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name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form> <ul class="navbar-nav"><li class="nav-item"> <a class="external-link nav-link" href="https://www.researchgate.net/profile/Tiago_Olivoto2"> <span class="fab fa fab fa-researchgate fa-2x"></span> </a> </li> <li class="nav-item"> <a class="external-link nav-link" href="https://github.com/TiagoOlivoto"> <span class="fa fa-github fa-2x"></span> </a> </li> <li class="nav-item"> <a class="external-link nav-link" href="https://twitter.com/tolivoto"> <span class="fab fa fab fa-twitter fa-2x"></span> </a> </li> <li class="nav-item"> <a class="external-link nav-link" href="https://www.mendeley.com/profiles/tiago-olivoto/"> <span class="fab fa fab fa-mendeley fa-2x"></span> </a> </li> </ul></div> </div> </nav><div class="container template-reference-topic"> <div class="row"> <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"><-</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">#></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">#></span> Method: REML/BLUP</span> <span class="r-msg co"><span class="r-pr">#></span> Random effects: GEN</span> <span class="r-msg co"><span class="r-pr">#></span> Fixed effects: REP</span> <span class="r-msg co"><span class="r-pr">#></span> Denominador DF: Satterthwaite's method</span> <span class="r-out co"><span class="r-pr">#></span> ---------------------------------------------------------------------------</span> <span class="r-out co"><span class="r-pr">#></span> P-values for Likelihood Ratio Test of the analyzed traits</span> <span class="r-out co"><span class="r-pr">#></span> ---------------------------------------------------------------------------</span> <span class="r-out co"><span class="r-pr">#></span> model PH ED EL CL CW KW NR TKW NKE</span> <span class="r-out co"><span class="r-pr">#></span> Complete NA NA NA NA NA NA NA NA NA</span> <span class="r-out co"><span class="r-pr">#></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">#></span> ---------------------------------------------------------------------------</span> <span class="r-out co"><span class="r-pr">#></span> Variables with nonsignificant Genotype effect</span> <span class="r-out co"><span class="r-pr">#></span> PH EL </span> <span class="r-out co"><span class="r-pr">#></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">#></span> Class of the model: gamem</span> <span class="r-msg co"><span class="r-pr">#></span> Variable extracted: lrt</span> <span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># A tibble: 9 × 8</span></span> <span class="r-out co"><span class="r-pr">#></span> VAR model npar logLik AIC LRT Df `Pr(>Chisq)`</span> <span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span></span> <span class="r-out co"><span class="r-pr">#></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">#></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">#></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">#></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">#></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">#></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">#></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">#></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">#></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">#></span> Class of the model: gamem</span> <span class="r-msg co"><span class="r-pr">#></span> Variable extracted: vcomp</span> <span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># A tibble: 2 × 10</span></span> <span class="r-out co"><span class="r-pr">#></span> Group PH ED EL CL CW KW NR TKW NKE</span> <span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span></span> <span class="r-out co"><span class="r-pr">#></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">#></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">#></span> Class of the model: gamem</span> <span class="r-msg co"><span class="r-pr">#></span> Variable extracted: genpar</span> <span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># A tibble: 11 × 10</span></span> <span class="r-out co"><span class="r-pr">#></span> Paramet…¹ PH ED EL CL CW KW NR TKW NKE</span> <span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span></span> <span class="r-out co"><span