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<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
<meta charset="utf-8">
<meta name="generator" content="quarto-1.3.353">
<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
<title>ME1 / White high-tier</title>
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<main class="content" id="quarto-document-content">
<header id="title-block-header" class="quarto-title-block default">
<div class="quarto-title">
<h1 class="title">ME1 / White high-tier</h1>
</div>
<div class="quarto-title-meta">
</div>
</header>
<ul>
<li>LDSR_SETOTU; EROTP_RMX; LDSR_CERCZM; SMUTN_SUB_SPHTRE; LDSR_PUCCSO</li>
<li>ldsr: setotu, puccso</li>
<li>sdp: fusasp, zzxxff</li>
</ul>
<p>Data-points</p>
<div class="cell">
<div class="cell-output-display">
<p><img src="norla_ME1_2024_files/figure-html/data-points-1.png" class="img-fluid" width="672"></p>
</div>
</div>
<section id="yld" class="level2">
<h2 class="anchored" data-anchor-id="yld">YLD</h2>
<div class="cell">
<div class="cell-output cell-output-stdout">
<pre><code><list_of<
tbl_df<
commercialName: factor<b3a99>
env_index : factor<4ca89>
emmean : double
SE : double
df : double
lower.CL : double
upper.CL : double
.group : character
rank : integer
>
>[3]>
[[1]]
# A tibble: 14 × 9
commercialName env_index emmean SE df lower.CL upper.CL .group rank
<fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <int>
1 MX8639 <130 125. 3.02 230. 120. 131. " a " 1
2 MY8509 <130 124. 3.02 230. 118. 130. " ab " 2
3 MX8438 <130 124. 3.02 230. 118. 130. " abc " 3
4 HIPOPOTAMO <130 121. 3.02 230. 115. 127. " abcd" 4
5 MX8436 <130 120. 3.02 230. 114. 126. " abcd" 4
6 VY8502 <130 117. 3.02 230. 111. 123. " abcd" 4
7 DK5021 <130 117. 3.02 230. 111. 123. " abcd" 4
8 DK4050 <130 117. 3.02 230. 111. 123. " abcd" 4
9 VY8503 <130 115. 3.17 272. 109. 122. " abcd" 4
10 P3270W <130 115. 3.02 230. 109. 121. " abcd" 4
11 MX8432 <130 114. 3.02 230. 108. 120. " bcd" 5
12 CALAMAR <130 114. 3.02 230. 108. 120. " bcd" 5
13 VX8614 <130 113. 3.02 230. 107. 119. " cd" 6
14 P3274W <130 113. 3.02 230. 107. 119. " d" 7
[[2]]
# A tibble: 14 × 9
commercialName env_index emmean SE df lower.CL upper.CL .group rank
<fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <int>
1 MX8438 130-160 153. 2.28 219. 149. 158. " a " 1
2 MY8509 130-160 151. 2.28 219. 147. 156. " ab " 2
3 MX8639 130-160 150. 2.28 219. 145. 154. " abc " 3
4 MX8436 130-160 149. 2.28 219. 144. 153. " abcd " 4
5 VY8502 130-160 146. 2.28 219. 142. 151. " abcde " 5
6 MX8432 130-160 146. 2.28 219. 141. 150. " abcde " 5
7 HIPOPOTAMO 130-160 145. 2.28 219. 141. 150. " bcde " 6
8 DK5021 130-160 144. 2.28 219. 140. 149. " bcde " 6
9 CALAMAR 130-160 144. 2.28 219. 139. 148. " bcde " 6
10 DK4050 130-160 142. 2.28 219. 137. 146. " cdef" 7
11 P3274W 130-160 142. 2.28 219. 137. 146. " def" 8
12 VY8503 130-160 140. 2.30 225. 136. 145. " ef" 9
13 P3270W 130-160 138. 2.28 219. 134. 143. " ef" 9
14 VX8614 130-160 136. 2.30 225. 131. 140. " f" 10
[[3]]
# A tibble: 14 × 9
commercialName env_index emmean SE df lower.CL upper.CL .group rank
<fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <int>
1 MY8509 >160 182. 6.03 230. 170. 194. " a " 1
2 MX8438 >160 182. 6.03 230. 170. 194. " a " 1
3 MX8639 >160 179. 6.03 230. 167. 190. " ab" 2
4 MX8436 >160 176. 6.03 230. 164. 188. " ab" 2
5 VY8503 >160 173. 6.03 230. 161. 185. " ab" 2
6 VY8502 >160 171. 6.03 230. 159. 183. " ab" 2
7 HIPOPOTAMO >160 170. 6.03 230. 158. 182. " ab" 2
8 MX8432 >160 170. 6.03 230. 158. 182. " ab" 2
9 P3270W >160 167. 6.03 230. 156. 179. " ab" 2
10 CALAMAR >160 167. 6.03 230. 155. 179. " ab" 2
