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Analysis of genotypes in single experiments using mixed-effect models with
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  <main id="main" class="col-md-9"><div class="page-header">
      <img src="../logo.png" class="logo" alt=""><h1>Genotype analysis by mixed-effect models</h1>
      <small class="dont-index">Source: <a href="https://github.com/TiagoOlivoto/metan/blob/HEAD/R/gamem.R" class="external-link"><code>R/gamem.R</code></a></small>
      <div class="d-none name"><code>gamem.Rd</code></div>
    </div>

    <div class="ref-description section level2">
    <p><a href="https://lifecycle.r-lib.org/articles/stages.html#stable" class="external-link"><img src="figures/lifecycle-stable.svg" alt="[Stable]"></a></p>
<p>Analysis of genotypes in single experiments using mixed-effect models with
estimation of genetic parameters.</p>
    </div>

    <div class="section level2">
    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">gamem</span><span class="op">(</span></span>
<span>  <span class="va">.data</span>,</span>
<span>  <span class="va">gen</span>,</span>
<span>  <span class="va">rep</span>,</span>
<span>  <span class="va">resp</span>,</span>
<span>  block <span class="op">=</span> <span class="cn">NULL</span>,</span>
<span>  by <span class="op">=</span> <span class="cn">NULL</span>,</span>
<span>  prob <span class="op">=</span> <span class="fl">0.05</span>,</span>
<span>  verbose <span class="op">=</span> <span class="cn">TRUE</span></span>
<span><span class="op">)</span></span></code></pre></div>
    </div>

    <div class="section level2">
    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
    <dl><dt>.data</dt>
<dd><p>The dataset containing the columns related to, Genotypes,
replication/block and response variable(s).</p></dd>


<dt>gen</dt>
<dd><p>The name of the column that contains the levels of the genotypes,
that will be treated as random effect.</p></dd>


<dt>rep</dt>
<dd><p>The name of the column that contains the levels of the
replications (assumed to be fixed).</p></dd>


<dt>resp</dt>
<dd><p>The response variable(s). To analyze multiple variables in a
single procedure a vector of variables may be used. For example <code>resp = c(var1, var2, var3)</code>. Select helpers are also allowed.</p></dd>


<dt>block</dt>
<dd><p>Defaults to <code>NULL</code>. In this case, a randomized complete
block design is considered. If block is informed, then an alpha-lattice
design is employed considering block as random to make use of inter-block
information, whereas the complete replicate effect is always taken as
fixed, as no inter-replicate information was to be recovered (Mohring et
al., 2015).</p></dd>


<dt>by</dt>
<dd><p>One variable (factor) to compute the function by. It is a shortcut
to <code><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">dplyr::group_by()</a></code>.This is especially useful, for example,
when the researcher want to fit a mixed-effect model for each environment.
In this case, an object of class gamem_grouped is returned.
<code><a href="mgidi.html">mgidi()</a></code> can then be used to compute the mgidi index within each
environment.</p></dd>


<dt>prob</dt>
<dd><p>The probability for estimating confidence interval for BLUP's
prediction.</p></dd>


<dt>verbose</dt>
<dd><p>Logical argument. If <code>verbose = FALSE</code> the code are run
silently.</p></dd>

</dl></div>
    <div class="section level2">
    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
    

