> <span class="hl str">"BB-2"</span><span class="hl std">,</span>
<span class="hl str">"C2"</span> <span class="hl std">=</span> <span class="hl str">"BB-8"</span><span class="hl std">,</span>
<span class="hl str">"C4"</span> <span class="hl std">=</span> <span class="hl str">"NO-MODEL"</span><span class="hl std">))</span> <span class="hl opt">%>%</span>
<span class="hl kwd">mutate</span><span class="hl std">(</span><span class="hl kwc">pred_err</span> <span class="hl std">=</span> <span class="hl kwd">abs</span><span class="hl std">(final_pred</span> <span class="hl opt">-</span> <span class="hl std">actual_price),</span>
<span class="hl kwc">model_err</span> <span class="hl std">=</span> <span class="hl kwd">abs</span><span class="hl std">(model_pred</span> <span class="hl opt">-</span> <span class="hl std">actual_price),</span>
<span class="hl kwc">deviation</span> <span class="hl std">=</span> <span class="hl kwd">abs</span><span class="hl std">(final_pred</span> <span class="hl opt">-</span> <span class="hl std">model_pred),</span>
<span class="hl kwc">sim_err</span> <span class="hl std">=</span> <span class="hl kwd">abs</span><span class="hl std">(user_model_pred</span> <span class="hl opt">-</span> <span class="hl std">model_pred))</span> <span class="hl opt">%>%</span>
<span class="hl kwd">ungroup</span><span class="hl std">()</span>
<span class="hl com">#########################################</span>
<span class="hl com"># histograms</span>
<span class="hl com">#########################################</span>
<span class="hl com"># users' final prediction</span>
<span class="hl std">breaks</span> <span class="hl kwb"><-</span> <span class="hl kwd">seq</span><span class="hl std">(</span><span class="hl num">1</span><span class="hl std">,</span><span class="hl num">3</span><span class="hl std">,</span><span class="hl num">1</span><span class="hl std">)</span>
<span class="hl std">responses1</span> <span class="hl kwb"><-</span> <span class="hl kwd">filter</span><span class="hl std">(responses, q_id</span> <span class="hl opt"><</span> <span class="hl num">6</span><span class="hl std">)</span>
<span class="hl std">responses1</span><span class="hl opt">$</span><span class="hl std">q_id</span> <span class="hl kwb"><-</span> <span class="hl std">responses1</span><span class="hl opt">$</span><span class="hl std">q_id</span> <span class="hl opt">+</span> <span class="hl num">1</span>
<span class="hl std">responses2</span> <span class="hl kwb"><-</span> <span class="hl kwd">filter</span><span class="hl std">(responses, q_id</span> <span class="hl opt">>=</span> <span class="hl num">6</span><span class="hl std">)</span>
<span class="hl std">responses2</span><span class="hl opt">$</span><span class="hl std">q_id</span> <span class="hl kwb"><-</span> <span class="hl std">responses2</span><span class="hl opt">$</span><span class="hl std">q_id</span> <span class="hl opt">+</span> <span class="hl num">1</span>
<span class="hl com"># replace the -1 for actual price of apartment 12 (synthetic) with NA</span>
<span class="hl std">responses1</span><span class="hl opt">$</span><span class="hl std">actual_price</span> <span class="hl kwb"><-</span> <span class="hl kwd">replace</span><span class="hl std">(responses1</span><span class="hl opt">$</span><span class="hl std">actual_price,</span> <span class="hl kwd">which</span><span class="hl std">(responses1</span><span class="hl opt">$</span><span class="hl std">actual_price</span> <span class="hl opt">== -</span><span class="hl num">1</span><span class="hl std">),</span> <span class="hl num">NA</span><span class="hl std">)</span>
<span class="hl std">responses2</span><span class="hl opt">$</span><span class="hl std">actual_price</span> <span class="hl kwb"><-</span> <span class="hl kwd">replace</span><span class="hl std">(responses2</span><span class="hl opt">$</span><span class="hl std">actual_price,</span> <span class="hl kwd">which</span><span class="hl std">(responses2</span><span class="hl opt">$</span><span class="hl std">actual_price</span> <span class="hl opt">== -</span><span class="hl num">1</span><span class="hl std">),</span> <span class="hl num">NA</span><span class="hl std">)</span>
<span class="hl kwd">ggplot</span><span class="hl std">(responses1,</span> <span class="hl kwd">aes</span><span class="hl std">(</span><span class="hl kwc">x</span> <span class="hl std">= final_pred,</span> <span class="hl kwc">fill</span><span class="hl std">=condition))</span> <span class="hl opt">+</span>
<span class="hl kwd">geom_histogram</span><span class="hl std">(</span><span class="hl kwc">binwidth</span> <span class="hl std">=</span> <span class="hl num">0.1</span><span class="hl std">,</span> <span class="hl kwc">alpha</span> <span class="hl std">=</span> <span class="hl num">0.5</span><span class="hl std">)</span> <span class="hl opt">+</span>
<span class="hl kwd">geom_vline</span><span class="hl std">(</span><span class="hl kwc">alpha</span><span class="hl std">=</span><span class="hl num">.2</span><span class="hl std">,</span> <span class="hl kwc">size</span><span class="hl std">=</span><span class="hl num">1</span><span class="hl std">,</span> <span class="hl kwd">aes</span><span class="hl std">(</span><span class="hl kwc">xintercept</span> <span class="hl std">= model_pred,</span> <span class="hl kwc">linetype</span><span class="hl std">=</span><span class="hl str">"Model's prediction"</span><span class="hl std">))</span><span class="hl opt">+</span>
<span class="hl kwd">geom_vline</span><span class="hl std">(</span><span class="hl kwc">alpha</span><span class="hl std">=</span><span class="hl num">.2</span><span class="hl std">,</span> <span class="hl kwc">size</span><span class="hl std">=</span><span class="hl num">1</span><span class="hl std">,</span> <span class="hl kwd">aes</span><span class="hl std">(</span><span class="hl kwc">xintercept</span> <span class="hl std">= actual_price,</span> <span class="hl kwc">linetype</span><span class="hl std">=</span><span class="hl str">"Actual price"</span><span class="hl std">))</span><span class="hl opt">+</span>
<span class="hl kwd">scale_fill_manual</span><span class="hl std">(</span><span class="hl kwc">guide</span> <span class="hl std">=</span> <span class="hl num">FALSE</span><span class="hl std">,</span><span class="hl kwc">values</span><span class="hl std">=my_pallete)</span> <span class="hl opt">+</span> <span class="hl kwd">scale_colour_manual</span><span class="hl std">(</span><span class="hl kwc">guide</span> <span class="hl std">=</span> <span class="hl num">FALSE</span><span class="hl std">,</span> <span class="hl kwc">values</span><span class="hl std">=my_pallete)</span><span class="hl opt">+</span>
<span class="hl kwd">facet_grid</span><span class="hl std">(</span><span class="hl kwd">vars</span><span class="hl std">(condition),</span> <span class="hl kwd">vars</span><span class="hl std">(q_id),</span> <span class="hl kwc">scale</span><span class="hl std">=</span><span class="hl str">'free'</span><span class="hl std">)</span><span class="hl opt">+</span>
<span class="hl kwd">xlab</span><span class="hl std">(</span><span class="hl str">"Participants' price prediction"</span><span class="hl std">)</span><span class="hl opt">+</span>
<span class="hl kwd">ylab</span><span class="hl std">(</span><span class="hl str">"Number of participants"</span><span class="hl std">)</span><span class="hl opt">+</span>
<span class="hl kwd">scale_x_continuous</span><span class="hl std">(</span><span class="hl kwc">breaks</span> <span class="hl std">= breaks,</span> <span class="hl kwc">labels</span> <span class="hl std">=</span> <span class="hl kwd">sprintf</span><span class="hl std">(</span><span class="hl str">'$%dM'</span><span class="hl std">, breaks))</span> <span class="hl opt">+</span>
<span class="hl kwd">theme</span><span class="hl std">(</span><span class="hl kwc">panel.grid.major</span> <span class="hl std">=</span> <span class="hl kwd">element_blank</span><span class="hl std">(),</span> <span class="hl kwc">panel.grid.minor</span> <span class="hl std">=</span> <span class="hl kwd">element_blank</span><span class="hl std">(),</span>
<span class="hl kwc">legend.position</span><span class="hl std">=</span><span class="hl str">"top"</span><span class="hl std">,</span> <span class="hl kwc">legend.title</span> <span class="hl std">=</span> <span class="hl kwd">element_blank</span><span class="hl std">(),</span> <span class="hl kwc">legend.key</span> <span class="hl std">=</span> <span class="hl kwd">element_rect</span><span class="hl std">(</span><span class="hl kwc">size</span> <span class="hl std">=</span> <span class="hl num">10</span><span class="hl std">),</span> <span class="hl kwc">legend.key.size</span> <span class="hl std">=</span> <span class="hl kwd">unit</span><span class="hl std">(</span><span class="hl num">1.5</span><span class="hl std">,</span> <span class="hl str">'lines'</span><span class="hl std">),</span> <span class="hl kwc">legend.text</span><span class="hl std">=</span><span class="hl kwd">element_text</span><span class="hl std">(</span><span class="hl kwc">size</span><span class="hl std">=</span><span class="hl num">12</span><span class="hl std">),</span>