11 DK4050 >160 167. 6.03 230. 155. 178. " ab" 2
12 VX8614 >160 166. 6.03 230. 154. 177. " ab" 2
13 DK5021 >160 165. 6.03 230. 153. 177. " ab" 2
14 P3274W >160 158. 6.03 230. 146. 170. " b" 3</code></pre>
</div>
</div>
</section>
<section id="evolución-de-enfermedades" class="level2">
<h2 class="anchored" data-anchor-id="evolución-de-enfermedades">Evolución de enfermedades</h2>
<div class="cell">
<div class="cell-output-display">
<p><img src="norla_ME1_2024_files/figure-html/unnamed-chunk-7-1.png" class="img-fluid" width="672"></p>
</div>
</div>
</section>
<section id="sdp_fusasp" class="level2">
<h2 class="anchored" data-anchor-id="sdp_fusasp">SDP_FUSASP</h2>
<p>Datos crudos: Cuanto mas oscuro >> MEJOR (menos enfermedad) Se funsionan ‘SDP_ZZXXFF’ a ‘SDP_FUSASP’</p>
<div class="cell">
<div class="cell-output-display">
<p><img src="norla_ME1_2024_files/figure-html/unnamed-chunk-8-1.png" class="img-fluid" width="960"></p>
</div>
</div>
<div class="cell">
<div class="cell-output cell-output-stdout">
<pre><code> commercialName emmean SE df lower.CL upper.CL .group rank
1 CALAMAR 5.521739 2.115847 74.93926 1.306699 9.736779 a 1
13 VY8502 6.739130 2.115847 74.93926 2.524091 10.954170 a 1
14 VY8503 7.326087 2.115847 74.93926 3.111047 11.541127 a 1
12 VX8614 7.673913 2.115847 74.93926 3.458873 11.888953 a 1
9 MY8509 12.456522 2.115847 74.93926 8.241482 16.671561 ab 2
8 MX8639 12.717391 2.115847 74.93926 8.502352 16.932431 ab 2
10 P3270W 15.369565 2.115847 74.93926 11.154525 19.584605 bc 3
11 P3274W 15.521739 2.115847 74.93926 11.306699 19.736779 bc 3
4 HIPOPOTAMO 16.608696 2.115847 74.93926 12.393656 20.823735 bc 3
3 DK5021 18.195652 2.115847 74.93926 13.980612 22.410692 bc 3
2 DK4050 18.391304 2.115847 74.93926 14.176265 22.606344 bc 3
7 MX8438 19.000000 2.115847 74.93926 14.784960 23.215040 bc 3
5 MX8432 21.456522 2.115847 74.93926 17.241482 25.671561 c 4
6 MX8436 22.260870 2.115847 74.93926 18.045830 26.475909 c 4</code></pre>
</div>
<div class="cell-output-display">
<p><img src="norla_ME1_2024_files/figure-html/unnamed-chunk-9-1.png" class="img-fluid" width="960"></p>
</div>
</div>
<section id="efecto-de-la-cantidad-de-riegos-sobre-muerte-subita" class="level3">
<h3 class="anchored" data-anchor-id="efecto-de-la-cantidad-de-riegos-sobre-muerte-subita">Efecto de la cantidad de riegos sobre muerte subita</h3>
<div class="cell">
<div class="cell-output-display">
<p><img src="norla_ME1_2024_files/figure-html/unnamed-chunk-10-1.png" class="img-fluid" width="672"></p>
</div>
<div class="cell-output cell-output-stdout">
<pre><code>Analysis of Variance Table
Response: OBS_numValue
Df Sum Sq Mean Sq F value Pr(>F)
factor(no_riegos) 2 2610.7 1305.33 13.2022 3.318e-06 ***
commercialName 13 9410.8 723.91 7.3217 2.415e-12 ***
factor(no_riegos):commercialName 26 1418.9 54.57 0.5519 0.9644
Residuals 280 27684.2 98.87
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
</div>
</div>
<div class="cell">
<div class="cell-output-display">
<table class="table table-sm table-striped small">
<thead>
<tr class="header">
<th style="text-align: right;">no_riegos</th>
<th style="text-align: right;">emmean</th>
<th style="text-align: right;">SE</th>
<th style="text-align: right;">df</th>
<th style="text-align: right;">lower.CL</th>
<th style="text-align: right;">upper.CL</th>
<th style="text-align: left;">.group</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: right;">4</td>
<td style="text-align: right;">9.053571</td>
<td style="text-align: right;">1.3287486</td>
<td style="text-align: right;">280</td>
<td style="text-align: right;">6.437966</td>
<td style="text-align: right;">11.66918</td>