class="r-pr">#></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">#></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">#></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">#></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">#></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">#></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">#></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">#></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">#></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">#></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">#></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">#></span> <span style="color: #949494;"># … with abbreviated variable name ¹Parameters</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">#></span> Class of the model: gamem</span> <span class="r-msg co"><span class="r-pr">#></span> Variable extracted: ranef</span> <span class="r-out co"><span class="r-pr">#></span> $GEN</span> <span class="r-out co"><span class="r-pr">#></span> GEN PH ED EL CL CW KW</span> <span class="r-out co"><span class="r-pr">#></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">#></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">#></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">#></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">#></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">#></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">#></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">#></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">#></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">#></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">#></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">#></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">#></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">#></span> NR TKW NKE</span> <span class="r-out co"><span class="r-pr">#></span> 1 0.06038462 36.368389 -30.472599</span> <span class="r-out co"><span class="r-pr">#></span> 2 -0.33211539 -50.194254 14.894629</span> <span class="r-out co"><span class="r-pr">#></span> 3 0.35475962 -20.721892 14.134550</span> <span class="r-out co"><span class="r-pr">#></span> 4 0.35475962 -24.282196 34.894214</span> <span class="r-out co"><span class="r-pr">#></span> 5 2.02288464 1.360088 79.073819</span> <span class="r-out co"><span class="r-pr">#></span> 6 -0.03774039 20.096643 -1.114539</span> <span class="r-out co"><span class="r-pr">#></span> 7 -0.72461539 13.062513 -33.417906</span> <span class="r-out co"><span class="r-pr">#></span> 8 -1.41149040 -7.828821 4.491045</span> <span class="r-out co"><span class="r-pr">#></span> 9 1.23788463 -8.706006 48.860670</span> <span class="r-out co"><span class="r-pr">#></span> 10 0.45288462 7.352279 -34.463015</span> <span class="r-out co"><span class="r-pr">#></span> 11 -0.13586539 28.730081 -19.403946</span> <span class="r-out co"><span class="r-pr">#></span> 12 -0.92086539 21.404122 -43.156422</span> <span class="r-out co"><span class="r-pr">#></span> 13 -0.92086539 -16.640945 -34.320501</span> <span class="r-out co"><span class="r-pr">#></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">#></span> <span style="color: #949494;"># A tibble: 39 × 11</span></span> <span class="r-out co"><span class="r-pr">#></span> GEN REP PH ED EL CL CW KW NR TKW NKE</span> <span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><fct></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span></span> <span class="r-out co"><span class="r-pr">#></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">#></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">#></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">#></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">#></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">#></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">#></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">#></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">#></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">#></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">#></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"><-</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">#></span> Evaluating trait YIELD |=========================================| 100% 00:00:00 </span> <span