<p>An object of class <code>gamem</code> or <code>gamem_grouped</code>, which is a
list with the following items for each element (variable):</p><ul><li><p><strong>fixed:</strong> Test for fixed effects.</p></li>
<li><p><strong>random:</strong> Variance components for random effects.</p></li>
<li><p><strong>LRT:</strong> The Likelihood Ratio Test for the random effects.</p></li>
<li><p><strong>BLUPgen:</strong> The estimated BLUPS for genotypes</p></li>
<li><p><strong>ranef:</strong> The random effects of the model</p></li>
<li><p><strong>modellme</strong> The mixed-effect model of class <code>lmerMod</code>.</p></li>
<li><p><strong>residuals</strong> The residuals of the mixed-effect model.</p></li>
<li><p><strong>model_lm</strong> The fixed-effect model of class <code>lm</code>.</p></li>
<li><p><strong>residuals_lm</strong> The residuals of the fixed-effect model.</p></li>
<li><p><strong>Details:</strong> A tibble with the following data: <code>Ngen</code>, the
number of genotypes; <code>OVmean</code>, the grand mean; <code>Min</code>, the minimum
observed (returning the genotype and replication/block); <code>Max</code> the
maximum observed, <code>MinGEN</code> the winner genotype, <code>MaxGEN</code>, the
loser genotype.</p></li>
<li><p><strong>ESTIMATES:</strong> A tibble with the values:</p><ul><li><p><code>Gen_var</code>, the genotypic variance and ;</p></li>
<li><p><code>rep:block_var</code> block-within-replicate variance (if
an alpha-lattice design is used by informing the block in <code>block</code>);</p></li>
<li><p><code>Res_var</code>, the residual variance;</p></li>
<li><p><code>Gen (%), rep:block (%), and Res (%)</code> the respective contribution
of variance components to the phenotypic variance;</p></li>
<li><p><code>H2</code>, broad-sense heritability;</p></li>
<li><p><code>h2mg</code>, heritability on the entry-mean basis;</p></li>
<li><p><code>Accuracy</code>, the accuracy of selection (square root of
<code>h2mg</code>);</p></li>
<li><p><code>CVg</code>, genotypic coefficient of variation;</p></li>
<li><p><code>CVr</code>, residual coefficient of variation;</p></li>
<li><p><code>CV ratio</code>, the ratio between genotypic and residual coefficient of
variation.</p></li>
</ul></li>
<li><p><strong>formula</strong> The formula used to fit the mixed-model.</p></li>
</ul></div>
    <div class="section level2">
    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
    <p><code>gamem</code> analyses data from a one-way genotype testing experiment.
By default, a randomized complete block design is used according to the following model:
<script id="MathJax-script" async src="../../mathjaxr/doc/mathjax/es5/tex-chtml-full.js"></script>

\[Y_{ij} = m + g_i + r_j + e_{ij}\]
where <em></em>\(Y_{ij}\) is the response variable of the ith genotype in the <em>j</em>th block;
<em>m</em> is the grand mean (fixed); <em></em>\(g_i\) is the effect of the <em>i</em>th genotype
(assumed to be random); <em></em>\(r_j\) is the effect of the <em>j</em>th replicate (assumed to be fixed);
and <em></em>\(e_{ij}\) is the random error.</p>
<p>When <code>block</code> is informed, then a resolvable alpha design is implemented, according to the following model:</p>
<p>\[Y_{ijk} = m + g_i + r_j + b_{jk} + e_{ijk}\]
where where <em></em>\(y_{ijk}\) is the response variable of the <em>i</em>th genotype in the
<em>k</em>th block of the <em>j</em>th replicate; <em>m</em> is the intercept, <em></em>\(t_i\) is
the effect for the <em>i</em>th genotype <em></em>\(r_j\) is the effect of the <em>j</em>th
replicate, <em></em>\(b_{jk}\) is the effect of the <em>k</em>th incomplete block of
the <em>j</em>th replicate, and <em></em>\(e_{ijk}\) is the plot error effect
corresponding to <em></em>\(y_{ijk}\).</p>
    </div>
    <div class="section level2">
    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
    <p>Mohring, J., E. Williams, and H.-P. Piepho. 2015. Inter-block information:
to recover or not to recover it? TAG. Theor. Appl. Genet. 128:1541-54.
<a href="https://doi.org/10.1007/s00122-015-2530-0" class="external-link">doi:10.1007/s00122-015-2530-0</a></p>
    </div>
    <div class="section level2">
    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
    <div class="dont-index"><p><code><a href="get_model_data.html">get_model_data()</a></code> <code><a href="waasb.html">waasb()</a></code></p></div>
    </div>
    <div class="section level2">
    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
    <p>Tiago Olivoto <a href="mailto:tiagoolivoto@gmail.com">tiagoolivoto@gmail.com</a></p>
    </div>

