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<title>Interaction between phylogenetic uncertainty and intraspecific variability - Phylogenetic logistic Regression — tree_intra_phyglm • sensiPhy</title>


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<meta property="og:title" content="Interaction between phylogenetic uncertainty and intraspecific variability - Phylogenetic logistic Regression — tree_intra_phyglm" />
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intraspecific variability in response and/or predictor variables
and uncertainty in trees topology." />
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    <h1>Interaction between phylogenetic uncertainty and intraspecific variability - Phylogenetic logistic Regression</h1>
    <small class="dont-index">Source: <a href='https://github.com/paternogbc/sensiPhy/blob/master/R/tree_intra_phyglm.R'><code>R/tree_intra_phyglm.R</code></a></small>
    <div class="hidden name"><code>tree_intra_phyglm.Rd</code></div>
    </div>

    <div class="ref-description">
    <p>Performs Phylogenetic logistic regression evaluating
intraspecific variability in response and/or predictor variables
and uncertainty in trees topology.</p>
    </div>

    <pre class="usage"><span class='fu'>tree_intra_phyglm</span>(
  <span class='no'>formula</span>,
  <span class='no'>data</span>,
  <span class='no'>phy</span>,
  <span class='kw'>Vx</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
  <span class='kw'>x.transf</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
  <span class='kw'>n.intra</span> <span class='kw'>=</span> <span class='fl'>10</span>,
  <span class='kw'>n.tree</span> <span class='kw'>=</span> <span class='fl'>2</span>,
  <span class='kw'>distrib</span> <span class='kw'>=</span> <span class='st'>"normal"</span>,
  <span class='kw'>track</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
  <span class='kw'>btol</span> <span class='kw'>=</span> <span class='fl'>50</span>,
  <span class='no'>...</span>
)</pre>

    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
    <table class="ref-arguments">
    <colgroup><col class="name" /><col class="desc" /></colgroup>
    <tr>
      <th>formula</th>
      <td><p>The model formula: <code>response~predictor</code>.</p></td>
    </tr>
    <tr>
      <th>data</th>
      <td><p>Data frame containing species traits and species names as row names.</p></td>
    </tr>
    <tr>
      <th>phy</th>
      <td><p>A phylogeny (class 'phylo', see ?<code>ape</code>).</p></td>
    </tr>
    <tr>
      <th>Vx</th>
      <td><p>Name of the column containing the standard deviation or the standard error of the predictor 
variable. When information is not available for one taxon, the value can be 0 or <code>NA</code></p></td>
    </tr>
    <tr>
      <th>x.transf</th>
      <td><p>Transformation for the predictor variable (e.g. <code>log</code> or <code>sqrt</code>). Please use this 
argument instead of transforming data in the formula directly (see also details below).</p></td>
    </tr>
    <tr>
      <th>n.intra</th>
      <td><p>Number of times to repeat the analysis generating a random value for response and/or predictor variables.
If NULL, <code>n.intra</code> = 30</p></td>
    </tr>
    <tr>
      <th>n.tree</th>
      <td><p>Number of times to repeat the analysis with n different trees picked 
randomly in the multiPhylo file.
If NULL, <code>n.tree</code> = 2</p></td>
    </tr>
    <tr>
      <th>distrib</th>
      <td><p>A character string indicating which distribution to use to generate a random value for the response 
and/or predictor variables. Default is normal distribution: "normal" (function <code><a href='https://rdrr.io/r/stats/Normal.html'>rnorm</a></code>).
Uniform distribution: "uniform" (<code><a href='https://rdrr.io/r/stats/Uniform.html'>runif</a></code>)
Warning: we recommend to use normal distribution with Vx or Vy = standard deviation of the mean.</p></td>
    </tr>
    <tr>
      <th>track</th>
      <td><p>Print a report tracking function progress (default = TRUE)</p></td>
    </tr>
    <tr>
      <th>btol</th>
      <td><p>Bound on searching space. For details see <code>phyloglm</code></p></td>
    </tr>
    <tr>
      <th>...</th>
      <td><p>Further arguments to be passed to <code>phyloglm</code></p></td>
    </tr>
    </table>

    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>

    <p>The function <code>tree_intra_phylm</code> returns a list with the following
components:</p>
<p><code>formula</code>: The formula</p>
<p><code>data</code>: Original full dataset</p>
<p><code>sensi.estimates</code>: Coefficients, aic and the optimised value of the phylogenetic 
parameter (e.g. <code>lambda</code>) for each regression using a value in the interval of variation and 
a different phylogenetic tree.</p>
<p><code>N.obs</code>: Size of the dataset after matching it with tree tips and removing NA's.</p>
<p><code>stats</code>: Main statistics for model parameters.<code>CI_low</code> and <code>CI_high</code> are the lower 
and upper limits of the 95<!-- % confidence interval. --></p>
<p><code>all.stats</code>: Complete statistics for model parameters.
Fields coded using <code>all</code> describe statistics due to both intraspecific variation and phylogenetic uncertainty.
Fields coded using <code>intra</code> describe statistics due to intraspecific variation only.
Fields coded using <code>tree</code> describe statistics due to phylogenetic uncertainty only.
<code>sd</code> is the standard deviation. <code>CI_low</code> and <code>CI_high</code> are the lower and upper limits 
of the 95<!-- % confidence interval. --></p>
<p><code>sp.pb</code>: Species that caused problems with data transformation (see details above).</p>
    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>