<span class="hl kwc">axis.title.x</span> <span class="hl std">=</span> <span class="hl kwd">element_text</span><span class="hl std">(</span><span class="hl kwc">size</span><span class="hl std">=</span><span class="hl num">20</span><span class="hl std">),</span> <span class="hl kwc">axis.title.y</span> <span class="hl std">=</span> <span class="hl kwd">element_text</span><span class="hl std">(</span><span class="hl kwc">size</span><span class="hl std">=</span><span class="hl num">18</span><span class="hl std">),</span> <span class="hl kwc">axis.text.x</span> <span class="hl std">=</span> <span class="hl kwd">element_text</span><span class="hl std">(</span><span class="hl kwc">angle</span> <span class="hl std">=</span> <span class="hl num">45</span><span class="hl std">,</span> <span class="hl kwc">size</span><span class="hl std">=</span><span class="hl num">10</span><span class="hl std">),</span> <span class="hl kwc">axis.text.y</span> <span class="hl std">=</span> <span class="hl kwd">element_text</span><span class="hl std">(</span><span class="hl kwc">size</span><span class="hl std">=</span><span class="hl num">14</span><span class="hl std">),</span>
<span class="hl kwc">strip.text</span> <span class="hl std">=</span> <span class="hl kwd">element_text</span><span class="hl std">(</span><span class="hl kwc">size</span><span class="hl std">=</span><span class="hl num">14</span><span class="hl std">))</span>
</pre></div>
</div><div class="rimage center"><img src="figure/analyze-experiment-1-Rhtmlauto-report-3.png" title="plot of chunk auto-report" alt="plot of chunk auto-report" class="plot" /></div><div class="rcode">
<div class="source"><pre class="knitr r"><span class="hl kwd">ggsave</span><span class="hl std">(</span><span class="hl kwc">filename</span> <span class="hl std">=</span> <span class="hl str">"figures/exp1/dist_final_prediction_1.pdf"</span><span class="hl std">,</span> <span class="hl kwc">height</span><span class="hl std">=</span><span class="hl num">10</span><span class="hl std">,</span> <span class="hl kwc">width</span><span class="hl std">=</span><span class="hl num">10</span><span class="hl std">)</span>
<span class="hl kwd">ggplot</span><span class="hl std">(responses2,</span> <span class="hl kwd">aes</span><span class="hl std">(</span><span class="hl kwc">x</span> <span class="hl std">= final_pred,</span> <span class="hl kwc">fill</span><span class="hl std">=condition))</span> <span class="hl opt">+</span>
<span class="hl kwd">geom_histogram</span><span class="hl std">(</span><span class="hl kwc">binwidth</span> <span class="hl std">=</span> <span class="hl num">0.1</span><span class="hl std">,</span> <span class="hl kwc">alpha</span> <span class="hl std">=</span> <span class="hl num">0.5</span><span class="hl std">)</span> <span class="hl opt">+</span>
<span class="hl kwd">geom_vline</span><span class="hl std">(</span><span class="hl kwc">alpha</span><span class="hl std">=</span><span class="hl num">.2</span><span class="hl std">,</span> <span class="hl kwc">size</span><span class="hl std">=</span><span class="hl num">1</span><span class="hl std">,</span> <span class="hl kwd">aes</span><span class="hl std">(</span><span class="hl kwc">xintercept</span> <span class="hl std">= model_pred,</span> <span class="hl kwc">linetype</span><span class="hl std">=</span><span class="hl str">"Model's prediction"</span><span class="hl std">))</span><span class="hl opt">+</span>
<span class="hl kwd">geom_vline</span><span class="hl std">(</span><span class="hl kwc">alpha</span><span class="hl std">=</span><span class="hl num">.2</span><span class="hl std">,</span> <span class="hl kwc">size</span><span class="hl std">=</span><span class="hl num">1</span><span class="hl std">,</span> <span class="hl kwd">aes</span><span class="hl std">(</span><span class="hl kwc">xintercept</span> <span class="hl std">= actual_price,</span> <span class="hl kwc">linetype</span><span class="hl std">=</span><span class="hl str">"Actual price"</span><span class="hl std">))</span><span class="hl opt">+</span>
<span class="hl kwd">scale_fill_manual</span><span class="hl std">(</span><span class="hl kwc">guide</span> <span class="hl std">=</span> <span class="hl num">FALSE</span><span class="hl std">,</span><span class="hl kwc">values</span><span class="hl std">=my_pallete)</span> <span class="hl opt">+</span> <span class="hl kwd">scale_colour_manual</span><span class="hl std">(</span><span class="hl kwc">guide</span> <span class="hl std">=</span> <span class="hl num">FALSE</span><span class="hl std">,</span> <span class="hl kwc">values</span><span class="hl std">=my_pallete)</span><span class="hl opt">+</span>
<span class="hl kwd">facet_grid</span><span class="hl std">(</span><span class="hl kwd">vars</span><span class="hl std">(condition),</span> <span class="hl kwd">vars</span><span class="hl std">(q_id),</span> <span class="hl kwc">scale</span><span class="hl std">=</span><span class="hl str">'free'</span><span class="hl std">)</span><span class="hl opt">+</span>
<span class="hl kwd">xlab</span><span class="hl std">(</span><span class="hl str">"Participants' price prediction"</span><span class="hl std">)</span><span class="hl opt">+</span>
<span class="hl kwd">ylab</span><span class="hl std">(</span><span class="hl str">"Number of participants"</span><span class="hl std">)</span><span class="hl opt">+</span>
<span class="hl kwd">scale_x_continuous</span><span class="hl std">(</span><span class="hl kwc">breaks</span> <span class="hl std">= breaks,</span> <span class="hl kwc">labels</span> <span class="hl std">=</span> <span class="hl kwd">sprintf</span><span class="hl std">(</span><span class="hl str">'$%dM'</span><span class="hl std">, breaks))</span> <span class="hl opt">+</span>
<span class="hl kwd">theme</span><span class="hl std">(</span><span class="hl kwc">panel.grid.major</span> <span class="hl std">=</span> <span class="hl kwd">element_blank</span><span class="hl std">(),</span> <span class="hl kwc">panel.grid.minor</span> <span class="hl std">=</span> <span class="hl kwd">element_blank</span><span class="hl std">(),</span>
<span class="hl kwc">legend.position</span><span class="hl std">=</span><span class="hl str">"top"</span><span class="hl std">,</span> <span class="hl kwc">legend.title</span> <span class="hl std">=</span> <span class="hl kwd">element_blank</span><span class="hl std">(),</span> <span class="hl kwc">legend.key</span> <span class="hl std">=</span> <span class="hl kwd">element_rect</span><span class="hl std">(</span><span class="hl kwc">size</span> <span class="hl std">=</span> <span class="hl num">10</span><span class="hl std">),</span> <span class="hl kwc">legend.key.size</span> <span class="hl std">=</span> <span class="hl kwd">unit</span><span class="hl std">(</span><span class="hl num">1.5</span><span class="hl std">,</span> <span class="hl str">'lines'</span><span class="hl std">),</span> <span class="hl kwc">legend.text</span><span class="hl std">=</span><span class="hl kwd">element_text</span><span class="hl std">(</span><span class="hl kwc">size</span><span class="hl std">=</span><span class="hl num">12</span><span class="hl std">),</span>
<span class="hl kwc">axis.title.x</span> <span class="hl std">=</span> <span class="hl kwd">element_text</span><span class="hl std">(</span><span class="hl kwc">size</span><span class="hl std">=</span><span class="hl num">20</span><span class="hl std">),</span> <span class="hl kwc">axis.title.y</span> <span class="hl std">=</span> <span class="hl kwd">element_text</span><span class="hl std">(</span><span class="hl kwc">size</span><span class="hl std">=</span><span class="hl num">18</span><span class="hl std">),</span> <span class="hl kwc">axis.text.x</span> <span class="hl std">=</span> <span class="hl kwd">element_text</span><span class="hl std">(</span><span class="hl kwc">angle</span> <span class="hl std">=</span> <span class="hl num">45</span><span class="hl std">,</span> <span class="hl kwc">size</span><span class="hl std">=</span><span class="hl num">10</span><span class="hl std">),</span> <span class="hl kwc">axis.text.y</span> <span class="hl std">=</span> <span class="hl kwd">element_text</span><span class="hl std">(</span><span class="hl kwc">size</span><span class="hl std">=</span><span class="hl num">14</span><span class="hl std">),</span>
<span class="hl kwc">strip.text</span> <span class="hl std">=</span> <span class="hl kwd">element_text</span><span class="hl std">(</span><span class="hl kwc">size</span><span class="hl std">=</span><span class="hl num">14</span><span class="hl std">))</span>
</pre></div>
<div class="warning"><pre class="knitr r">## Warning: Removed 1250 rows containing missing values (geom_vline).