class="r-msg co"><span class="r-pr">#></span> Method: REML/BLUP</span> <span class="r-msg co"><span class="r-pr">#></span> Random effects: GEN, BLOCK(REP)</span> <span class="r-msg co"><span class="r-pr">#></span> Fixed effects: REP</span> <span class="r-msg co"><span class="r-pr">#></span> Denominador DF: Satterthwaite's method</span> <span class="r-out co"><span class="r-pr">#></span> ---------------------------------------------------------------------------</span> <span class="r-out co"><span class="r-pr">#></span> P-values for Likelihood Ratio Test of the analyzed traits</span> <span class="r-out co"><span class="r-pr">#></span> ---------------------------------------------------------------------------</span> <span class="r-out co"><span class="r-pr">#></span> model YIELD</span> <span class="r-out co"><span class="r-pr">#></span> Complete NA</span> <span class="r-out co"><span class="r-pr">#></span> Genotype 1.18e-06</span> <span class="r-out co"><span class="r-pr">#></span> rep:block 3.35e-03</span> <span class="r-out co"><span class="r-pr">#></span> ---------------------------------------------------------------------------</span> <span class="r-out co"><span class="r-pr">#></span> All variables with significant (p < 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">#></span> Class of the model: gamem</span> <span class="r-msg co"><span class="r-pr">#></span> Variable extracted: genpar</span> <span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># A tibble: 13 × 2</span></span> <span class="r-out co"><span class="r-pr">#></span> Parameters YIELD</span> <span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><dbl></span></span> <span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 1</span> Gen_var 0.143 </span> <span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 2</span> Gen (%) 48.5 </span> <span class="r-out co"><span class="r-pr">#></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">#></span> <span style="color: #BCBCBC;"> 4</span> rep:block (%) 23.8 </span> <span class="r-out co"><span class="r-pr">#></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">#></span> <span style="color: #BCBCBC;"> 6</span> Res (%) 27.7 </span> <span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 7</span> Phen_var 0.295 </span> <span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 8</span> H2 0.485 </span> <span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 9</span> h2mg 0.798 </span> <span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">10</span> Accuracy 0.893 </span> <span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">11</span> CVg 8.44 </span> <span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">12</span> CVr 6.38 </span> <span class="r-out co"><span class="r-pr">#></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">#></span> Class of the model: gamem</span> <span class="r-msg co"><span class="r-pr">#></span> Variable extracted: ranef</span> <span class="r-out co"><span class="r-pr">#></span> $GEN</span> <span class="r-out co"><span class="r-pr">#></span> GEN YIELD</span> <span class="r-out co"><span class="r-pr">#></span> 1 G01 0.501183769</span> <span class="r-out co"><span class="r-pr">#></span> 2 G02 0.004962705</span> <span class="r-out co"><span class="r-pr">#></span> 3 G03 -0.784562783</span> <span class="r-out co"><span class="r-pr">#></span> 4 G04 0.006125660</span> <span class="r-out co"><span class="r-pr">#></span> 5 G05 0.474950041</span> <span class="r-out co"><span class="r-pr">#></span> 6 G06 0.044640383</span> <span class="r-out co"><span class="r-pr">#></span> 7 G07 -0.308947691</span> <span class="r-out co"><span class="r-pr">#></span> 8 G08 0.062229524</span> <span class="r-out co"><span class="r-pr">#></span> 9 G09 -0.809931603</span> <span class="r-out co"><span class="r-pr">#></span> 10 G10 -0.089373059</span> <span class="r-out co"><span class="r-pr">#></span> 11 G11 -0.196434546</span> <span class="r-out co"><span class="r-pr">#></span> 12 G12 0.225758446</span> <span class="r-out co"><span class="r-pr">#></span> 13 G13 0.231664921</span> <span class="r-out co"><span class="r-pr">#></span> 14 G14 0.243399964</span> <span class="r-out co"><span class="r-pr">#></span> 15 G15 0.424699859</span> <span class="r-out co"><span class="r-pr">#></span> 16 G16 0.200964673</span> <span