    <p>This function fits a phylogenetic logistic regression model using <code><a href='https://rdrr.io/pkg/phylolm/man/phyloglm.html'>phyloglm</a></code> to n trees (<code>n.tree</code>), 
randomly picked in a multiPhylo file. The regression is also repeated <code>n.intra</code> times.
At each iteration the function generates a random value for each row in the dataset using the standard deviation 
or errors supplied and assuming a normal or uniform distribution. To calculate means and se for your raw data, 
you can use the <code>summarySE</code> function from the package <code>Rmisc</code>.</p>
<p>#' All phylogenetic models from <code>phyloglm</code> can be used, i.e. <code>BM</code>,
<code>OUfixedRoot</code>, <code>OUrandomRoot</code>, <code>lambda</code>, <code>kappa</code>,
<code>delta</code>, <code>EB</code> and <code>trend</code>. See ?<code>phyloglm</code> for details.</p>
<p>Currently, this function can only implement simple logistic models (i.e. \(trait~
predictor\)). In the future we will implement more complex models.</p>
<p>Output can be visualised using <code>sensi_plot</code>.</p>
    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>

    
  
<p>When Vy or Vx exceed Y or X, respectively, negative (or null) values can be generated, this might cause problems
for data transformation (e.g. log-transformation). In these cases, the function will skip the simulation. This problem can
be solved by increasing <code>times</code>, changing the transformation type and/or checking the target species in output$sp.pb.</p>
    <h2 class="hasAnchor" id="references"><a class="anchor" href="#references"></a>References</h2>

    <p>Paterno, G. B., Penone, C. Werner, G. D. A. 
<a href='http://doi.wiley.com/10.1111/2041-210X.12990'>sensiPhy: 
An r-package for sensitivity analysis in phylogenetic 
comparative methods.</a> Methods in Ecology and Evolution 
2018, 9(6):1461-1467</p>
<p>Martinez, P. a., Zurano, J.P., Amado, T.F., Penone, C., Betancur-R, R., 
Bidau, C.J. &amp; Jacobina, U.P. (2015). Chromosomal diversity in tropical reef 
fishes is related to body size and depth range. Molecular Phylogenetics and 
Evolution, 93, 1-4</p>
<p>Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for 
Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.</p>
    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>

    <div class='dont-index'><p><code><a href='https://rdrr.io/pkg/phylolm/man/phyloglm.html'>phyloglm</a></code>, <code><a href='tree_phyglm.html'>tree_phyglm</a></code>, <code><a href='intra_phyglm.html'>intra_phyglm</a></code>,
<code><a href='tree_intra_phylm.html'>tree_intra_phylm</a></code>, <code><a href='sensi_plot.html'>sensi_plot</a></code></p></div>