</pre></div>
</div><div class="rimage center"><img src="figure/analyze-experiment-1-Rhtmlauto-report-4.png" title="plot of chunk auto-report" alt="plot of chunk auto-report" class="plot" /></div><div class="rcode">
<div class="source"><pre class="knitr r"><span class="hl kwd">ggsave</span><span class="hl std">(</span><span class="hl kwc">filename</span> <span class="hl std">=</span> <span class="hl str">"figures/exp1/dist_final_prediction_2.pdf"</span><span class="hl std">,</span> <span class="hl kwc">height</span><span class="hl std">=</span><span class="hl num">10</span><span class="hl std">,</span> <span class="hl kwc">width</span><span class="hl std">=</span><span class="hl num">10</span><span class="hl std">)</span>
</pre></div>
<div class="warning"><pre class="knitr r">## Warning: Removed 1250 rows containing missing values (geom_vline).
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl com">#########################################</span>
<span class="hl com"># hypotheses analyses and plots</span>
<span class="hl com">#########################################</span>
<span class="hl com"># limit everything to first 10 questions for now</span>
<span class="hl std">responses_normal</span> <span class="hl kwb"><-</span> <span class="hl kwd">filter</span><span class="hl std">(responses, q_id</span> <span class="hl opt"><</span> <span class="hl num">10</span><span class="hl std">)</span>
<span class="hl com"># limit to the four primary conditions</span>
<span class="hl com"># add transparency and number of features factors</span>
<span class="hl std">model_data</span> <span class="hl kwb"><-</span> <span class="hl kwd">filter</span><span class="hl std">(responses_normal, condition</span> <span class="hl opt">!=</span> <span class="hl str">"NO-MODEL"</span><span class="hl std">)</span> <span class="hl opt">%>%</span>
<span class="hl kwd">mutate</span><span class="hl std">(</span><span class="hl kwc">transparency</span> <span class="hl std">=</span> <span class="hl kwd">ifelse</span><span class="hl std">(condition</span> <span class="hl opt">==</span> <span class="hl str">"CLEAR-2"</span> <span class="hl opt">|</span> <span class="hl std">condition</span> <span class="hl opt">==</span> <span class="hl str">"CLEAR-8"</span><span class="hl std">,</span> <span class="hl str">"CLEAR"</span><span class="hl std">,</span> <span class="hl str">"BB"</span><span class="hl std">))</span> <span class="hl opt">%>%</span>
<span class="hl kwd">mutate</span><span class="hl std">(</span><span class="hl kwc">num_features</span> <span class="hl std">=</span> <span class="hl kwd">ifelse</span><span class="hl std">(condition</span> <span class="hl opt">==</span> <span class="hl str">"CLEAR-2"</span> <span class="hl opt">|</span> <span class="hl std">condition</span> <span class="hl opt">==</span> <span class="hl str">"BB-2"</span><span class="hl std">,</span> <span class="hl str">"two"</span><span class="hl std">,</span> <span class="hl str">"eight"</span><span class="hl std">))</span>
<span class="hl com">########################################</span>
<span class="hl com"># DEVIATION FROM THE MODEL</span>
<span class="hl com">########################################</span>
<span class="hl com"># fit the two factor model</span>
<span class="hl std">hlm_model</span> <span class="hl kwb"><-</span> <span class="hl kwd">lmer</span><span class="hl std">(deviation</span> <span class="hl opt">~</span> <span class="hl std">transparency</span><span class="hl opt">*</span><span class="hl std">num_features</span> <span class="hl opt">+</span> <span class="hl std">(</span><span class="hl num">1</span><span class="hl opt">|</span><span class="hl std">worker_id),</span> <span class="hl kwc">data</span><span class="hl std">=model_data)</span>
<span class="hl kwd">summary</span><span class="hl std">(hlm_model)</span>
</pre></div>
<div class="output"><pre class="knitr r">## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: deviation ~ transparency * num_features + (1 | worker_id)
## Data: model_data
##
## REML criterion at convergence: -11263.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.8859 -0.5903 -0.1514 0.4332 8.3083
##
## Random effects:
## Groups Name Variance Std.Dev.
## worker_id (Intercept) 0.004361 0.06604
## Residual 0.016599 0.12884
## Number of obs: 9980, groups: worker_id, 998
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.150547 0.004850 994.000012 31.044 <2e-16 ***
## transparencyCLEAR 0.013016 0.006920 994.000000 1.881 0.0603 .
## num_featurestwo -0.006053 0.006920 994.000000 -0.875 0.3820
## transparencyCLEAR:num_featurestwo -0.002308 0.009826 993.999995 -0.235 0.8143
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trCLEAR nm_ftr
## trnsprCLEAR -0.701
## num_fetrstw -0.701 0.491
## trnsCLEAR:_ 0.494 -0.704 -0.704
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl com"># look at ANOVA</span>
<span class="hl std">anova_table</span> <span class="hl kwb"><-</span> <span class="hl kwd">anova</span><span class="hl std">(hlm_model)</span>
<span class="hl com"># export in LaTEX format</span>
<span class="hl kwd">xtable</span><span class="hl std">(anova_table)</span>
</pre></div>
<div class="output"><pre class="knitr r">## % latex table generated in R 3.5.1 by xtable 1.8-2 package
## % Fri Jan 8 17:58:09 2021
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrrrrrr}
## \hline
## & Sum Sq & Mean Sq & NumDF & DenDF & F value & Pr($>$F) \\
## \hline
## transparency & 0.10 & 0.10 & 1.00 & 994.00 & 5.83 & 0.0159 \\
## num\_features & 0.04 & 0.04 & 1.00 & 994.00 & 2.15 & 0.1427 \\
## transparency:num\_features & 0.00 & 0.00 & 1.00 & 994.00 & 0.06 & 0.8143 \\
## \hline
## \end{tabular}
## \end{table}
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl com"># get the marginal means for all combinations of transparency and number of features</span>
<span class="hl std">means</span> <span class="hl kwb"><-</span> <span class="hl kwd">emmeans</span><span class="hl std">(hlm_model,</span> <span class="hl kwd">c</span><span class="hl std">(</span><span class="hl str">"transparency"</span><span class="hl std">,</span> <span class="hl str">"num_features"</span><span class="hl std">))</span>
<span class="hl com"># setup contrasts</span>
<span class="hl std">clist</span> <span class="hl kwb"><-</span> <span class="hl kwd">list</span><span class="hl std">(</span><span class="hl kwc">clear2_vs_bb8</span> <span class="hl std">=</span> <span class="hl kwd">c</span><span class="hl std">(</span><span class="hl opt">-</span><span class="hl num">1</span><span class="hl std">,</span> <span class="hl num">0</span><span class="hl std">,</span> <span class="hl num">0</span><span class="hl std">,</span> <span class="hl num">1</span><span class="hl std">))</span>
<span class="hl std">emmeans</span><span class="hl opt">::</span><span class="hl kwd">contrast</span><span class="hl std">(means, clist)</span>
</pre></div>
<div class="output"><pre class="knitr r">## contrast estimate SE df t.ratio p.value
## clear2_vs_bb8 0.00465 0.00691 994 0.673 0.5009
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl com"># setup contrasts</span>
<span class="hl std">clist</span> <span class="hl kwb"><-</span> <span class="hl kwd">list</span><span class="hl std">(</span><span class="hl kwc">clear8_vs_else</span> <span class="hl std">=</span> <span class="hl kwd">c</span><span class="hl std">(</span><span class="hl opt">-</span><span class="hl num">1</span><span class="hl std">,</span> <span class="hl num">3</span><span class="hl std">,</span> <span class="hl opt">-</span><span class="hl num">1</span><span class="hl std">,</span> <span class="hl opt">-</span><span class="hl num">1</span><span class="hl std">))</span>
<span class="hl std">emmeans</span><span class="hl opt">::</span><span class="hl kwd">contrast</span><span class="hl std">(means, clist)</span>
</pre></div>
<div class="output"><pre class="knitr r">## contrast estimate SE df t.ratio p.value
## clear8_vs_else 0.0404 0.0171 994 2.369 0.0180
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl com"># setup contrasts</span>
<span class="hl std">clist</span> <span class="hl kwb"><-</span> <span class="hl kwd">list</span><span class="hl std">(</span><span class="hl kwc">clear2_vs_else</span> <span class="hl std">=</span> <span class="hl kwd">c</span><span class="hl std">(</span><span class="hl opt">-</span><span class="hl num">1</span><span class="hl std">,</span> <span class="hl opt">-</span><span class="hl num">1</span><span class="hl std">,</span> <span class="hl opt">-</span><span class="hl num">1</span><span class="hl std">,</span> <span class="hl num">3</span><span class="hl std">))</span>
<span class="hl std">emmeans</span><span class="hl opt">::</span><span class="hl kwd">contrast</span><span class="hl std">(means, clist)</span>
</pre></div>
<div class="output"><pre class="knitr r">## contrast estimate SE df t.ratio p.value
## clear2_vs_else 0.007 0.0171 994 0.411 0.6815
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl std">model_data</span> <span class="hl opt">%>%</span>
