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‎.nojekyll‎

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‎bayesian.html‎

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‎lamp.html‎

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<section id="logging-in" class="level1" data-number="1">
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<h1 data-number="1"><span class="header-section-number">1</span> Logging In</h1>
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<p>To log in to the lab’s LAMP server, use <a href="https://winscp.net/eng/download.php">WinSCP</a>.</p>
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<p>To log in to the lab’s LAMP server, use <a href="https://winscp.net/eng/download.php"><code>WinSCP</code></a> or <a href="https://its.uiowa.edu/services/securecrt"><code>SecureCRT</code></a>.</p>
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<p>Our LAMP domain is: <code>pbslamp.divms.uiowa.edu</code></p>
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<p>Our credentials are in our LastPass account. You must be on the university network (or VPN) in order to connect to the server.</p>
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</section>
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<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a></span>
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<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="fu"># Logging In</span></span>
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<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a></span>
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<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a>To log in to the lab's LAMP server, use <span class="co">[</span><span class="ot">WinSCP</span><span class="co">](https://winscp.net/eng/download.php)</span>.</span>
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<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a>To log in to the lab's LAMP server, use <span class="co">[</span><span class="ot">`WinSCP`</span><span class="co">](https://winscp.net/eng/download.php)</span> or <span class="co">[</span><span class="ot">`SecureCRT`</span><span class="co">](https://its.uiowa.edu/services/securecrt)</span>.</span>
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<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a></span>
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<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a>Our LAMP domain is: <span class="in">`pbslamp.divms.uiowa.edu`</span></span>
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<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a></span>