    <div class="section level2">
    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \donttest{</span></span></span>
<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/TiagoOlivoto/metan" class="external-link">metan</a></span><span class="op">)</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># fitting the model considering an RCBD</span></span></span>
<span class="r-in"><span><span class="co"># Genotype as random effects</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="va">rcbd</span> <span class="op">&lt;-</span> <span class="fu">gamem</span><span class="op">(</span><span class="va">data_g</span>,</span></span>
<span class="r-in"><span>             gen <span class="op">=</span> <span class="va">GEN</span>,</span></span>
<span class="r-in"><span>             rep <span class="op">=</span> <span class="va">REP</span>,</span></span>
<span class="r-in"><span>             resp <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">PH</span>, <span class="va">ED</span>, <span class="va">EL</span>, <span class="va">CL</span>, <span class="va">CW</span>, <span class="va">KW</span>, <span class="va">NR</span>, <span class="va">TKW</span>, <span class="va">NKE</span><span class="op">)</span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Evaluating trait PH |=====                                       | 11% 00:00:00 
Evaluating trait ED |==========                                  | 22% 00:00:00 
Evaluating trait EL |===============                             | 33% 00:00:00 
Evaluating trait CL |====================                        | 44% 00:00:00 
Evaluating trait CW |========================                    | 56% 00:00:01 
Evaluating trait KW |=============================               | 67% 00:00:01 
Evaluating trait NR |==================================          | 78% 00:00:01 
Evaluating trait TKW |======================================     | 89% 00:00:01 
Evaluating trait NKE |===========================================| 100% 00:00:01 
</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> Method: REML/BLUP</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> Random effects: GEN</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> Fixed effects: REP</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> Denominador DF: Satterthwaite's method</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> ---------------------------------------------------------------------------</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> P-values for Likelihood Ratio Test of the analyzed traits</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> ---------------------------------------------------------------------------</span>
<span class="r-out co"><span class="r-pr">#&gt;</span>     model    PH       ED    EL       CL       CW     KW     NR     TKW     NKE</span>
<span class="r-out co"><span class="r-pr">#&gt;</span>  Complete    NA       NA    NA       NA       NA     NA     NA      NA      NA</span>
<span class="r-out co"><span class="r-pr">#&gt;</span>  Genotype 0.051 2.73e-05 0.786 2.25e-06 1.24e-05 0.0253 0.0056 0.00955 0.00952</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> ---------------------------------------------------------------------------</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Variables with nonsignificant Genotype effect</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> PH EL </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> ---------------------------------------------------------------------------</span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># Likelihood ratio test for random effects</span></span></span>
<span class="r-in"><span><span class="fu"><a href="get_model_data.html">get_model_data</a></span><span class="op">(</span><span class="va">rcbd</span>, <span class="st">"lrt"</span><span class="op">)</span></span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> Class of the model: gamem</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> Variable extracted: lrt</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 9 × 8</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span>   VAR   model     npar   logLik    AIC     LRT    Df `Pr(&gt;Chisq)`</span>
<span class="r-out co"><span class="r-pr">#&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>        <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> PH    Genotype     4   -<span style="color: #BB0000;">0.947</span>   9.89  3.81       1   0.051<span style="text-decoration: underline;">0</span>    </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> ED    Genotype     4  -<span style="color: #BB0000;">91.9</span>   192.   17.6        1   0.000<span style="text-decoration: underline;">027</span>3 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> EL    Genotype     4  -<span style="color: #BB0000;">55.5</span>   119.    0.073<span style="text-decoration: underline;">5</span>     1   0.786     </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">4</span> CL    Genotype     4  -<span style="color: #BB0000;">86.2</span>   180.   22.4        1   0.000<span style="text-decoration: underline;">002</span>25</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">5</span> CW    Genotype     4 -<span style="color: #BB0000;">114.</span>    235.   19.1        1   0.000<span style="text-decoration: underline;">012</span>4 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">6</span> KW    Genotype     4 -<span style="color: #BB0000;">165.</span>    339.    5.00       1   0.025<span style="text-decoration: underline;">3</span>    </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">7</span> NR    Genotype     4  -<span style="color: #BB0000;">71.1</span>   150.    7.67       1   0.005<span style="text-decoration: underline;">60</span>   </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">8</span> TKW   Genotype     4 -<span style="color: #BB0000;">190.</span>    389.    6.72       1   0.009<span style="text-decoration: underline;">55</span>   </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">9</span> NKE   Genotype     4 -<span style="color: #BB0000;">206.</span>    420.    6.72       1   0.009<span style="text-decoration: underline;">52</span>   </span>