class="r-out co"><span class="r-pr">#></span> 17 G17 0.078077967</span> <span class="r-out co"><span class="r-pr">#></span> 18 G18 -0.110180929</span> <span class="r-out co"><span class="r-pr">#></span> 19 G19 0.289576067</span> <span class="r-out co"><span class="r-pr">#></span> 20 G20 -0.338969056</span> <span class="r-out co"><span class="r-pr">#></span> 21 G21 0.256132122</span> <span class="r-out co"><span class="r-pr">#></span> 22 G22 0.024088815</span> <span class="r-out co"><span class="r-pr">#></span> 23 G23 -0.176997620</span> <span class="r-out co"><span class="r-pr">#></span> 24 G24 -0.253057630</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> $REP_BLOCK</span> <span class="r-out co"><span class="r-pr">#></span> REP BLOCK YIELD</span> <span class="r-out co"><span class="r-pr">#></span> 1 R1 B1 0.123136175</span> <span class="r-out co"><span class="r-pr">#></span> 2 R1 B2 -0.141225413</span> <span class="r-out co"><span class="r-pr">#></span> 3 R1 B3 -0.150394401</span> <span class="r-out co"><span class="r-pr">#></span> 4 R1 B4 -0.106755541</span> <span class="r-out co"><span class="r-pr">#></span> 5 R1 B5 0.073704281</span> <span class="r-out co"><span class="r-pr">#></span> 6 R1 B6 0.201534899</span> <span class="r-out co"><span class="r-pr">#></span> 7 R2 B1 -0.532640774</span> <span class="r-out co"><span class="r-pr">#></span> 8 R2 B2 -0.301232978</span> <span class="r-out co"><span class="r-pr">#></span> 9 R2 B3 0.243239346</span> <span class="r-out co"><span class="r-pr">#></span> 10 R2 B4 0.134878440</span> <span class="r-out co"><span class="r-pr">#></span> 11 R2 B5 0.275336937</span> <span class="r-out co"><span class="r-pr">#></span> 12 R2 B6 0.180419028</span> <span class="r-out co"><span class="r-pr">#></span> 13 R3 B1 0.050569780</span> <span class="r-out co"><span class="r-pr">#></span> 14 R3 B2 -0.047784038</span> <span class="r-out co"><span class="r-pr">#></span> 15 R3 B3 0.151079007</span> <span class="r-out co"><span class="r-pr">#></span> 16 R3 B4 0.053760694</span> <span class="r-out co"><span class="r-pr">#></span> 17 R3 B5 -0.008047649</span> <span class="r-out co"><span class="r-pr">#></span> 18 R3 B6 -0.199577794</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> $GEN_REP_BLOCK</span> <span class="r-out co"><span class="r-pr">#></span> GEN REP BLOCK YIELD</span> <span class="r-out co"><span class="r-pr">#></span> 1 G01 R1 B5 0.57488805</span> <span class="r-out co"><span class="r-pr">#></span> 2 G01 R2 B4 0.63606221</span> <span class="r-out co"><span class="r-pr">#></span> 3 G01 R3 B1 0.55175355</span> <span class="r-out co"><span class="r-pr">#></span> 4 G02 R1 B2 -0.13626271</span> <span class="r-out co"><span class="r-pr">#></span> 5 G02 R2 B5 0.28029964</span> <span class="r-out co"><span class="r-pr">#></span> 6 G02 R3 B2 -0.04282133</span> <span class="r-out co"><span class="r-pr">#></span> 7 G03 R1 B4 -0.89131832</span> <span class="r-out co"><span class="r-pr">#></span> 8 G03 R2 B2 -1.08579576</span> <span class="r-out co"><span class="r-pr">#></span> 9 G03 R3 B6 -0.98414058</span> <span class="r-out co"><span class="r-pr">#></span> 10 G04 R1 B1 0.12926184</span> <span class="r-out co"><span class="r-pr">#></span> 11 G04 R2 B1 -0.52651511</span> <span class="r-out co"><span class="r-pr">#></span> 12 G04 R3 B3 0.15720467</span> <span class="r-out co"><span class="r-pr">#></span> 13 G05 R1 B1 0.59808622</span> <span class="r-out co"><span class="r-pr">#></span> 14 G05 R2 B4 0.60982848</span> <span class="r-out co"><span class="r-pr">#></span> 15 G05 R3 B6 0.27537225</span> <span class="r-out co"><span class="r-pr">#></span> 16 G06 R1 B6 0.24617528</span> <span class="r-out co"><span class="r-pr">#></span> 17 G06 R2 B6 0.22505941</span> <span class="r-out co"><span class="r-pr">#></span> 18 G06 R3 B3 0.19571939</span> <span class="r-out co"><span class="r-pr">#></span> 19 G07 R1 B5 -0.23524341</span> <span class="r-out co"><span class="r-pr">#></span> 20 G07 R2 B6 -0.12852866</span> <span class="r-out co"><span class="r-pr">#></span> 21 G07 R3 B6 -0.50852549</span> <span class="r-out co"><span class="r-pr">#></span> 22 G08 R1 B4 -0.04452602</span> <span class="r-out co"><span class="r-pr">#></span> 23 G08 R2 B1 -0.47041125</span> <span class="r-out co"><span class="r-pr">#></span> 24 G08 R3 B2 0.01444549</span> <span class="r-out