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CONTRIBUTING.html

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Intro-to-GLMs.html

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@@ -545,7 +545,7 @@
 <li>nonparametric tests:
 <ul>
 <li>Wilcoxon signed-rank test for matched pairs</li>
-<li>Mann–Whitney/Kruskal-Wallis rank sum test for ≥2 independent samples</li>
+<li>Mann–Whitney/Kruskal-Wallis rank sum test for <span class="math inline">\(\geq 2\)</span> independent samples</li>
 <li>Fisher’s exact test for contingency tables</li>
 <li>Cochran–Mantel–Haenszel-Cox log-rank test</li>
 </ul>

Linear-models-overview.html

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appendices-are-prereqs.html

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common-mistakes.html

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count-regression.html

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estimation.html

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glms.html

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index.html

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inference.html

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intro-MLEs.html

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intro-bayes.html

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intro-multilevel-models.html

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intro-to-R.html

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+  <script src="https://cdnjs.cloudflare.com/polyfill/v3/polyfill.min.js?features=es6"></script>
+  <script src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-chtml-full.js" type="text/javascript"></script>
+
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+  typesetMath
+};
+</script>
 
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@@ -515,7 +544,7 @@ <h2 data-number="J.6" class="anchored" data-anchor-id="piping"><span class="head
 <p>There are currently (2024) two commonly-used pipe operators in R:</p>
 <ul>
 <li><p><code>%&gt;%</code>: the “<code>magrittr</code> pipe”, from the <a href="https://cran.r-project.org/web/packages/magrittr/index.html"><code>magrittr</code></a> package (<span class="citation" data-cites="magrittr">Bache and Wickham (<a href="references.html#ref-magrittr" role="doc-biblioref">2022</a>)</span>; <a href="https://r-pkgs.org/dependencies-in-practice.html#re-exporting">re-exported</a> by <a href="https://cran.r-project.org/web/packages/dplyr/index.html"><code>dplyr</code></a> and others) .</p></li>
-<li><p><code>|&gt;</code>: the “native pipe”, from base R (≥4.1.0)</p></li>
+<li><p><code>|&gt;</code>: the “native pipe”, from base R (<span class="math inline">\(\geq\)</span> 4.1.0)</p></li>
 </ul>
 <section id="which-pipe-should-i-use" class="level3" data-number="J.6.1">
 <h3 data-number="J.6.1" class="anchored" data-anchor-id="which-pipe-should-i-use"><span class="header-section-number">J.6.1</span> Which pipe should I use?</h3>
@@ -1410,7 +1439,7 @@ <h1 data-number="L"><span class="header-section-number">L</span> Contributing to
 <span id="cb1-102"><a href="#cb1-102" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb1-103"><a href="#cb1-103" aria-hidden="true" tabindex="-1"></a><span class="ss">- </span><span class="in">`%&gt;%`</span>: the "<span class="in">`magrittr`</span> pipe", from the <span class="co">[</span><span class="ot">`magrittr`</span><span class="co">](https://cran.r-project.org/web/packages/magrittr/index.html)</span> package (@magrittr; <span class="co">[</span><span class="ot">re-exported</span><span class="co">](https://r-pkgs.org/dependencies-in-practice.html#re-exporting)</span> by <span class="co">[</span><span class="ot">`dplyr`</span><span class="co">](https://cran.r-project.org/web/packages/dplyr/index.html)</span> and others) .</span>
 <span id="cb1-104"><a href="#cb1-104" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-105"><a href="#cb1-105" aria-hidden="true" tabindex="-1"></a><span class="ss">- </span><span class="in">`|&gt;`</span>: the "native pipe", from base R (≥4.1.0)</span>
+<span id="cb1-105"><a href="#cb1-105" aria-hidden="true" tabindex="-1"></a><span class="ss">- </span><span class="in">`|&gt;`</span>: the "native pipe", from base R ($\geq$ 4.1.0)</span>
 <span id="cb1-106"><a href="#cb1-106" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb1-107"><a href="#cb1-107" aria-hidden="true" tabindex="-1"></a><span class="fu">### Which pipe should I use?</span></span>
 <span id="cb1-108"><a href="#cb1-108" aria-hidden="true" tabindex="-1"></a></span>

intro-to-survival-analysis.html

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logistic-regression.html

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math-prereqs.html

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notation.html

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parametric-survival-models.html

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probability.html

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proportional-hazards-models.html

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references.html

@@ -377,7 +377,7 @@
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search.json