<span class="r-in"><span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># Variance components</span></span></span>
<span class="r-in"><span><span class="fu"><a href="get_model_data.html">get_model_data</a></span><span class="op">(</span><span class="va">rcbd</span>, <span class="st">"vcomp"</span><span class="op">)</span></span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> Class of the model: gamem</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> Variable extracted: vcomp</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 2 × 10</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span>   Group        PH    ED     EL    CL    CW    KW    NR   TKW   NKE</span>
<span class="r-out co"><span class="r-pr">#&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> GEN      0.017<span style="text-decoration: underline;">1</span>  5.37 0.047<span style="text-decoration: underline;">2</span>  4.27 18.5   181.  1.18  841. <span style="text-decoration: underline;">1</span>982.</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> Residual 0.032<span style="text-decoration: underline;">8</span>  2.43 0.984   1.41  7.54  280.  1.27 <span style="text-decoration: underline;">1</span>018. <span style="text-decoration: underline;">2</span>399.</span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># Genetic parameters</span></span></span>
<span class="r-in"><span><span class="fu"><a href="get_model_data.html">get_model_data</a></span><span class="op">(</span><span class="va">rcbd</span>, <span class="st">"genpar"</span><span class="op">)</span></span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> Class of the model: gamem</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> Variable extracted: genpar</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 11 × 10</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span>    Paramet…¹      PH     ED      EL     CL     CW      KW     NR     TKW     NKE</span>
<span class="r-out co"><span class="r-pr">#&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>       <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> Gen_var    0.017<span style="text-decoration: underline;">1</span>  5.37   0.047<span style="text-decoration: underline;">2</span>  4.27  18.5   181.     1.18  8.41<span style="color: #949494;">e</span>+2 1.98<span style="color: #949494;">e</span>+3</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> Gen (%)   34.3    68.8    4.58   75.1   71.0    39.2   48.2   4.52<span style="color: #949494;">e</span>+1 4.52<span style="color: #949494;">e</span>+1</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> Res_var    0.032<span style="text-decoration: underline;">8</span>  2.43   0.984   1.41   7.54  280.     1.27  1.02<span style="color: #949494;">e</span>+3 2.40<span style="color: #949494;">e</span>+3</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> Res (%)   65.7    31.2   95.4    24.9   29.0    60.8   51.8   5.48<span style="color: #949494;">e</span>+1 5.48<span style="color: #949494;">e</span>+1</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> Phen_var   0.049<span style="text-decoration: underline;">8</span>  7.80   1.03    5.68  26.0   461.     2.45  1.86<span style="color: #949494;">e</span>+3 4.38<span style="color: #949494;">e</span>+3</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> H2         0.343   0.688  0.045<span style="text-decoration: underline;">8</span>  0.751  0.710   0.392  0.482 4.52<span style="color: #949494;">e</span><span style="color: #BB0000;">-1</span> 4.52<span style="color: #949494;">e</span><span style="color: #BB0000;">-1</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> h2mg       0.610   0.869  0.126   0.901  0.880   0.659  0.736 7.12<span style="color: #949494;">e</span><span style="color: #BB0000;">-1</span> 7.13<span style="color: #949494;">e</span><span style="color: #BB0000;">-1</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> Accuracy   0.781   0.932  0.355   0.949  0.938   0.812  0.858 8.44<span style="color: #949494;">e</span><span style="color: #BB0000;">-1</span> 8.44<span style="color: #949494;">e</span><span style="color: #BB0000;">-1</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> CVg        6.03    4.84   1.48    7.26  20.7     9.16   6.88  9.13<span style="color: #949494;">e</span>+0 9.52<span style="color: #949494;">e</span>+0</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> CVr        8.35    3.26   6.76    4.18  13.2    11.4    7.14  1.00<span style="color: #949494;">e</span>+1 1.05<span style="color: #949494;">e</span>+1</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">11</span> CV ratio   0.722   1.49   0.219   1.74   1.56    0.803  0.964 9.09<span style="color: #949494;">e</span><span style="color: #BB0000;">-1</span> 9.09<span style="color: #949494;">e</span><span style="color: #BB0000;">-1</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># … with abbreviated variable name ¹​Parameters</span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># random effects</span></span></span>
<span class="r-in"><span><span class="fu"><a href="get_model_data.html">get_model_data</a></span><span class="op">(</span><span class="va">rcbd</span>, <span class="st">"ranef"</span><span class="op">)</span></span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> Class of the model: gamem</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> Variable extracted: ranef</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> $GEN</span>