<span class="hl kwd">mutate</span><span class="hl std">(</span><span class="hl kwc">outcome</span> <span class="hl std">= deviation</span> <span class="hl opt">*</span> <span class="hl num">1e3</span><span class="hl std">)</span> <span class="hl opt">%>%</span>
<span class="hl kwd">plot_distributions_with_means</span><span class="hl std">(.,</span> <span class="hl str">'Average deviation from the model'</span><span class="hl std">,</span> <span class="hl str">'Proportion of participants'</span><span class="hl std">, my_pallete)</span>
</pre></div>
<div class="warning"><pre class="knitr r">## Warning: Ignoring unknown aesthetics: x
</pre></div>
<div class="warning"><pre class="knitr r">## Warning: The plyr::rename operation has created duplicates for the following name(s):
## (`size`)
</pre></div>
<div class="message"><pre class="knitr r">## Picking joint bandwidth of 16.8
</pre></div>
</div><div class="rimage center"><img src="figure/analyze-experiment-1-Rhtmlauto-report-5.png" title="plot of chunk auto-report" alt="plot of chunk auto-report" class="plot" /></div><div class="rcode">
<div class="source"><pre class="knitr r"><span class="hl kwd">ggsave</span><span class="hl std">(</span><span class="hl kwc">file</span> <span class="hl std">=</span> <span class="hl str">'figures/exp1/dev_from_model.pdf'</span><span class="hl std">,</span> <span class="hl kwc">height</span> <span class="hl std">=</span> <span class="hl num">4</span><span class="hl std">,</span> <span class="hl kwc">width</span> <span class="hl std">=</span> <span class="hl num">4</span><span class="hl std">)</span>
</pre></div>
<div class="message"><pre class="knitr r">## Picking joint bandwidth of 16.8
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl com">########################################</span>
<span class="hl com"># Self-reported confidence</span>
<span class="hl com">########################################</span>
<span class="hl com"># fit the two factor model</span>
<span class="hl std">hlm_model</span> <span class="hl kwb"><-</span> <span class="hl kwd">lmer</span><span class="hl std">(model_conf</span> <span class="hl opt">~</span> <span class="hl std">transparency</span><span class="hl opt">*</span><span class="hl std">num_features</span> <span class="hl opt">+</span> <span class="hl std">(</span><span class="hl num">1</span><span class="hl opt">|</span><span class="hl std">worker_id),</span> <span class="hl kwc">data</span><span class="hl std">=model_data)</span>
<span class="hl kwd">summary</span><span class="hl std">(hlm_model)</span>
</pre></div>
<div class="output"><pre class="knitr r">## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: model_conf ~ transparency * num_features + (1 | worker_id)
## Data: model_data
##
## REML criterion at convergence: 28249.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6798 -0.5754 0.0235 0.6455 3.4948
##
## Random effects:
## Groups Name Variance Std.Dev.
## worker_id (Intercept) 0.3406 0.5836
## Residual 0.8433 0.9183
## Number of obs: 9980, groups: worker_id, 998
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.23906 0.04074 994.00001 79.499 <2e-16 ***
## transparencyCLEAR 0.12976 0.05814 994.00001 2.232 0.0258 *
## num_featurestwo -0.01437 0.05814 994.00001 -0.247 0.8049
## transparencyCLEAR:num_featurestwo 0.13264 0.08255 994.00000 1.607 0.1084
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trCLEAR nm_ftr
## trnsprCLEAR -0.701
## num_fetrstw -0.701 0.491
## trnsCLEAR:_ 0.494 -0.704 -0.704
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl com"># look at ANOVA</span>
<span class="hl std">anova_table</span> <span class="hl kwb"><-</span> <span class="hl kwd">anova</span><span class="hl std">(hlm_model)</span>
<span class="hl com"># export in LaTEX format</span>
<span class="hl kwd">xtable</span><span class="hl std">(anova_table)</span>
</pre></div>
<div class="output"><pre class="knitr r">## % latex table generated in R 3.5.1 by xtable 1.8-2 package
## % Fri Jan 8 17:58:10 2021
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrrrrrr}
## \hline
## & Sum Sq & Mean Sq & NumDF & DenDF & F value & Pr($>$F) \\
## \hline
## transparency & 19.03 & 19.03 & 1.00 & 994.00 & 22.57 & 0.0000 \\
## num\_features & 1.34 & 1.34 & 1.00 & 994.00 & 1.58 & 0.2084 \\
## transparency:num\_features & 2.18 & 2.18 & 1.00 & 994.00 & 2.58 & 0.1084 \\
## \hline
## \end{tabular}
## \end{table}
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl com"># get the marginal means for all combinations of transparency and number of features</span>
<span class="hl std">means</span> <span class="hl kwb"><-</span> <span class="hl kwd">emmeans</span><span class="hl std">(hlm_model,</span> <span class="hl kwd">c</span><span class="hl std">(</span><span class="hl str">"transparency"</span><span class="hl std">,</span> <span class="hl str">"num_features"</span><span class="hl std">))</span>
<span class="hl com"># setup contrasts</span>
<span class="hl std">clist</span> <span class="hl kwb"><-</span> <span class="hl kwd">list</span><span class="hl std">(</span><span class="hl kwc">clear2_vs_bb8</span> <span class="hl std">=</span> <span class="hl kwd">c</span><span class="hl std">(</span><span class="hl opt">-</span><span class="hl num">1</span><span class="hl std">,</span> <span class="hl num">0</span><span class="hl std">,</span> <span class="hl num">0</span><span class="hl std">,</span> <span class="hl num">1</span><span class="hl std">))</span>
<span class="hl std">emmeans</span><span class="hl opt">::</span><span class="hl kwd">contrast</span><span class="hl std">(means, clist)</span>
</pre></div>
<div class="output"><pre class="knitr r">## contrast estimate SE df t.ratio p.value
## clear2_vs_bb8 0.248 0.0581 994 4.270 <.0001
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl com"># effect sizes</span>
<span class="hl std">clear2_data</span> <span class="hl kwb"><-</span> <span class="hl kwd">filter</span><span class="hl std">(model_data, condition</span> <span class="hl opt">==</span> <span class="hl str">"CLEAR-2"</span><span class="hl std">)</span>
<span class="hl std">bb8_data</span> <span class="hl kwb"><-</span> <span class="hl kwd">filter</span><span class="hl std">(model_data, condition</span> <span class="hl opt">==</span> <span class="hl str">"BB-8"</span><span class="hl std">)</span>
<span class="hl std">effect_size</span> <span class="hl kwb"><-</span> <span class="hl kwd">cohensD</span><span class="hl std">(clear2_data</span><span class="hl opt">$</span><span class="hl std">model_conf, bb8_data</span><span class="hl opt">$</span><span class="hl std">model_conf)</span>
<span class="hl com"># compute and show the means and standard errors by condition from the model</span>
<span class="hl std">hlm_model</span> <span class="hl kwb"><-</span> <span class="hl kwd">lmer</span><span class="hl std">(model_conf</span> <span class="hl opt">~</span> <span class="hl std">condition</span> <span class="hl opt">+</span> <span class="hl std">(</span><span class="hl num">1</span> <span class="hl opt">|</span> <span class="hl std">worker_id),</span> <span class="hl kwc">data</span> <span class="hl std">= model_data)</span>
<span class="hl std">means</span> <span class="hl kwb"><-</span> <span class="hl kwd">lsmeansLT</span><span class="hl std">(hlm_model,</span> <span class="hl str">"condition"</span><span class="hl std">)</span>
<span class="hl kwd">print</span><span class="hl std">(means)</span>
</pre></div>
<div class="output"><pre class="knitr r">## Least Squares Means table:
##
## Estimate Std. Error df t value lower upper Pr(>|t|)
## conditionCLEAR-2 3.487097 0.041395 994 84.239 3.405864 3.568329 < 2.2e-16 ***
## conditionCLEAR-8 3.368826 0.041479 994 81.218 3.287429 3.450223 < 2.2e-16 ***
## conditionBB-2 3.224696 0.041479 994 77.743 3.143300 3.306093 < 2.2e-16 ***
## conditionBB-8 3.239062 0.040743 994 79.499 3.159109 3.319016 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Confidence level: 95%
## Degrees of freedom method: Satterthwaite
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl com">########################################</span>
<span class="hl com"># Simulation error</span>
<span class="hl com">########################################</span>
<span class="hl com"># fit the two factor model</span>
<span class="hl std">hlm_model</span> <span class="hl kwb"><-</span> <span class="hl kwd">lmer</span><span class="hl std">(sim_err</span> <span class="hl opt">~</span> <span class="hl std">transparency</span><span class="hl opt">*</span><span class="hl std">num_features</span> <span class="hl opt">+</span> <span class="hl std">(</span><span class="hl num">1</span><span class="hl opt">|</span><span class="hl std">worker_id),</span> <span class="hl kwc">data</span><span class="hl std">=model_data)</span>
<span class="hl kwd">summary</span><span class="hl std">(hlm_model)</span>
</pre></div>
<div class="output"><pre class="knitr r">## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: sim_err ~ transparency * num_features + (1 | worker_id)
## Data: model_data
##
## REML criterion at convergence: -2319.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7392 -0.6144 -0.2247 0.3985 6.5875
