Start the documentation examples

docs/build/html/.buildinfo

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 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: a5551204f8be5fc030aea28ec741f72f
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docs/build/html/_sources/basic-parametric.rst.txt

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+========================
+Basic Parametric example
+========================
+
+Load the included Balsakhi data set, which we'll use to estimate the control mean.
+
+.. code-block:: r
+
+    library(pwrcalc)
+    data(balsakhi)
+    control_mean <- mean(subset(balsakhi$post_totnorm, balsakhi$bal == 0), na.rm = TRUE)
+    control_sd   <- sd(subset(balsakhi$post_totnorm, balsakhi$bal == 0), na.rm = TRUE)
+
+Let's inspect the results to make sure we're all on the same page.
+
+.. code-block:: rconsole
+
+    > print(control_mean)
+    [1] 0.4288781
+    > print(control_sd)
+    [1] 1.15142
+
+Let's say, based on other studies, that we expect an effect size of a tenth of a standard deviation. Now let's calculate the sample size given that we know the likely effect size.
+
+.. code-block:: r
+
+    expected_effect <- control_sd / 10
+    treated_mean    <- expected_effect + control_mean
+
+We can now calculate the sample size needed to test that hypothesis.
+
+.. code-block:: rconsole
+
+    > twomeans(m1 = control_mean, m2 = treated_mean, sd = control_sd)
+
+         Two-sample t-test power calculation 
+
+                 m1 = 0.4288781
+                 m2 = 0.5440201
+                 n1 = 1570
+                 n2 = 1570
+          sig.level = 0.05
+              power = 0.8
+        alternative = two.sided
+
+    NOTE: 
+    m1 and m2 are the means of group 1 and 2, respectively.
+    n1 and n2 are the obs. of group 1 and 2, respectively.

docs/build/html/_sources/index.rst

@@ -0,0 +1,23 @@
+.. pwrcalc documentation master file, created by
+
+=======================================
+Documentation for the R package pwrcalc
+=======================================
+
+Contents:
+
+.. toctree::
+   :maxdepth: 2
+
+   intro
+   reference
+
+
+==================
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`
+

docs/build/html/_sources/index.rst.txt

@@ -11,6 +11,7 @@ Contents:
 
    intro
    reference
+   basic-parametric
 
 
 ==================

docs/build/html/_sources/intro.rst.txt

@@ -12,8 +12,8 @@ Power influences many design aspects, including what research questions to pursu
 Why Another Power Analysis Package for R?
 -----------------------------------------
 
-There exists a fine number of options for power calculations in R. From the default power_ command to pwr_ to samplesize_. However, economists love Stata. For users new to both Stata and power calculations, replicating the examples used by economists in power calculation lectures can be a bit tricky. This is the impetus for pwrcalc.
+There exists a fine number of options for power calculations in R. From the default power_ command to pwr_ to samplesize_. However, economists love Stata. For users new to both R and power calculations, replicating the examples used by economists in power calculation lectures can be a bit tricky. This is the impetus for pwrcalc.
 
 .. _power: https://stat.ethz.ch/R-manual/R-devel/library/stats/html/power.t.test.html
 .. _pwr: https://cran.r-project.org/web/packages/pwr/index.html
-.. _samplesize: https://cran.r-project.org/web/packages/samplesize/index.html
+.. _samplesize: https://cran.r-project.org/web/packages/samplesize/index.html

docs/build/html/_sources/reference.rst.txt

@@ -20,4 +20,18 @@ twomeans
     :param sd2: Standard deviation of a group 2 (e.g., the experimental-group)
     :param sig.level: significance level; default is sig.level = 0.05
     :param power: one minus the probability of type II error, default is power = 0.8
-    :rtype: A power.htest object
+    :rtype: A power.htest object with results in a structured list
+
+---------
+clustered
+---------
+
+.. function:: clustered(unclustered, rho, obsclus = NULL, numclus = NULL))
+
+   Power calculations in an experiment with group clusters
+
+    :params unclustered: Results from twomeans not adjusting for clusters
+    :params rho: Specifies the intraclass correlation coefficient
+    :params obsclus: Number of observations in each cluster
+    :params numclus: Maximum number of clusters
+    :rtype: A power.htest object with results in a structured list