<span class="r-out co"><span class="r-pr">#&gt;</span>    GEN           PH         ED           EL         CL         CW         KW</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 1   H1  0.018773415  2.3610811  0.020813796  2.2056449  5.3329442   6.597949</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 2  H10 -0.078441587 -3.4773234 -0.085772984 -3.3060659 -7.4818217 -17.311524</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 3  H11 -0.039799640 -0.7171292 -0.053041610 -1.8922680 -4.2006643  -4.019522</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 4  H12  0.160731724 -0.1152736 -0.089465754 -2.3605323 -2.6282930   1.022669</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 5  H13  0.263641328  2.4352270  0.090472873 -1.0499926  0.6731997  22.941732</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 6   H2 -0.007665811  2.4004711  0.092151405  1.5266616  1.1173015   8.900057</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 7   H3 -0.075187528 -0.6956964  0.008224807  0.1086613 -2.0755405  -5.159344</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 8   H4 -0.071526712 -1.7847132  0.108936725 -0.6927910 -1.1569455  -2.213329</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 9   H5 -0.043867214  1.9584925  0.003189211  1.6503314  5.2258693   9.434104</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 10  H6 -0.008072569  1.8461152 -0.142843071  3.1109558  1.9916308  -6.425132</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 11  H7  0.006570695  0.8086529 -0.016113907  1.5969013  5.7354166   5.832276</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 12  H8 -0.040613155 -1.5286784  0.004867743  0.5270975  1.1054193  -3.547764</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 13  H9 -0.084542947 -3.4912257  0.058580766 -1.4246040 -3.6385165 -16.052173</span>
<span class="r-out co"><span class="r-pr">#&gt;</span>             NR        TKW        NKE</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 1   0.06038462  36.368389 -30.472599</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 2  -0.33211539 -50.194254  14.894629</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 3   0.35475962 -20.721892  14.134550</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 4   0.35475962 -24.282196  34.894214</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 5   2.02288464   1.360088  79.073819</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 6  -0.03774039  20.096643  -1.114539</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 7  -0.72461539  13.062513 -33.417906</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 8  -1.41149040  -7.828821   4.491045</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 9   1.23788463  -8.706006  48.860670</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 10  0.45288462   7.352279 -34.463015</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 11 -0.13586539  28.730081 -19.403946</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 12 -0.92086539  21.404122 -43.156422</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 13 -0.92086539 -16.640945 -34.320501</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># Predicted values</span></span></span>
<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">rcbd</span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 39 × 11</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span>    GEN   REP      PH    ED    EL    CL    CW    KW    NR   TKW   NKE</span>
<span class="r-out co"><span class="r-pr">#&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;fct&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> H1    1      2.12  50.5  14.9  31.5  26.9  156.  15.8  360.  436.</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> H1    2      2.20  49.5  14.5  29.9  24.4  146.  16.1  343.  428.</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> H1    3      2.24  50.7  14.6  30.6  27.1  159.  15.7  359.  449.</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> H10   1      2.02  44.6  14.8  26.0  14.0  132.  15.4  274.  481.</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> H10   2      2.10  43.7  14.4  24.4  11.6  122.  15.7  257.  473.</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> H10   3      2.14  44.9  14.5  25.1  14.2  135.  15.3  272.  494.</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> H11   1      2.06  47.4  14.9  27.4  17.3  145.  16.1  303.  481.</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> H11   2      2.14  46.4  14.5  25.8  14.9  135.  16.4  286.  472.</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> H11   3      2.18  47.6  14.5  26.5  17.5  148.  16.0  302.  493.</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> H12   1      2.26  48.0  14.8  26.9  18.9  150.  16.1  300.  501.</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># … with 29 more rows</span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># fitting the model considering an alpha-lattice design</span></span></span>
<span class="r-in"><span><span class="co"># Genotype and block-within-replicate as random effects</span></span></span>
<span class="r-in"><span><span class="co"># Note that block effect was now informed.</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="va">alpha</span> <span class="op">&lt;-</span> <span class="fu">gamem</span><span class="op">(</span><span class="va">data_alpha</span>,</span></span>
<span class="r-in"><span>               gen <span class="op">=</span> <span class="va">GEN</span>,</span></span>
<span class="r-in"><span>               rep <span class="op">=</span> <span class="va">REP</span>,</span></span>
<span class="r-in"><span>               block <span class="op">=</span> <span class="va">BLOCK</span>,</span></span>
<span class="r-in"><span>               resp <span class="op">=</span> <span class="va">YIELD</span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Evaluating trait YIELD |=========================================| 100% 00:00:00 
</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> Method: REML/BLUP</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> Random effects: GEN, BLOCK(REP)</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> Fixed effects: REP</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> Denominador DF: Satterthwaite's method</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> ---------------------------------------------------------------------------</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> P-values for Likelihood Ratio Test of the analyzed traits</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> ---------------------------------------------------------------------------</span>
<span class="r-out co"><span class="r-pr">#&gt;</span>      model    YIELD</span>
<span class="r-out co"><span class="r-pr">#&gt;</span>   Complete       NA</span>
<span class="r-out co"><span class="r-pr">#&gt;</span>   Genotype 1.18e-06</span>