##
## Random effects:
## Groups Name Variance Std.Dev.
## worker_id (Intercept) 0.009739 0.09869
## Residual 0.040975 0.20242
## Number of obs: 9980, groups: worker_id, 998
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.239492 0.007352 993.999988 32.576 < 2e-16 ***
## transparencyCLEAR 0.021560 0.010491 993.999997 2.055 0.04014 *
## num_featurestwo -0.033460 0.010491 993.999997 -3.189 0.00147 **
## transparencyCLEAR:num_featurestwo -0.095940 0.014896 994.000002 -6.441 1.85e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trCLEAR nm_ftr
## trnsprCLEAR -0.701
## num_fetrstw -0.701 0.491
## trnsCLEAR:_ 0.494 -0.704 -0.704
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl com"># look at ANOVA</span>
<span class="hl std">anova_table</span> <span class="hl kwb"><-</span> <span class="hl kwd">anova</span><span class="hl std">(hlm_model)</span>
<span class="hl com"># export in LaTEX format</span>
<span class="hl kwd">xtable</span><span class="hl std">(anova_table)</span>
</pre></div>
<div class="output"><pre class="knitr r">## % latex table generated in R 3.5.1 by xtable 1.8-2 package
## % Fri Jan 8 17:58:11 2021
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrrrrrr}
## \hline
## & Sum Sq & Mean Sq & NumDF & DenDF & F value & Pr($>$F) \\
## \hline
## transparency & 0.52 & 0.52 & 1.00 & 994.00 & 12.57 & 0.0004 \\
## num\_features & 4.90 & 4.90 & 1.00 & 994.00 & 119.54 & 0.0000 \\
## transparency:num\_features & 1.70 & 1.70 & 1.00 & 994.00 & 41.48 & 0.0000 \\
## \hline
## \end{tabular}
## \end{table}
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl com"># get the marginal means for all combinations of transparency and number of features</span>
<span class="hl std">means</span> <span class="hl kwb"><-</span> <span class="hl kwd">emmeans</span><span class="hl std">(hlm_model,</span> <span class="hl kwd">c</span><span class="hl std">(</span><span class="hl str">"transparency"</span><span class="hl std">,</span> <span class="hl str">"num_features"</span><span class="hl std">))</span>
<span class="hl com"># setup contrasts</span>
<span class="hl std">clist</span> <span class="hl kwb"><-</span> <span class="hl kwd">list</span><span class="hl std">(</span><span class="hl kwc">clear2_vs_else</span> <span class="hl std">=</span> <span class="hl kwd">c</span><span class="hl std">(</span><span class="hl opt">-</span><span class="hl num">1</span><span class="hl std">,</span> <span class="hl opt">-</span><span class="hl num">1</span><span class="hl std">,</span> <span class="hl opt">-</span><span class="hl num">1</span><span class="hl std">,</span> <span class="hl num">3</span><span class="hl std">))</span>
<span class="hl std">emmeans</span><span class="hl opt">::</span><span class="hl kwd">contrast</span><span class="hl std">(means, clist)</span>
</pre></div>
<div class="output"><pre class="knitr r">## contrast estimate SE df t.ratio p.value
## clear2_vs_else -0.312 0.0259 994 -12.055 <.0001
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl std">clist</span> <span class="hl kwb"><-</span> <span class="hl kwd">list</span><span class="hl std">(</span><span class="hl kwc">clear8_vs_else</span> <span class="hl std">=</span> <span class="hl kwd">c</span><span class="hl std">(</span><span class="hl opt">-</span><span class="hl num">1</span><span class="hl std">,</span> <span class="hl num">3</span><span class="hl std">,</span> <span class="hl opt">-</span><span class="hl num">1</span><span class="hl std">,</span> <span class="hl opt">-</span><span class="hl num">1</span><span class="hl std">))</span>
<span class="hl std">emmeans</span><span class="hl opt">::</span><span class="hl kwd">contrast</span><span class="hl std">(means, clist)</span>
</pre></div>
<div class="output"><pre class="knitr r">## contrast estimate SE df t.ratio p.value
## clear8_vs_else 0.206 0.0259 994 7.957 <.0001
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl std">model_data</span> <span class="hl opt">%>%</span>
<span class="hl kwd">mutate</span><span class="hl std">(</span><span class="hl kwc">outcome</span> <span class="hl std">= sim_err</span> <span class="hl opt">*</span> <span class="hl num">1e3</span><span class="hl std">)</span> <span class="hl opt">%>%</span>
<span class="hl kwd">plot_distributions_with_means</span><span class="hl std">(.,</span> <span class="hl str">'Average simulation error'</span><span class="hl std">,</span> <span class="hl str">'Proportion of participants'</span><span class="hl std">, my_pallete,</span> <span class="hl kwc">xlim_percentile</span> <span class="hl std">=</span> <span class="hl num">.95</span><span class="hl std">)</span>
</pre></div>
<div class="warning"><pre class="knitr r">## Warning: Ignoring unknown aesthetics: x
## Warning: The plyr::rename operation has created duplicates for the following name(s):
## (`size`)
</pre></div>
<div class="message"><pre class="knitr r">## Picking joint bandwidth of 30.6
</pre></div>
</div><div class="rimage center"><img src="figure/analyze-experiment-1-Rhtmlauto-report-6.png" title="plot of chunk auto-report" alt="plot of chunk auto-report" class="plot" /></div><div class="rcode">
<div class="source"><pre class="knitr r"><span class="hl kwd">ggsave</span><span class="hl std">(</span><span class="hl kwc">file</span> <span class="hl std">=</span> <span class="hl str">'figures/exp1/sim_error.pdf'</span><span class="hl std">,</span> <span class="hl kwc">height</span> <span class="hl std">=</span> <span class="hl num">4</span><span class="hl std">,</span> <span class="hl kwc">width</span> <span class="hl std">=</span> <span class="hl num">4</span><span class="hl std">)</span>
</pre></div>
<div class="message"><pre class="knitr r">## Picking joint bandwidth of 30.6
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl com">########################################</span>
<span class="hl com"># PREDICTION ERROR</span>
<span class="hl com">########################################</span>
<span class="hl com"># primary conditions</span>
<span class="hl std">hlm_model</span> <span class="hl kwb"><-</span> <span class="hl kwd">lmer</span><span class="hl std">(pred_err</span> <span class="hl opt">~</span> <span class="hl std">condition</span> <span class="hl opt">+</span> <span class="hl std">(</span><span class="hl num">1</span><span class="hl opt">|</span><span class="hl std">worker_id),</span> <span class="hl kwc">data</span><span class="hl std">=model_data)</span>
<span class="hl kwd">summary</span><span class="hl std">(hlm_model)</span>
</pre></div>
<div class="output"><pre class="knitr r">## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: pred_err ~ condition + (1 | worker_id)
## Data: model_data
##
## REML criterion at convergence: -3579.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.826 -0.666 -0.168 0.399 9.188