docs/build/html/basic-parametric.html

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+  <div class="section" id="basic-parametric-example">
+<h1>Basic Parametric example<a class="headerlink" href="#basic-parametric-example" title="Permalink to this headline">¶</a></h1>
+<p>Load the included Balsakhi data set, which we&#8217;ll use to estimate the control mean.</p>
+<div class="highlight-r"><div class="highlight"><pre><span></span><span class="kn">library</span><span class="p">(</span>pwrcalc<span class="p">)</span>
+data<span class="p">(</span>balsakhi<span class="p">)</span>
+control_mean <span class="o">&lt;-</span> <span class="kp">mean</span><span class="p">(</span><span class="kp">subset</span><span class="p">(</span>balsakhi<span class="o">$</span>post_totnorm<span class="p">,</span> balsakhi<span class="o">$</span>bal <span class="o">==</span> <span class="m">0</span><span class="p">),</span> na.rm <span class="o">=</span> <span class="kc">TRUE</span><span class="p">)</span>
+control_sd   <span class="o">&lt;-</span> sd<span class="p">(</span><span class="kp">subset</span><span class="p">(</span>balsakhi<span class="o">$</span>post_totnorm<span class="p">,</span> balsakhi<span class="o">$</span>bal <span class="o">==</span> <span class="m">0</span><span class="p">),</span> na.rm <span class="o">=</span> <span class="kc">TRUE</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Let&#8217;s inspect the results to make sure we&#8217;re all on the same page.</p>
+<div class="highlight-rconsole"><div class="highlight"><pre><span></span><span class="gp">&gt; </span><span class="kp">print</span><span class="p">(</span>control_mean<span class="p">)</span>
+<span class="go">[1] 0.4288781</span>
+<span class="gp">&gt; </span><span class="kp">print</span><span class="p">(</span>control_sd<span class="p">)</span>
+<span class="go">[1] 1.15142</span>
+</pre></div>
+</div>
+<p>Let&#8217;s say, based on other studies, that we expect an effect size of a tenth of a standard deviation. Now let&#8217;s calculate the sample size given that we know the likely effect size.</p>
+<div class="highlight-r"><div class="highlight"><pre><span></span>expected_effect <span class="o">&lt;-</span> control_sd <span class="o">/</span> <span class="m">10</span>
+treated_mean    <span class="o">&lt;-</span> expected_effect <span class="o">+</span> control_mean
+</pre></div>
+</div>
+<p>We can now calculate the sample size needed to test that hypothesis.</p>
+<div class="highlight-rconsole"><div class="highlight"><pre><span></span><span class="gp">&gt; </span>twomeans<span class="p">(</span>m1 <span class="o">=</span> control_mean<span class="p">,</span> m2 <span class="o">=</span> treated_mean<span class="p">,</span> sd <span class="o">=</span> control_sd<span class="p">)</span>
+
+<span class="go">     Two-sample t-test power calculation</span>
+
+<span class="go">             m1 = 0.4288781</span>
+<span class="go">             m2 = 0.5440201</span>
+<span class="go">             n1 = 1570</span>
+<span class="go">             n2 = 1570</span>
+<span class="go">      sig.level = 0.05</span>
+<span class="go">          power = 0.8</span>
+<span class="go">    alternative = two.sided</span>
+
+<span class="go">NOTE:</span>
+<span class="go">m1 and m2 are the means of group 1 and 2, respectively.</span>
+<span class="go">n1 and n2 are the obs. of group 1 and 2, respectively.</span>
+</pre></div>
+</div>
+</div>
+
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