<span class="r-out co"><span class="r-pr">#&gt;</span>  rep:block 3.35e-03</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> ---------------------------------------------------------------------------</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> All variables with significant (p &lt; 0.05) genotype effect</span>
<span class="r-in"><span><span class="co"># Genetic parameters</span></span></span>
<span class="r-in"><span><span class="fu"><a href="get_model_data.html">get_model_data</a></span><span class="op">(</span><span class="va">alpha</span>, <span class="st">"genpar"</span><span class="op">)</span></span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> Class of the model: gamem</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> Variable extracted: genpar</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 13 × 2</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span>    Parameters      YIELD</span>
<span class="r-out co"><span class="r-pr">#&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>           <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> Gen_var        0.143 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> Gen (%)       48.5   </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> rep:block_var  0.070<span style="text-decoration: underline;">2</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> rep:block (%) 23.8   </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> Res_var        0.081<span style="text-decoration: underline;">6</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> Res (%)       27.7   </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> Phen_var       0.295 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> H2             0.485 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> h2mg           0.798 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> Accuracy       0.893 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">11</span> CVg            8.44  </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">12</span> CVr            6.38  </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">13</span> CV ratio       1.32  </span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># Random effects</span></span></span>
<span class="r-in"><span><span class="fu"><a href="get_model_data.html">get_model_data</a></span><span class="op">(</span><span class="va">alpha</span>, <span class="st">"ranef"</span><span class="op">)</span></span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> Class of the model: gamem</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> Variable extracted: ranef</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> $GEN</span>
<span class="r-out co"><span class="r-pr">#&gt;</span>    GEN        YIELD</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 1  G01  0.501183769</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 2  G02  0.004962705</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 3  G03 -0.784562783</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 4  G04  0.006125660</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 5  G05  0.474950041</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 6  G06  0.044640383</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 7  G07 -0.308947691</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 8  G08  0.062229524</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 9  G09 -0.809931603</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 10 G10 -0.089373059</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 11 G11 -0.196434546</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 12 G12  0.225758446</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 13 G13  0.231664921</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 14 G14  0.243399964</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 15 G15  0.424699859</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 16 G16  0.200964673</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 17 G17  0.078077967</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 18 G18 -0.110180929</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 19 G19  0.289576067</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 20 G20 -0.338969056</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 21 G21  0.256132122</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 22 G22  0.024088815</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 23 G23 -0.176997620</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 24 G24 -0.253057630</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> $REP_BLOCK</span>
<span class="r-out co"><span class="r-pr">#&gt;</span>    REP BLOCK        YIELD</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 1   R1    B1  0.123136175</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 2   R1    B2 -0.141225413</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 3   R1    B3 -0.150394401</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 4   R1    B4 -0.106755541</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 5   R1    B5  0.073704281</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 6   R1    B6  0.201534899</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 7   R2    B1 -0.532640774</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 8   R2    B2 -0.301232978</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 9   R2    B3  0.243239346</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 10  R2    B4  0.134878440</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 11  R2    B5  0.275336937</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 12  R2    B6  0.180419028</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 13  R3    B1  0.050569780</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 14  R3    B2 -0.047784038</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 15  R3    B3  0.151079007</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 16  R3    B4  0.053760694</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 17  R3    B5 -0.008047649</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 18  R3    B6 -0.199577794</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> $GEN_REP_BLOCK</span>