##
## Random effects:
## Groups Name Variance Std.Dev.
## worker_id (Intercept) 0.001736 0.04167
## Residual 0.039311 0.19827
## Number of obs: 9980, groups: worker_id, 998
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.329e-01 4.781e-03 9.940e+02 48.728 <2e-16 ***
## conditionCLEAR-8 1.507e-02 6.768e-03 9.940e+02 2.227 0.0262 *
## conditionBB-2 -6.763e-04 6.768e-03 9.940e+02 -0.100 0.9204
## conditionBB-8 1.510e-03 6.708e-03 9.940e+02 0.225 0.8220
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cCLEAR cnBB-2
## cndtCLEAR-8 -0.706
## conditnBB-2 -0.706 0.499
## conditnBB-8 -0.713 0.503 0.503
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl com"># look at ANOVA</span>
<span class="hl std">anova_table</span> <span class="hl kwb"><-</span> <span class="hl kwd">anova</span><span class="hl std">(hlm_model)</span>
<span class="hl com"># export in LaTEX format</span>
<span class="hl kwd">xtable</span><span class="hl std">(anova_table)</span>
</pre></div>
<div class="output"><pre class="knitr r">## % latex table generated in R 3.5.1 by xtable 1.8-2 package
## % Fri Jan 8 17:58:12 2021
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrrrrrr}
## \hline
## & Sum Sq & Mean Sq & NumDF & DenDF & F value & Pr($>$F) \\
## \hline
## condition & 0.29 & 0.10 & 3.00 & 994.00 & 2.43 & 0.0642 \\
## \hline
## \end{tabular}
## \end{table}
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl com"># get the marginal means for conditions</span>
<span class="hl std">means</span> <span class="hl kwb"><-</span> <span class="hl kwd">emmeans</span><span class="hl std">(hlm_model,</span> <span class="hl str">"condition"</span><span class="hl std">)</span>
<span class="hl com"># two-way anova</span>
<span class="hl std">hlm_model</span> <span class="hl kwb"><-</span> <span class="hl kwd">lmer</span><span class="hl std">(pred_err</span> <span class="hl opt">~</span> <span class="hl std">transparency</span> <span class="hl opt">*</span> <span class="hl std">num_features</span> <span class="hl opt">+</span> <span class="hl std">(</span><span class="hl num">1</span><span class="hl opt">|</span><span class="hl std">worker_id),</span> <span class="hl kwc">data</span><span class="hl std">=model_data)</span>
<span class="hl kwd">summary</span><span class="hl std">(hlm_model)</span>
</pre></div>
<div class="output"><pre class="knitr r">## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: pred_err ~ transparency * num_features + (1 | worker_id)
## Data: model_data
##
## REML criterion at convergence: -3579.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.826 -0.666 -0.168 0.399 9.188
##
## Random effects:
## Groups Name Variance Std.Dev.
## worker_id (Intercept) 0.001736 0.04167
## Residual 0.039311 0.19827
## Number of obs: 9980, groups: worker_id, 998
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.234453 0.004705 994.000022 49.829 <2e-16 ***
## transparencyCLEAR 0.013563 0.006714 994.000022 2.020 0.0437 *
## num_featurestwo -0.002186 0.006714 994.000022 -0.326 0.7448
## transparencyCLEAR:num_featurestwo -0.012887 0.009533 994.000022 -1.352 0.1768
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trCLEAR nm_ftr
## trnsprCLEAR -0.701
## num_fetrstw -0.701 0.491
## trnsCLEAR:_ 0.494 -0.704 -0.704
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl com"># look at ANOVA</span>
<span class="hl std">anova_table</span> <span class="hl kwb"><-</span> <span class="hl kwd">anova</span><span class="hl std">(hlm_model)</span>
<span class="hl com"># export in LaTEX format</span>
<span class="hl kwd">xtable</span><span class="hl std">(anova_table)</span>
</pre></div>
<div class="output"><pre class="knitr r">## % latex table generated in R 3.5.1 by xtable 1.8-2 package
## % Fri Jan 8 17:58:13 2021
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrrrrrr}
## \hline
## & Sum Sq & Mean Sq & NumDF & DenDF & F value & Pr($>$F) \\
## \hline
## transparency & 0.09 & 0.09 & 1.00 & 994.00 & 2.23 & 0.1356 \\
## num\_features & 0.13 & 0.13 & 1.00 & 994.00 & 3.28 & 0.0705 \\
## transparency:num\_features & 0.07 & 0.07 & 1.00 & 994.00 & 1.83 & 0.1768 \\
## \hline
## \end{tabular}
## \end{table}
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl com"># setup contrasts</span>
<span class="hl std">means</span> <span class="hl kwb"><-</span> <span class="hl kwd">emmeans</span><span class="hl std">(hlm_model,</span> <span class="hl kwd">c</span><span class="hl std">(</span><span class="hl str">"transparency"</span><span class="hl std">,</span><span class="hl str">"num_features"</span><span class="hl std">))</span>
<span class="hl std">clist</span> <span class="hl kwb"><-</span> <span class="hl kwd">list</span><span class="hl std">(</span><span class="hl kwc">clear8_vs_else</span> <span class="hl std">=</span> <span class="hl kwd">c</span><span class="hl std">(</span><span class="hl opt">-</span><span class="hl num">1</span><span class="hl std">,</span> <span class="hl num">3</span><span class="hl std">,</span> <span class="hl opt">-</span><span class="hl num">1</span><span class="hl std">,</span> <span class="hl opt">-</span><span class="hl num">1</span><span class="hl std">))</span>
<span class="hl std">emmeans</span><span class="hl opt">::</span><span class="hl kwd">contrast</span><span class="hl std">(means, clist)</span>
</pre></div>
<div class="output"><pre class="knitr r">## contrast estimate SE df t.ratio p.value
## clear8_vs_else 0.0444 0.0166 994 2.679 0.0075
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl com"># all conditions including baseline</span>
<span class="hl std">model_baseline_data</span> <span class="hl kwb"><-</span> <span class="hl kwd">filter</span><span class="hl std">(responses_normal, q_id</span> <span class="hl opt"><</span> <span class="hl num">10</span><span class="hl std">)</span>
<span class="hl com"># fit the one factor model</span>
<span class="hl std">hlm_model</span> <span class="hl kwb"><-</span> <span class="hl kwd">lmer</span><span class="hl std">(pred_err</span> <span class="hl opt">~</span> <span class="hl std">condition</span> <span class="hl opt">+</span> <span class="hl std">(</span><span class="hl num">1</span><span class="hl opt">|</span><span class="hl std">worker_id),</span> <span class="hl kwc">data</span><span class="hl std">=model_baseline_data)</span>
<span class="hl kwd">summary</span><span class="hl std">(hlm_model)</span>
</pre></div>
<div class="output"><pre class="knitr r">## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: pred_err ~ condition + (1 | worker_id)
## Data: model_baseline_data
##
## REML criterion at convergence: -2062
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1061 -0.6328 -0.1638 0.3845 8.1336
##
## Random effects:
## Groups Name Variance Std.Dev.
## worker_id (Intercept) 0.002828 0.05318
## Residual 0.047227 0.21732
## Number of obs: 12500, groups: worker_id, 1250
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.329e-01 5.518e-03 1.245e+03 42.216 <2e-16 ***