@@ -64,7 +64,7 @@
     "href": "Intro-to-GLMs.html#introduction-to-epi-204",
     "title": "\n1  Introduction\n",
     "section": "\n1.1 Introduction to Epi 204",
-    "text": "1.1 Introduction to Epi 204\nWelcome to Epidemiology 204: Quantitative Epidemiology III (Statistical Models).\nIn this course, we will start where Epi 203 left off: with linear regression models.\n\n\n\n\n\n\nNote\n\n\n\nEpi 203/STA 130B/STA 131B is a prerequisite for this course. If you haven’t passed one of these courses, please talk to me ASAP.\n\n\n\n1.1.1 What you should already know\nEpi 202: probability models for different data types\n\nProbability distributions\n\nbinomial\nPoisson\nGaussian\nexponential\n\n\nCharacteristics of probability distributions\n\nMean, median, mode, quantiles\nVariance, standard deviation, overdispersion\n\n\nCharacteristics of samples\n\nindependence, dependence, covariance, correlation\nranks, order statistics\nidentical vs nonidentical distribution (homogeneity vs heterogeneity)\nLaws of Large Numbers\nCentral Limit Theorem for the mean of an iid sample\n\n\n\nEpi 203: inference for one or several homogenous populations\n\nthe maximum likelihood inference framework:\n\nlikelihood functions\nlog-likelihood functions\nscore functions\nestimating equations\ninformation matrices\npoint estimates\nstandard errors\nconfidence intervals\nhypothesis tests\np-values\n\n\nHypothesis tests for one, two, and >2 groups:\n\nt-tests/ANOVA for Gaussian models\nchi-square tests for binomial and Poisson models\nnonparametric tests:\n\nWilcoxon signed-rank test for matched pairs\nMann–Whitney/Kruskal-Wallis rank sum test for ≥2 independent samples\nFisher’s exact test for contingency tables\nCochran–Mantel–Haenszel-Cox log-rank test\n\n\n\n\nSome linear regression\n\nFor all of the quantities above, and especially for confidence intervals and p-values, you should know how both: - how to compute them - how to interpret them\nStat 108: linear regression models\n\nbuilding models for Gaussian outcomes\n\nmultiple predictors\ninteractions\n\n\nregression diagnostics\nfundamentals of R programming; e.g.:\n\nWickham, Çetinkaya-Rundel, and Grolemund (2023)\nDalgaard (2008)\n\n\n\nRMarkdown or Quarto for formatting homework\n\nLaTeX for writing math in RMarkdown/Quarto\n\n\n\n1.1.2 What we will cover in this course\n\nLinear (Gaussian) regression models (review and more details)\n\nRegression models for non-Gaussian outcomes\n\nbinary\ncount\ntime to event\n\n\nStatistical analysis using R",
+    "text": "1.1 Introduction to Epi 204\nWelcome to Epidemiology 204: Quantitative Epidemiology III (Statistical Models).\nIn this course, we will start where Epi 203 left off: with linear regression models.\n\n\n\n\n\n\nNote\n\n\n\nEpi 203/STA 130B/STA 131B is a prerequisite for this course. If you haven’t passed one of these courses, please talk to me ASAP.\n\n\n\n1.1.1 What you should already know\nEpi 202: probability models for different data types\n\nProbability distributions\n\nbinomial\nPoisson\nGaussian\nexponential\n\n\nCharacteristics of probability distributions\n\nMean, median, mode, quantiles\nVariance, standard deviation, overdispersion\n\n\nCharacteristics of samples\n\nindependence, dependence, covariance, correlation\nranks, order statistics\nidentical vs nonidentical distribution (homogeneity vs heterogeneity)\nLaws of Large Numbers\nCentral Limit Theorem for the mean of an iid sample\n\n\n\nEpi 203: inference for one or several homogenous populations\n\nthe maximum likelihood inference framework:\n\nlikelihood functions\nlog-likelihood functions\nscore functions\nestimating equations\ninformation matrices\npoint estimates\nstandard errors\nconfidence intervals\nhypothesis tests\np-values\n\n\nHypothesis tests for one, two, and >2 groups:\n\nt-tests/ANOVA for Gaussian models\nchi-square tests for binomial and Poisson models\nnonparametric tests:\n\nWilcoxon signed-rank test for matched pairs\nMann–Whitney/Kruskal-Wallis rank sum test for \\(\\geq 2\\) independent samples\nFisher’s