‎regression.html‎

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<details open="" class="code-fold"><summary>Code</summary><div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb25"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/confint.html">confint</a></span><span class="op">(</span><span class="va">robustLinearRegression</span><span class="op">)</span></span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
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</details><div class="cell-output cell-output-stdout">
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<pre><code> 2.5 % 97.5 %
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(Intercept) 0.83801566 1.0499981
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(Intercept) 0.83801565 1.0499981
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bpi_antisocialT1Sum 0.44749135 0.5352118
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bpi_anxiousDepressedSum 0.09683728 0.2184779</code></pre>
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</div>
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Residuals (from reweighted LS):
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Min 1Q Median 3Q Max
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-3.9305 -0.9091 0.0000 0.9997 3.9036
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-3.8820 -0.8888 0.0000 1.0146 3.9168
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Coefficients:
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Estimate Std. Error t value Pr(&gt;|t|)
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Intercept 0.74932 0.04795 15.626 &lt; 2e-16 ***
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bpi_antisocialT1Sum 0.54338 0.01524 35.647 &lt; 2e-16 ***
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bpi_anxiousDepressedSum 0.13826 0.02343 5.901 4.06e-09 ***
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Intercept 0.74524 0.04813 15.485 &lt; 2e-16 ***
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bpi_antisocialT1Sum 0.55667 0.01528 36.428 &lt; 2e-16 ***
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bpi_anxiousDepressedSum 0.12006 0.02351 5.107 3.5e-07 ***
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---
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Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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Residual standard error: 1.534 on 2710 degrees of freedom
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Multiple R-Squared: 0.4121, Adjusted R-squared: 0.4116
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F-statistic: 949.7 on 2 and 2710 DF, p-value: &lt; 2.2e-16 </code></pre>
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Residual standard error: 1.541 on 2713 degrees of freedom
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Multiple R-Squared: 0.4158, Adjusted R-squared: 0.4153
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F-statistic: 965.4 on 2 and 2713 DF, p-value: &lt; 2.2e-16 </code></pre>
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</div>
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<details open="" class="code-fold"><summary>Code</summary><div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb31"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/confint.html">confint</a></span><span class="op">(</span><span class="va">robustLinearRegression</span><span class="op">)</span></span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
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</details><div class="cell-output cell-output-stdout">
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<pre><code> 2.5 % 97.5 %
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(Intercept) 0.83801566 1.0499981
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(Intercept) 0.83801565 1.0499981
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bpi_antisocialT1Sum 0.44749135 0.5352118
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bpi_anxiousDepressedSum 0.09683728 0.2184779</code></pre>
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</details><div class="cell-output-display">
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</details><div class="cell-output-display">
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{"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["R"],"name":[1],"type":["dbl"],"align":["right"]},{"label":["original"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["bootBias"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["bootSE"],"name":[4],"type":["dbl"],"align":["right"]},{"label":["bootMed"],"name":[5],"type":["dbl"],"align":["right"]}],"data":[{"1":"1000","2":"1.1983004","3":"-0.0005354575","4":"0.06274757","5":"1.1950994","_rn_":"(Intercept)"},{"1":"1000","2":"0.4655286","3":"0.0006145372","4":"0.02306453","5":"0.4670473","_rn_":"bpi_antisocialT1Sum"},{"1":"1000","2":"0.1607541","3":"-0.0001944043","4":"0.03245513","5":"0.1582765","_rn_":"bpi_anxiousDepressedSum"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
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{"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["R"],"name":[1],"type":["dbl"],"align":["right"]},{"label":["original"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["bootBias"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["bootSE"],"name":[4],"type":["dbl"],"align":["right"]},{"label":["bootMed"],"name":[5],"type":["dbl"],"align":["right"]}],"data":[{"1":"1000","2":"1.1983004","3":"-0.0010536520","4":"0.05948430","5":"1.1970503","_rn_":"(Intercept)"},{"1":"1000","2":"0.4655286","3":"0.0006930044","4":"0.02249255","5":"0.4659657","_rn_":"bpi_antisocialT1Sum"},{"1":"1000","2":"0.1607541","3":"0.0017068538","4":"0.03348742","5":"0.1621254","_rn_":"bpi_anxiousDepressedSum"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
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<pre><code>Bootstrap percent confidence intervals
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2.5 % 97.5 %
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(Intercept) 1.07812311 1.3258531
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bpi_antisocialT1Sum 0.42000249 0.5122183
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bpi_anxiousDepressedSum 0.09959739 0.2247719</code></pre>
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(Intercept) 1.08504718 1.3137404
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bpi_antisocialT1Sum 0.42121493 0.5113543
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bpi_anxiousDepressedSum 0.09480539 0.2277918</code></pre>
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</div>
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<details open="" class="code-fold"><summary>Code</summary><div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb157"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/hist.html">hist</a></span><span class="op">(</span><span class="va">multipleRegressionModelBootstrapped</span><span class="op">)</span></span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
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</details><div class="cell-output-display">
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<div data-pagedtable="false">
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{"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["R"],"name":[1],"type":["dbl"],"align":["right"]},{"label":["original"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["bootBias"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["bootSE"],"name":[4],"type":["dbl"],"align":["right"]},{"label":["bootMed"],"name":[5],"type":["dbl"],"align":["right"]}],"data":[{"1":"1000","2":"1.1983004","3":"-0.0030441683","4":"0.06463221","5":"1.1968790","_rn_":"(Intercept)"},{"1":"1000","2":"0.4655286","3":"0.0017775824","4":"0.02311647","5":"0.4674464","_rn_":"bpi_antisocialT1Sum"},{"1":"1000","2":"0.1607541","3":"-0.0009672998","4":"0.03348008","5":"0.1580222","_rn_":"bpi_anxiousDepressedSum"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
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<pre><code>Bootstrap percent confidence intervals
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2.5 % 97.5 %
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(Intercept) 1.07224898 1.3300424
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bpi_antisocialT1Sum 0.41911183 0.5118205
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bpi_anxiousDepressedSum 0.09758344 0.2285711</code></pre>
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(Intercept) 1.06869384 1.3218332
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bpi_antisocialT1Sum 0.42149924 0.5133955
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bpi_anxiousDepressedSum 0.09548221 0.2266754</code></pre>
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<details open="" class="code-fold"><summary>Code</summary><div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb161"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/hist.html">hist</a></span><span class="op">(</span><span class="va">generalizedRegressionModelBootstrapped</span><span class="op">)</span></span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
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<pre><code>
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10-fold CV results:
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Fit CV
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1 OLS 1.980629
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2 MM 1.026350
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3 LTS 1.007140
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1 OLS 1.980345
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2 MM 1.026726
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3 LTS 1.007151
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Best model:
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CV
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<div class="cell-output cell-output-stdout">
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<pre><code>$center
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bpi_antisocialT1Sum bpi_antisocialT2Sum
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2.056773 1.952882
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2.359537 2.258197
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$cov
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bpi_antisocialT1Sum bpi_antisocialT2Sum
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bpi_antisocialT1Sum 2.944663 1.910461
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bpi_antisocialT2Sum 1.910461 3.027803
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$msg
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[1] "146 singular samples of size 3 out of 1500"
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[1] "159 singular samples of size 3 out of 1500"
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$crit
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[1] 4.073122
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$best
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$cor
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bpi_antisocialT1Sum 1.0000000 0.6398183
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bpi_antisocialT2Sum 0.6398183 1.0000000
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$n.obs
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[1] 2875</code></pre>
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bpi_antisocialT2Sum 1.096437 2.482409
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$msg
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[1] "145 singular samples of size 3 out of 1500"
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[1] "141 singular samples of size 3 out of 1500"
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$crit
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[1] -0.3817849
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[1] -0.3839279
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$best
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[1] 1 3 8 9 10 12

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