## conditionCLEAR-8 1.507e-02 7.811e-03 1.245e+03 1.930 0.0539 .
## conditionBB-2 -6.763e-04 7.811e-03 1.245e+03 -0.087 0.9310
## conditionBB-8 1.510e-03 7.742e-03 1.245e+03 0.195 0.8454
## conditionNO-MODEL 9.761e-02 7.772e-03 1.245e+03 12.559 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cCLEAR cnBB-2 cnBB-8
## cndtCLEAR-8 -0.706
## conditnBB-2 -0.706 0.499
## conditnBB-8 -0.713 0.503 0.503
## cndNO-MODEL -0.710 0.501 0.501 0.506
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl com"># look at ANOVA</span>
<span class="hl std">anova_table</span> <span class="hl kwb"><-</span> <span class="hl kwd">anova</span><span class="hl std">(hlm_model)</span>
<span class="hl com"># export in LaTEX format</span>
<span class="hl kwd">xtable</span><span class="hl std">(anova_table)</span>
</pre></div>
<div class="output"><pre class="knitr r">## % latex table generated in R 3.5.1 by xtable 1.8-2 package
## % Fri Jan 8 17:58:13 2021
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrrrrrr}
## \hline
## & Sum Sq & Mean Sq & NumDF & DenDF & F value & Pr($>$F) \\
## \hline
## condition & 11.30 & 2.82 & 4.00 & 1245.00 & 59.80 & 0.0000 \\
## \hline
## \end{tabular}
## \end{table}
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl com"># get the marginal means for conditions</span>
<span class="hl std">means</span> <span class="hl kwb"><-</span> <span class="hl kwd">emmeans</span><span class="hl std">(hlm_model,</span> <span class="hl str">"condition"</span><span class="hl std">)</span>
<span class="hl com"># setup contrasts</span>
<span class="hl std">clist</span> <span class="hl kwb"><-</span> <span class="hl kwd">list</span><span class="hl std">(</span><span class="hl kwc">model_vs_baseline</span> <span class="hl std">=</span> <span class="hl kwd">c</span><span class="hl std">(</span><span class="hl opt">-</span><span class="hl num">1</span><span class="hl std">,</span> <span class="hl opt">-</span><span class="hl num">1</span><span class="hl std">,</span> <span class="hl opt">-</span><span class="hl num">1</span><span class="hl std">,</span> <span class="hl opt">-</span><span class="hl num">1</span><span class="hl std">,</span> <span class="hl num">4</span><span class="hl std">))</span>
<span class="hl std">emmeans</span><span class="hl opt">::</span><span class="hl kwd">contrast</span><span class="hl std">(means, clist)</span>
</pre></div>
<div class="output"><pre class="knitr r">## contrast estimate SE df t.ratio p.value
## model_vs_baseline 0.375 0.0245 1245 15.284 <.0001
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl std">xbreaks</span> <span class="hl kwb"><-</span> <span class="hl kwd">c</span><span class="hl std">(</span><span class="hl num">0</span><span class="hl std">,</span> <span class="hl num">200</span><span class="hl std">,</span> <span class="hl num">400</span><span class="hl std">,</span> <span class="hl num">600</span><span class="hl std">)</span>
<span class="hl std">model_baseline_data</span> <span class="hl opt">%>%</span>
<span class="hl kwd">mutate</span><span class="hl std">(</span><span class="hl kwc">outcome</span> <span class="hl std">= pred_err</span> <span class="hl opt">*</span> <span class="hl num">1e3</span><span class="hl std">)</span> <span class="hl opt">%>%</span>
<span class="hl kwd">plot_distributions_with_means</span><span class="hl std">(.,</span> <span class="hl str">'Average prediction error'</span><span class="hl std">,</span> <span class="hl str">'Proportion of participants'</span><span class="hl std">, my_pallete,</span>
<span class="hl kwc">xbreaks</span> <span class="hl std">= xbreaks,</span> <span class="hl kwc">vertical_line</span> <span class="hl std">=</span> <span class="hl kwd">mean</span><span class="hl std">(model_baseline_data</span><span class="hl opt">$</span><span class="hl std">model_err)</span> <span class="hl opt">*</span> <span class="hl num">1e3</span><span class="hl std">,</span> <span class="hl kwc">xlim_percentile</span> <span class="hl std">=</span> <span class="hl num">0.95</span><span class="hl std">)</span>
</pre></div>
<div class="warning"><pre class="knitr r">## Warning: Ignoring unknown aesthetics: x
## Warning: The plyr::rename operation has created duplicates for the following name(s):
## (`size`)
</pre></div>
<div class="message"><pre class="knitr r">## Picking joint bandwidth of 19.7
</pre></div>
</div><div class="rimage center"><img src="figure/analyze-experiment-1-Rhtmlauto-report-7.png" title="plot of chunk auto-report" alt="plot of chunk auto-report" class="plot" /></div><div class="rcode">
<div class="source"><pre class="knitr r"><span class="hl kwd">ggsave</span><span class="hl std">(</span><span class="hl kwc">file</span> <span class="hl std">=</span> <span class="hl str">'figures/exp1/prediction_error.pdf'</span><span class="hl std">,</span> <span class="hl kwc">height</span> <span class="hl std">=</span> <span class="hl num">4.5</span><span class="hl std">,</span> <span class="hl kwc">width</span> <span class="hl std">=</span><span class="hl num">4</span><span class="hl std">)</span>
</pre></div>
<div class="message"><pre class="knitr r">## Picking joint bandwidth of 19.7
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl com">####################################</span>
<span class="hl com"># last two questions (unusual apartments)</span>
<span class="hl com">####################################</span>
<span class="hl std">responses_unusual</span> <span class="hl kwb"><-</span> <span class="hl kwd">filter</span><span class="hl std">(responses, q_id</span> <span class="hl opt">>=</span> <span class="hl num">10</span><span class="hl std">)</span>
<span class="hl com">########################################</span>
<span class="hl com"># deviation for q11</span>
<span class="hl com">########################################</span>
<span class="hl std">q11_data</span> <span class="hl kwb"><-</span> <span class="hl kwd">filter</span><span class="hl std">(responses_unusual, q_id</span> <span class="hl opt">==</span> <span class="hl num">10</span><span class="hl std">)</span>
<span class="hl com"># prediction error in no-model vs rest</span>
<span class="hl std">lm_model</span> <span class="hl kwb"><-</span> <span class="hl kwd">lm</span><span class="hl std">(pred_err</span> <span class="hl opt">~</span> <span class="hl std">condition,</span> <span class="hl kwc">data</span><span class="hl std">=q11_data)</span>
<span class="hl std">means</span> <span class="hl kwb"><-</span> <span class="hl kwd">emmeans</span><span class="hl std">(lm_model,</span> <span class="hl kwd">c</span><span class="hl std">(</span><span class="hl str">"condition"</span><span class="hl std">))</span>
<span class="hl std">clist</span> <span class="hl kwb"><-</span> <span class="hl kwd">list</span><span class="hl std">(</span><span class="hl kwc">baseline_vs_rest</span> <span class="hl std">=</span> <span class="hl kwd">c</span><span class="hl std">(</span><span class="hl opt">-</span><span class="hl num">1</span><span class="hl std">,</span> <span class="hl opt">-</span><span class="hl num">1</span><span class="hl std">,</span> <span class="hl opt">-</span><span class="hl num">1</span><span class="hl std">,</span> <span class="hl opt">-</span><span class="hl num">1</span><span class="hl std">,</span> <span class="hl num">4</span><span class="hl std">))</span>
<span class="hl std">emmeans</span><span class="hl opt">::</span><span class="hl kwd">contrast</span><span class="hl std">(means, clist)</span>
</pre></div>
<div class="output"><pre class="knitr r">## contrast estimate SE df t.ratio p.value
## baseline_vs_rest -0.488 0.0611 1245 -7.988 <.0001
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl com">#fit a model for deviation</span>
<span class="hl std">q11_data</span> <span class="hl kwb"><-</span> <span class="hl kwd">filter</span><span class="hl std">(q11_data, condition</span> <span class="hl opt">!=</span> <span class="hl str">"NO-MODEL"</span><span class="hl std">)</span> <span class="hl opt">%>%</span>
<span class="hl kwd">mutate</span><span class="hl std">(</span><span class="hl kwc">transparency</span> <span class="hl std">=</span> <span class="hl kwd">ifelse</span><span class="hl std">(condition</span> <span class="hl opt">==</span> <span class="hl str">"CLEAR-2"</span> <span class="hl opt">|</span> <span class="hl std">condition</span> <span class="hl opt">==</span> <span class="hl str">"CLEAR-8"</span><span class="hl std">,</span> <span class="hl str">"CLEAR"</span><span class="hl std">,</span> <span class="hl str">"BB"</span><span class="hl std">))</span> <span class="hl opt">%>%</span>