<span class="r-out co"><span class="r-pr">#&gt;</span>    GEN REP BLOCK       YIELD</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 1  G01  R1    B5  0.57488805</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 2  G01  R2    B4  0.63606221</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 3  G01  R3    B1  0.55175355</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 4  G02  R1    B2 -0.13626271</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 5  G02  R2    B5  0.28029964</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 6  G02  R3    B2 -0.04282133</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 7  G03  R1    B4 -0.89131832</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 8  G03  R2    B2 -1.08579576</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 9  G03  R3    B6 -0.98414058</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 10 G04  R1    B1  0.12926184</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 11 G04  R2    B1 -0.52651511</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 12 G04  R3    B3  0.15720467</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 13 G05  R1    B1  0.59808622</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 14 G05  R2    B4  0.60982848</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 15 G05  R3    B6  0.27537225</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 16 G06  R1    B6  0.24617528</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 17 G06  R2    B6  0.22505941</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 18 G06  R3    B3  0.19571939</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 19 G07  R1    B5 -0.23524341</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 20 G07  R2    B6 -0.12852866</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 21 G07  R3    B6 -0.50852549</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 22 G08  R1    B4 -0.04452602</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 23 G08  R2    B1 -0.47041125</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 24 G08  R3    B2  0.01444549</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 25 G09  R1    B6 -0.60839670</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 26 G09  R2    B4 -0.67505316</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 27 G09  R3    B2 -0.85771564</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 28 G10  R1    B2 -0.23059847</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 29 G10  R2    B4  0.04550538</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 30 G10  R3    B4 -0.03561236</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 31 G11  R1    B1 -0.07329837</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 32 G11  R2    B3  0.04680480</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 33 G11  R3    B1 -0.14586477</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 34 G12  R1    B6  0.42729335</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 35 G12  R2    B3  0.46899779</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 36 G12  R3    B4  0.27951914</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 37 G13  R1    B4  0.12490938</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 38 G13  R2    B5  0.50700186</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 39 G13  R3    B4  0.28542562</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 40 G14  R1    B3  0.09300556</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 41 G14  R2    B1 -0.28924081</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 42 G14  R3    B1  0.29396974</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 43 G15  R1    B5  0.49840414</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 44 G15  R2    B2  0.12346688</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 45 G15  R3    B2  0.37691582</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 46 G16  R1    B3  0.05057027</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 47 G16  R2    B6  0.38138370</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 48 G16  R3    B5  0.19291702</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 49 G17  R1    B5  0.15178225</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 50 G17  R2    B3  0.32131731</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 51 G17  R3    B3  0.22915697</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 52 G18  R1    B3 -0.26057533</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 53 G18  R2    B5  0.16515601</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 54 G18  R3    B3  0.04089808</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 55 G19  R1    B4  0.18282053</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 56 G19  R2    B6  0.46999510</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 57 G19  R3    B1  0.34014585</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 58 G20  R1    B2 -0.48019447</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 59 G20  R2    B1 -0.87160983</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 60 G20  R3    B6 -0.53854685</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 61 G21  R1    B2  0.11490671</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 62 G21  R2    B3  0.49937147</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 63 G21  R3    B5  0.24808447</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 64 G22  R1    B1  0.14722499</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 65 G22  R2    B5  0.29942575</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 66 G22  R3    B5  0.01604117</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 67 G23  R1    B3 -0.32739202</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 68 G23  R2    B2 -0.47823060</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 69 G23  R3    B4 -0.12323693</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 70 G24  R1    B6 -0.05152273</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 71 G24  R2    B2 -0.55429061</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 72 G24  R3    B5 -0.26110528</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
<span class="r-in"><span><span class="co"># }</span></span></span>
<span class="r-in"><span></span></span>
</code></pre></div>
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