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inst/doc/RRRe.Rmd

@@ -25,7 +25,7 @@ set.seed(42)
 ## Overview
 
 This guide is an introduction to the **Rsampling** package, which replicates in R the functions
-of the *Resampling Stats* add-in for Excell.
+of the *Resampling Stats* add-in for Excel.
 (http://www.resample.com/) [^1].
 
 These functions are used in a workflow that summarizes the logic
@@ -37,11 +37,11 @@ behind significance tests:
 4. If the probability of the observed statistic of interest occurring under
 null hypothesis is lower than a critical value, reject the null hypothesis.
 
-*Resampling Stats* 's main idea is to facilitate the understanding of this logic,
+*Resampling Stats*'s main idea is to facilitate the understanding of this logic,
 by making the user execute each step in a spreadsheet, with the aid of
 some macros.
 
-**Rsampling** 's package aims at enabling this same training process
+**Rsampling**'s package aims at enabling this same training process
 in R. Keeping the original workflow is favored over performance.
 
 
@@ -158,7 +158,7 @@ the null hypothesis if the probability of
 the statistic of interest under the null hypothesis
 is under 5% (p < 0.05).
 
-The area not highlighted in gray marks the the top 5%
+The area not highlighted in gray marks the top 5%
 of the statistic distribution under the null hypothesis.
 Thus, if the observed statistic is in the gray area we do not reject
 the null hypothesis. This is called the *acceptance region* of H0.
@@ -504,7 +504,7 @@ To learn more about this data set refer to the help page (`?pielou`).
 There are several instances with zero frequency.
 We'll simulate a null hypothesis assuming
 these frequencies are structural, that is, assuming that
-zeroes indicates insect-plant associations that can not occur.
+zeros indicates insect-plant associations that can not occur.
 This can be a reasonable assumption for phytophagous insects, that
 are in general highly specialized in some host plants.
 