co"><span class="r-pr">#></span> 25 G09 R1 B6 -0.60839670</span> <span class="r-out co"><span class="r-pr">#></span> 26 G09 R2 B4 -0.67505316</span> <span class="r-out co"><span class="r-pr">#></span> 27 G09 R3 B2 -0.85771564</span> <span class="r-out co"><span class="r-pr">#></span> 28 G10 R1 B2 -0.23059847</span> <span class="r-out co"><span class="r-pr">#></span> 29 G10 R2 B4 0.04550538</span> <span class="r-out co"><span class="r-pr">#></span> 30 G10 R3 B4 -0.03561236</span> <span class="r-out co"><span class="r-pr">#></span> 31 G11 R1 B1 -0.07329837</span> <span class="r-out co"><span class="r-pr">#></span> 32 G11 R2 B3 0.04680480</span> <span class="r-out co"><span class="r-pr">#></span> 33 G11 R3 B1 -0.14586477</span> <span class="r-out co"><span class="r-pr">#></span> 34 G12 R1 B6 0.42729335</span> <span class="r-out co"><span class="r-pr">#></span> 35 G12 R2 B3 0.46899779</span> <span class="r-out co"><span class="r-pr">#></span> 36 G12 R3 B4 0.27951914</span> <span class="r-out co"><span class="r-pr">#></span> 37 G13 R1 B4 0.12490938</span> <span class="r-out co"><span class="r-pr">#></span> 38 G13 R2 B5 0.50700186</span> <span class="r-out co"><span class="r-pr">#></span> 39 G13 R3 B4 0.28542562</span> <span class="r-out co"><span class="r-pr">#></span> 40 G14 R1 B3 0.09300556</span> <span class="r-out co"><span class="r-pr">#></span> 41 G14 R2 B1 -0.28924081</span> <span class="r-out co"><span class="r-pr">#></span> 42 G14 R3 B1 0.29396974</span> <span class="r-out co"><span class="r-pr">#></span> 43 G15 R1 B5 0.49840414</span> <span class="r-out co"><span class="r-pr">#></span> 44 G15 R2 B2 0.12346688</span> <span class="r-out co"><span class="r-pr">#></span> 45 G15 R3 B2 0.37691582</span> <span class="r-out co"><span class="r-pr">#></span> 46 G16 R1 B3 0.05057027</span> <span class="r-out co"><span class="r-pr">#></span> 47 G16 R2 B6 0.38138370</span> <span class="r-out co"><span class="r-pr">#></span> 48 G16 R3 B5 0.19291702</span> <span class="r-out co"><span class="r-pr">#></span> 49 G17 R1 B5 0.15178225</span> <span class="r-out co"><span class="r-pr">#></span> 50 G17 R2 B3 0.32131731</span> <span class="r-out co"><span class="r-pr">#></span> 51 G17 R3 B3 0.22915697</span> <span class="r-out co"><span class="r-pr">#></span> 52 G18 R1 B3 -0.26057533</span> <span class="r-out co"><span class="r-pr">#></span> 53 G18 R2 B5 0.16515601</span> <span class="r-out co"><span class="r-pr">#></span> 54 G18 R3 B3 0.04089808</span> <span class="r-out co"><span class="r-pr">#></span> 55 G19 R1 B4 0.18282053</span> <span class="r-out co"><span class="r-pr">#></span> 56 G19 R2 B6 0.46999510</span> <span class="r-out co"><span class="r-pr">#></span> 57 G19 R3 B1 0.34014585</span> <span class="r-out co"><span class="r-pr">#></span> 58 G20 R1 B2 -0.48019447</span> <span class="r-out co"><span class="r-pr">#></span> 59 G20 R2 B1 -0.87160983</span> <span class="r-out co"><span class="r-pr">#></span> 60 G20 R3 B6 -0.53854685</span> <span class="r-out co"><span class="r-pr">#></span> 61 G21 R1 B2 0.11490671</span> <span class="r-out co"><span class="r-pr">#></span> 62 G21 R2 B3 0.49937147</span> <span class="r-out co"><span class="r-pr">#></span> 63 G21 R3 B5 0.24808447</span> <span class="r-out co"><span class="r-pr">#></span> 64 G22 R1 B1 0.14722499</span> <span class="r-out co"><span class="r-pr">#></span> 65 G22 R2 B5 0.29942575</span> <span class="r-out co"><span class="r-pr">#></span> 66 G22 R3 B5 0.01604117</span> <span class="r-out co"><span class="r-pr">#></span> 67 G23 R1 B3 -0.32739202</span> <span class="r-out co"><span class="r-pr">#></span> 68 G23 R2 B2 -0.47823060</span> <span class="r-out co"><span class="r-pr">#></span> 69 G23 R3 B4 -0.12323693</span> <span class="r-out co"><span class="r-pr">#></span> 70 G24 R1 B6 -0.05152273</span> <span class="r-out co"><span class="r-pr">#></span> 71 G24 R2 B2 -0.55429061</span> <span class="r-out co"><span class="r-pr">#></span> 72 G24 R3 B5 -0.26110528</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-in"><span><span class="co"># }</span></span></span> <span class="r-in"><span></span></span> </code></pre></div> </div> </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2> </nav></aside></div> <footer><div class="pkgdown-footer-left"> <p></p><p>Developed by <a 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