<span class="hl kwd">mutate</span><span class="hl std">(</span><span class="hl kwc">num_features</span> <span class="hl std">=</span> <span class="hl kwd">ifelse</span><span class="hl std">(condition</span> <span class="hl opt">==</span> <span class="hl str">"CLEAR-2"</span> <span class="hl opt">|</span> <span class="hl std">condition</span> <span class="hl opt">==</span> <span class="hl str">"BB-2"</span><span class="hl std">,</span> <span class="hl str">"two"</span><span class="hl std">,</span> <span class="hl str">"eight"</span><span class="hl std">))</span>
<span class="hl com"># two-way anova</span>
<span class="hl std">q11_anova</span> <span class="hl kwb"><-</span> <span class="hl kwd">aov</span><span class="hl std">(pred_err</span> <span class="hl opt">~</span> <span class="hl std">transparency</span><span class="hl opt">*</span><span class="hl std">num_features,</span> <span class="hl kwc">data</span><span class="hl std">=q11_data)</span>
<span class="hl kwd">summary</span><span class="hl std">(q11_anova)</span>
</pre></div>
<div class="output"><pre class="knitr r">## Df Sum Sq Mean Sq F value Pr(>F)
## transparency 1 1.17 1.1745 31.983 2.03e-08 ***
## num_features 1 1.66 1.6596 45.194 3.00e-11 ***
## transparency:num_features 1 0.00 0.0046 0.126 0.723
## Residuals 994 36.50 0.0367
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl com"># fit the one factor model</span>
<span class="hl std">lm_model</span> <span class="hl kwb"><-</span> <span class="hl kwd">lm</span><span class="hl std">(deviation</span> <span class="hl opt">~</span> <span class="hl std">condition,</span> <span class="hl kwc">data</span><span class="hl std">=q11_data)</span>
<span class="hl kwd">summary</span><span class="hl std">(lm_model)</span>
</pre></div>
<div class="output"><pre class="knitr r">##
## Call:
## lm(formula = deviation ~ condition, data = q11_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.16640 -0.06640 -0.04575 0.04492 1.35425
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1560484 0.0083064 18.787 <2e-16 ***
## conditionCLEAR-8 -0.0102994 0.0117589 -0.876 0.381
## conditionBB-2 0.0103484 0.0117589 0.880 0.379
## conditionBB-8 -0.0009703 0.0116549 -0.083 0.934
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1308 on 994 degrees of freedom
## Multiple R-squared: 0.003097, Adjusted R-squared: 8.814e-05
## F-statistic: 1.029 on 3 and 994 DF, p-value: 0.3788
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl com"># look at one-way ANOVA</span>
<span class="hl kwd">anova</span><span class="hl std">(lm_model)</span>
</pre></div>
<div class="output"><pre class="knitr r">## Analysis of Variance Table
##
## Response: deviation
## Df Sum Sq Mean Sq F value Pr(>F)
## condition 3 0.0528 0.017612 1.0293 0.3788
## Residuals 994 17.0085 0.017111
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl com"># run 2-by-2 anova for deviation on q11</span>
<span class="hl com"># fit the two factor model</span>
<span class="hl std">lm_model</span> <span class="hl kwb"><-</span> <span class="hl kwd">lm</span><span class="hl std">(deviation</span> <span class="hl opt">~</span> <span class="hl std">transparency</span><span class="hl opt">*</span><span class="hl std">num_features,</span> <span class="hl kwc">data</span><span class="hl std">=q11_data)</span>
<span class="hl kwd">summary</span><span class="hl std">(lm_model)</span>
</pre></div>
<div class="output"><pre class="knitr r">##
## Call:
## lm(formula = deviation ~ transparency * num_features, data = q11_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.16640 -0.06640 -0.04575 0.04492 1.35425
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.155078 0.008176 18.968 <2e-16 ***
## transparencyCLEAR -0.009329 0.011667 -0.800 0.424
## num_featurestwo 0.011319 0.011667 0.970 0.332
## transparencyCLEAR:num_featurestwo -0.001019 0.016565 -0.062 0.951
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1308 on 994 degrees of freedom
## Multiple R-squared: 0.003097, Adjusted R-squared: 8.814e-05
## F-statistic: 1.029 on 3 and 994 DF, p-value: 0.3788
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl com"># look at ANOVA</span>
<span class="hl std">anova_table</span> <span class="hl kwb"><-</span> <span class="hl kwd">anova</span><span class="hl std">(lm_model)</span>
<span class="hl com"># export in LaTEX format</span>
<span class="hl kwd">xtable</span><span class="hl std">(anova_table)</span>
</pre></div>
<div class="output"><pre class="knitr r">## % latex table generated in R 3.5.1 by xtable 1.8-2 package
## % Fri Jan 8 17:58:14 2021
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrrrrr}
## \hline
## & Df & Sum Sq & Mean Sq & F value & Pr($>$F) \\
## \hline
## transparency & 1 & 0.02 & 0.02 & 1.38 & 0.2405 \\
## num\_features & 1 & 0.03 & 0.03 & 1.70 & 0.1920 \\
## transparency:num\_features & 1 & 0.00 & 0.00 & 0.00 & 0.9509 \\
## Residuals & 994 & 17.01 & 0.02 & & \\
## \hline
## \end{tabular}
## \end{table}
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl com"># get the marginal means for all combinations of transparency and number of features</span>
<span class="hl std">means</span> <span class="hl kwb"><-</span> <span class="hl kwd">emmeans</span><span class="hl std">(lm_model,</span> <span class="hl kwd">c</span><span class="hl std">(</span><span class="hl str">"transparency"</span><span class="hl std">,</span> <span class="hl str">"num_features"</span><span class="hl std">))</span>
<span class="hl com"># setup contrasts</span>
<span class="hl std">clist</span> <span class="hl kwb"><-</span> <span class="hl kwd">list</span><span class="hl std">(</span><span class="hl kwc">clear_vs_bb</span> <span class="hl std">=</span> <span class="hl kwd">c</span><span class="hl std">(</span><span class="hl opt">-</span><span class="hl num">1</span><span class="hl std">,</span> <span class="hl num">1</span><span class="hl std">,</span> <span class="hl opt">-</span><span class="hl num">1</span><span class="hl std">,</span> <span class="hl num">1</span><span class="hl std">))</span>
<span class="hl std">emmeans</span><span class="hl opt">::</span><span class="hl kwd">contrast</span><span class="hl std">(means, clist)</span>
</pre></div>
<div class="output"><pre class="knitr r">## contrast estimate SE df t.ratio p.value
## clear_vs_bb -0.0197 0.0166 994 -1.188 0.2351
</pre></div>
<div class="source"><pre class="knitr r"><span class="hl com">########################################</span>
<span class="hl com"># deviation for q12</span>
<span class="hl com">########################################</span>
<span class="hl com"># comparison of prediction error for baseline with the rest</span>
<span class="hl std">q12_data</span> <span class="hl kwb"><-</span> <span class="hl kwd">filter</span><span class="hl std">(responses_unusual, q_id</span> <span class="hl opt">==</span> <span class="hl num">11</span><span class="hl std">)</span>
<span class="hl com">#fit a model for deviation</span>
<span class="hl std">q12_data</span> <span class="hl kwb"><-</span> <span class="hl kwd">filter</span><span class="hl std">(q12_data, condition</span> <span class="hl opt">!=</span> <span class="hl str">"NO-MODEL"</span><span class="hl std">)</span> <span class="hl opt">%>%</span>
<span class="hl kwd">mutate</span><span class="hl std">(</span><span class="hl kwc">transparency</span> <span class="hl std">=</span> <span class="hl kwd">ifelse</span><span class="hl std">(condition</span> <span class="hl opt">==</span> <span class="hl str">"CLEAR-2"</span> <span class="hl opt">|</span> <span class="hl std">condition</span> <span class="hl opt">==</span> <span class="hl str">"CLEAR-8"</span><span class="hl std">,</span> <span class="hl str">"CLEAR"</span><span class="hl std">,</span> <span class="hl str">"BB"</span><span class="hl std">))</span> <span class="hl opt">%>%</span>
<span class="hl kwd">mutate</span><span class="hl std">(</span><span class="hl kwc">num_features</span> <span class="hl std">=</span> <span class="hl kwd">ifelse</span><span class="hl std">(condition</span> <span class="hl opt">==</span> <span class="hl str">"CLEAR-2"</span> <span class="hl opt">|</span> <span class="hl std">condition</span> <span class="hl opt">==</span> <span class="hl str">"BB-2"</span><span class="hl std">,</span> <span class="hl str">"two"</span><span class="hl std">,</span> <span class="hl str">"eight"</span><span class="hl std">))</span>