@@ -557,7 +557,7 @@ of each species of aphids among the plants. Thus we simulate a situation where w
 kept the observed aggregation of records per plant species. But by shuffling records in each
 row of the data frame we simulate that these records are independent
 Furthermore, we use the `fix.zeroes = TRUE` option to indicate that zero values are not
-to be shuffled. In doing this we assume that zeroes indicate associations that can not occur.
+to be shuffled. In doing this we assume that zeros indicate associations that can not occur.
 
 ```{r pielou randomization, results="hide"}
 pielou.r1 <- Rsampling(type = "within_rows", dataframe = pielou,

inst/doc/RRRe.html

@@ -77,16 +77,16 @@ <h4 class="date"><em>April 2016</em></h4>
 
 <div id="overview" class="section level2">
 <h2>Overview</h2>
-<p>This guide is an introduction to the <strong>Rsampling</strong> package, which replicates in R the functions of the <em>Resampling Stats</em> add-in for Excell. (<a href="http://www.resample.com/" class="uri">http://www.resample.com/</a>) <a href="#fn1" class="footnoteRef" id="fnref1"><sup>1</sup></a>.</p>
+<p>This guide is an introduction to the <strong>Rsampling</strong> package, which replicates in R the functions of the <em>Resampling Stats</em> add-in for Excel. (<a href="http://www.resample.com/" class="uri">http://www.resample.com/</a>) <a href="#fn1" class="footnoteRef" id="fnref1"><sup>1</sup></a>.</p>
 <p>These functions are used in a workflow that summarizes the logic behind significance tests:</p>
 <ol style="list-style-type: decimal">
 <li>Define a statistic of interest;</li>
 <li>Define the null hypothesis;</li>
 <li>Get the statistic of interest distribution under null hypothesis;</li>
 <li>If the probability of the observed statistic of interest occurring under null hypothesis is lower than a critical value, reject the null hypothesis.</li>
 </ol>
-<p><em>Resampling Stats</em> ’s main idea is to facilitate the understanding of this logic, by making the user execute each step in a spreadsheet, with the aid of some macros.</p>
-<p><strong>Rsampling</strong> ’s package aims at enabling this same training process in R. Keeping the original workflow is favored over performance.</p>
+<p><em>Resampling Stats</em>’s main idea is to facilitate the understanding of this logic, by making the user execute each step in a spreadsheet, with the aid of some macros.</p>
+<p><strong>Rsampling</strong>’s package aims at enabling this same training process in R. Keeping the original workflow is favored over performance.</p>
 <p>The sections following installation instructions are examples of the simpler and most common applications of <strong>Rsampling</strong>. You may refer to the package help pages to learn about all other functionalities.</p>
 </div>
 <div id="installation" class="section level2">
@@ -164,7 +164,7 @@ <h3>Distribution of statistics under the null hypothesis</h3>
 <div id="decision-should-we-reject-the-null-hypothesis" class="section level3">
 <h3>Decision: should we reject the null hypothesis?</h3>
 <p>As usual in the biological sciences, we adopt the criteria of rejecting the null hypothesis if the probability of the statistic of interest under the null hypothesis is under 5% (p &lt; 0.05).</p>
-<p>The area not highlighted in gray marks the the top 5% of the statistic distribution under the null hypothesis. Thus, if the observed statistic is in the gray area we do not reject the null hypothesis. This is called the <em>acceptance region</em> of H0. As the observed value (red line) is outside the acceptance region, H0 can be rejected. You can also check this with a logical test in R:</p>
+<p>The area not highlighted in gray marks the top 5% of the statistic distribution under the null hypothesis. Thus, if the observed statistic is in the gray area we do not reject the null hypothesis. This is called the <em>acceptance region</em> of H0. As the observed value (red line) is outside the acceptance region, H0 can be rejected. You can also check this with a logical test in R:</p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">&gt;<span class="st"> </span><span class="kw">sum</span>(emb.r &gt;=<span class="st"> </span><span class="kw">emb.si</span>(embauba))/<span class="dv">1000</span> &lt;<span class="st"> </span><span class="fl">0.05</span>
 [<span class="dv">1</span>] <span class="ot">TRUE</span></code></pre></div>
 <p><strong>Conclusion:</strong> we reject the null hypothesis (p &lt; 0.05).</p>
@@ -427,7 +427,7 @@ <h2>Structural zeros</h2>
 macgillivrayae            <span class="dv">0</span>
 erigeronensis             <span class="dv">0</span>
 solirostratus             <span class="dv">0</span></code></pre></div>
-<p>To learn more about this data set refer to the help page (<code>?pielou</code>). There are several instances with zero frequency. We’ll simulate a null hypothesis assuming these frequencies are structural, that is, assuming that zeroes indicates insect-plant associations that can not occur. This can be a reasonable assumption for phytophagous insects, that are in general highly specialized in some host plants.</p>
+<p>To learn more about this data set refer to the help page (<code>?pielou</code>). There are several instances with zero frequency. We’ll simulate a null hypothesis assuming these frequencies are structural, that is, assuming that zeros indicates insect-plant associations that can not occur. This can be a reasonable assumption for phytophagous insects, that are in general highly specialized in some host plants.</p>
 <div id="study-hypothesis-3" class="section level3">
 <h3>Study Hypothesis</h3>
 <p>Our research hypothesis is that there is or there was resource partitioning of resources among aphid species. In this case, the observed associations should have resulted in decreased insect niche overlap.</p>
@@ -454,7 +454,7 @@ <h3>Statistics of interest</h3>
 </div>
 <div id="distribution-of-the-statistic-of-interest-under-the-null-hypothesis-1" class="section level3">
 <h3>Distribution of the statistic of interest under the null hypothesis</h3>
-<p>To simulate our null hypothesis, we shuffle the numbers of records of each species of aphids among the plants. Thus we simulate a situation where we kept the observed aggregation of records per plant species. But by shuffling records in each row of the data frame we simulate that these records are independent Furthermore, we use the <code>fix.zeroes = TRUE</code> option to indicate that zero values are not to be shuffled. In doing this we assume that zeroes indicate associations that can not occur.</p>
+<p>To simulate our null hypothesis, we shuffle the numbers of records of each species of aphids among the plants. Thus we simulate a situation where we kept the observed aggregation of records per plant species. But by shuffling records in each row of the data frame we simulate that these records are independent Furthermore, we use the <code>fix.zeroes = TRUE</code> option to indicate that zero values are not to be shuffled. In doing this we assume that zeros indicate associations that can not occur.</p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">&gt;<span class="st"> </span>pielou.r1 &lt;-<span class="st"> </span><span class="kw">Rsampling</span>(<span class="dt">type =</span> <span class="st">&quot;within_rows&quot;</span>, <span class="dt">dataframe =</span> pielou,
 +<span class="st">                    </span><span class="dt">statistics =</span> pielou.si, <span class="dt">ntrials =</span> <span class="dv">1000</span>, <span class="dt">fix.zeroes =</span> <span class="ot">TRUE</span>)</code></pre></div>
 <p>The observed value is greater than most values in the null distribution. As our hypothesis is one-tailed (overlapping observed lower than expected by chance) the observed value is in the null region of acceptance.</p>

inst/doc/regressions.Rmd

@@ -8,7 +8,7 @@ output:
     fig_height: 5
     fig_caption: true
 vignette: >
-  %\VignetteIndexEntry{Regression and ANOVA}
+  %\VignetteIndexEntry{Regression and ANCOVA}
   %\VignetteEngine{knitr::rmarkdown}
   \usepackage[utf8]{inputenc}
 ---