    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
    <pre class="examples"><div class='input'><span class='co'># Simulate data</span>
<span class='fu'><a href='https://rdrr.io/r/base/Random.html'>set.seed</a></span>(<span class='fl'>6987</span>)
<span class='no'>mphy</span> <span class='kw'>=</span> <span class='kw pkg'>ape</span><span class='kw ns'>::</span><span class='fu'><a href='https://rdrr.io/pkg/ape/man/rtree.html'>rmtree</a></span>(<span class='fl'>150</span>, <span class='kw'>N</span> <span class='kw'>=</span> <span class='fl'>30</span>)
<span class='no'>x</span> <span class='kw'>=</span> <span class='kw pkg'>phylolm</span><span class='kw ns'>::</span><span class='fu'><a href='https://rdrr.io/pkg/phylolm/man/rTrait.html'>rTrait</a></span>(<span class='kw'>n</span><span class='kw'>=</span><span class='fl'>1</span>,<span class='kw'>phy</span><span class='kw'>=</span><span class='no'>mphy</span><span class='kw'>[[</span><span class='fl'>1</span>]])
<span class='no'>x_sd</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/stats/Normal.html'>rnorm</a></span>(<span class='fl'>150</span>,<span class='kw'>mean</span> <span class='kw'>=</span> <span class='fl'>0.8</span>,<span class='kw'>sd</span><span class='kw'>=</span><span class='fl'>0.2</span>)
<span class='no'>X</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>cbind</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/rep.html'>rep</a></span>(<span class='fl'>1</span>,<span class='fl'>150</span>),<span class='no'>x</span>)
<span class='no'>y</span> <span class='kw'>=</span> <span class='fu'>rbinTrait</span>(<span class='kw'>n</span><span class='kw'>=</span><span class='fl'>1</span>,<span class='kw'>phy</span><span class='kw'>=</span><span class='no'>mphy</span><span class='kw'>[[</span><span class='fl'>1</span>]], <span class='kw'>beta</span><span class='kw'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(-<span class='fl'>1</span>,<span class='fl'>0.5</span>), <span class='kw'>alpha</span><span class='kw'>=</span><span class='fl'>.7</span> ,<span class='kw'>X</span><span class='kw'>=</span><span class='no'>X</span>)
<span class='no'>dat</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/data.frame.html'>data.frame</a></span>(<span class='no'>y</span>, <span class='no'>x</span>, <span class='no'>x_sd</span>)
<span class='no'>intra.tree</span> <span class='kw'>&lt;-</span> <span class='fu'>tree_intra_phyglm</span>(<span class='no'>y</span> ~ <span class='no'>x</span>, <span class='kw'>data</span> <span class='kw'>=</span> <span class='no'>dat</span>, <span class='kw'>phy</span> <span class='kw'>=</span> <span class='no'>mphy</span>, <span class='kw'>n.intra</span> <span class='kw'>=</span> <span class='fl'>3</span>,
                                           <span class='kw'>n.tree</span> <span class='kw'>=</span> <span class='fl'>3</span>, <span class='kw'>Vx</span> <span class='kw'>=</span> <span class='st'>"x_sd"</span>)</div><div class='output co'>#&gt; <span class='warning'>Warning: distrib = normal: make sure that standard deviation is provided for Vx and/or Vy</span></div><div class='output co'>#&gt; <span class='message'>Used dataset has  150  species that match data and phylogeny</span></div><div class='output co'>#&gt; 
  |                                                                            
  |                                                                      |   0%</div><div class='output co'>#&gt; <span class='warning'>Warning: the estimate of 'alpha' (10.4601876142051) reached the upper bound (10.4649228412699).</span>
#&gt; <span class='warning'> This may simply reflect a flat likelihood at large alpha values,</span>
#&gt; <span class='warning'> meaning that the phylogenetic correlation is estimated to be negligible.</span></div><div class='output co'>#&gt; <span class='warning'>Warning: the estimate of 'alpha' (10.4560158891315) reached the upper bound (10.4649228412699).</span>
#&gt; <span class='warning'> This may simply reflect a flat likelihood at large alpha values,</span>
#&gt; <span class='warning'> meaning that the phylogenetic correlation is estimated to be negligible.</span></div><div class='output co'>#&gt; 
  |                                                                            
  |=======================                                               |  33%
  |                                                                            
  |===============================================                       |  67%
  |                                                                            
  |======================================================================| 100%</div><div class='input'><span class='co'># summary results:</span>
<span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>intra.tree</span>)</div><div class='output co'>#&gt;                mean.all min.tree sd_tree min.intra max.tree CI_low_tree
#&gt; intercept        -0.709   -0.731   0.039    -0.746   -0.663      -0.811
#&gt; se.intercept      0.252    0.246   0.005     0.246    0.254       0.239
#&gt; pval.intercept    0.006    0.005   0.002     0.004    0.008       0.002
#&gt; estimate          0.383    0.377   0.008     0.372    0.391       0.356
#&gt; se.estimate       0.084    0.083   0.001     0.083    0.084       0.081
#&gt; pval.estimate     0.000    0.000   0.000     0.000    0.000       0.000
#&gt;                CI_low_intra mean.tree CI_high_tree
#&gt; intercept            -0.811    -0.709       -0.607
#&gt; se.intercept          0.239     0.252        0.265
#&gt; pval.intercept        0.002     0.006        0.010
#&gt; estimate              0.356     0.383        0.410
#&gt; se.estimate           0.081     0.084        0.086
#&gt; pval.estimate         0.000     0.000        0.000</div><div class='input'><span class='co'># Visual diagnostics for phylogenetic uncertainty:</span>
<span class='fu'><a href='sensi_plot.html'>sensi_plot</a></span>(<span class='no'>intra.tree</span>, <span class='kw'>uncer.type</span> <span class='kw'>=</span> <span class='st'>"all"</span>) <span class='co'>#or uncer.type = "tree", uncer.type = "intra"</span></div><div class='img'><img src='tree_intra_phyglm-1.png' alt='' width='700' height='433' /></div></pre>
  </div>
  <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
    <h2>Contents</h2>
    <ul class="nav nav-pills nav-stacked">
      <li><a href="#arguments">Arguments</a></li>
      <li><a href="#value">Value</a></li>
      <li><a href="#details">Details</a></li>
      <li><a href="#warning">Warning</a></li>
      <li><a href="#references">References</a></li>
      <li><a href="#see-also">See also</a></li>
      <li><a href="#examples">Examples</a></li>
    </ul>

    <h2>Author</h2>
    <p>Caterina Penone &amp; Pablo Ariel Martinez</p>
  </div>
</div>


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