exact test for contingency tables\nCochran–Mantel–Haenszel-Cox log-rank test\n\n\n\n\nSome linear regression\n\nFor all of the quantities above, and especially for confidence intervals and p-values, you should know how both: - how to compute them - how to interpret them\nStat 108: linear regression models\n\nbuilding models for Gaussian outcomes\n\nmultiple predictors\ninteractions\n\n\nregression diagnostics\nfundamentals of R programming; e.g.:\n\nWickham, Çetinkaya-Rundel, and Grolemund (2023)\nDalgaard (2008)\n\n\n\nRMarkdown or Quarto for formatting homework\n\nLaTeX for writing math in RMarkdown/Quarto\n\n\n\n1.1.2 What we will cover in this course\n\nLinear (Gaussian) regression models (review and more details)\n\nRegression models for non-Gaussian outcomes\n\nbinary\ncount\ntime to event\n\n\nStatistical analysis using R",
     "crumbs": [
       "<span class='chapter-number'>1</span>  <span class='chapter-title'>Introduction</span>"
     ]
@@ -1367,7 +1367,7 @@
     "href": "intro-to-R.html#piping",
     "title": "Appendix J — Statistical computing in R",
     "section": "J.6 Piping",
-    "text": "J.6 Piping\nSee Wickham, Çetinkaya-Rundel, and Grolemund (2023) for details.\nThere are currently (2024) two commonly-used pipe operators in R:\n\n%&gt;%: the “magrittr pipe”, from the magrittr package (Bache and Wickham (2022); re-exported by dplyr and others) .\n|&gt;: the “native pipe”, from base R (≥4.1.0)\n\n\nJ.6.1 Which pipe should I use?\nWickham, Çetinkaya-Rundel, and Grolemund (2023) recommends the native pipe:\n\nFor simple cases, |&gt; and %&gt;% behave identically. So why do we recommend the base pipe? Firstly, because it’s part of base R, it’s always available for you to use, even when you’re not using the tidyverse. Secondly, |&gt; is quite a bit simpler than %&gt;%: in the time between the invention of %&gt;% in 2014 and the inclusion of |&gt; in R 4.1.0 in 2021, we gained a better understanding of the pipe. This allowed the base implementation to jettison infrequently used and less important features.\n\n\n\nJ.6.2 Why doesn’t ggplot2 use piping?\nHere’s tidyverse creator Hadley Wickham’s answer (from 2018):\n\nI think it’s worth unpacking this question into a few smaller pieces:\n\nShould ggplot2 use the pipe? IMO, yes.\nCould ggplot2 support both the pipe and plus? No\nWould it be worth it to create a ggplot3 that uses the pipe? No.\n\n\nhttps://forum.posit.co/t/why-cant-ggplot2-use/4372/7",
+    "text": "J.6 Piping\nSee Wickham, Çetinkaya-Rundel, and Grolemund (2023) for details.\nThere are currently (2024) two commonly-used pipe operators in R:\n\n%&gt;%: the “magrittr pipe”, from the magrittr package (Bache and Wickham (2022); re-exported by dplyr and others) .\n|&gt;: the “native pipe”, from base R (\\(\\geq\\) 4.1.0)\n\n\nJ.6.1 Which pipe should I use?\nWickham, Çetinkaya-Rundel, and Grolemund (2023) recommends the native pipe:\n\nFor simple cases, |&gt; and %&gt;% behave identically. So why do we recommend the base pipe? Firstly, because it’s part of base R, it’s always available for you to use, even when you’re not using the tidyverse. Secondly, |&gt; is quite a bit simpler than %&gt;%: in the time between the invention of %&gt;% in 2014 and the inclusion of |&gt; in R 4.1.0 in 2021, we gained a better understanding of the pipe. This allowed the base implementation to jettison infrequently used and less important features.\n\n\n\nJ.6.2 Why doesn’t ggplot2 use piping?\nHere’s tidyverse creator Hadley Wickham’s answer (from 2018):\n\nI think it’s worth unpacking this question into a few smaller pieces:\n\nShould ggplot2 use the pipe? IMO, yes.\nCould ggplot2 support both the pipe and plus? No\nWould it be worth it to create a ggplot3 that uses the pipe? No.\n\n\nhttps://forum.posit.co/t/why-cant-ggplot2-use/4372/7",
     "crumbs": [
       "Appendices",
       "<span class='chapter-number'>J</span>  <span class='chapter-title'>Statistical computing in R</span>"

time-to-event-models.html

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