<td style="text-align: left;">a</td>
</tr>
<tr class="even">
<td style="text-align: right;">3</td>
<td style="text-align: right;">14.573661</td>
<td style="text-align: right;">0.6643743</td>
<td style="text-align: right;">280</td>
<td style="text-align: right;">13.265858</td>
<td style="text-align: right;">15.88146</td>
<td style="text-align: left;">b</td>
</tr>
<tr class="odd">
<td style="text-align: right;">2</td>
<td style="text-align: right;">19.309524</td>
<td style="text-align: right;">1.5343067</td>
<td style="text-align: right;">280</td>
<td style="text-align: right;">16.289283</td>
<td style="text-align: right;">22.32976</td>
<td style="text-align: left;">c</td>
</tr>
</tbody>
</table>
</div>
</div>
<blockquote class="blockquote">
<p>hay efecto simple de ambos factores: genotipo y nro de riegos (a mas riegos menos FUSASP)</p>
</blockquote>
</section>
</section>
<section id="ldsr_setotu" class="level2">
<h2 class="anchored" data-anchor-id="ldsr_setotu">LDSR_SETOTU</h2>
<div class="cell">
<div class="cell-output-display">
<p><img src="norla_ME1_2024_files/figure-html/unnamed-chunk-12-1.png" class="img-fluid" width="672"></p>
</div>
</div>
<blockquote class="blockquote">
<p>pocos datos</p>
</blockquote>
<div class="cell">
<div class="cell-output-display">
<p><img src="norla_ME1_2024_files/figure-html/unnamed-chunk-13-1.png" class="img-fluid" width="672"></p>
</div>
</div>
</section>
<section id="ldsr_puccso" class="level2">
<h2 class="anchored" data-anchor-id="ldsr_puccso">LDSR_PUCCSO</h2>
<div class="cell">
<div class="cell-output-display">
<p><img src="norla_ME1_2024_files/figure-html/unnamed-chunk-14-1.png" class="img-fluid" width="672"></p>
</div>
</div>
<div class="cell">
<div class="cell-output-display">
<p><img src="norla_ME1_2024_files/figure-html/unnamed-chunk-16-1.png" class="img-fluid" width="1152"></p>
</div>
</div>
<div class="cell">
<div class="cell-output cell-output-stdout">
<pre><code> commercialName emmean SE df lower.CL upper.CL .group rank
14 VY8503 1.5 0.2281743 110.434 1.047832 1.952168 a 1
1 CALAMAR 1.8 0.2281743 110.434 1.347832 2.252168 ab 2
12 VX8614 2.1 0.2281743 110.434 1.647832 2.552168 abc 3
8 MX8639 2.2 0.2281743 110.434 1.747832 2.652168 abc 3
11 P3274W 2.2 0.2281743 110.434 1.747832 2.652168 abc 3
5 MX8432 2.3 0.2281743 110.434 1.847832 2.752168 abc 3
7 MX8438 2.3 0.2281743 110.434 1.847832 2.752168 abc 3
10 P3270W 2.5 0.2281743 110.434 2.047832 2.952168 abc 3
13 VY8502 2.5 0.2281743 110.434 2.047832 2.952168 abc 3
4 HIPOPOTAMO 2.6 0.2281743 110.434 2.147832 3.052168 bc 4
9 MY8509 2.6 0.2281743 110.434 2.147832 3.052168 bc 4
6 MX8436 2.8 0.2281743 110.434 2.347832 3.252168 bc 4
3 DK5021 2.9 0.2281743 110.434 2.447832 3.352168 c 5
2 DK4050 3.1 0.2281743 110.434 2.647832 3.552168 c 5</code></pre>
</div>
<div class="cell-output-display">
<p><img src="norla_ME1_2024_files/figure-html/unnamed-chunk-17-1.png" class="img-fluid" width="672"></p>
</div>
</div>
</section>
<section id="tabla-resumen" class="level2">
<h2 class="anchored" data-anchor-id="tabla-resumen">Tabla resumen</h2>
<p>Cada celda contiene su valor estimado en el ajuste de modelo y debajo el ranking segun test de Tukey.</p>
<div class="cell">
<div class="cell-output-display">
<p><img src="norla_ME1_2024_files/figure-html/unnamed-chunk-18-1.png" class="img-fluid" width="864"></p>
</div>
</div>
</section>
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for (var i=0; i<noterefs.length; i++) {
const ref = noterefs[i];
tippyHover(ref, function() {
// use id or data attribute instead here
let href = ref.getAttribute('data-footnote-href') || ref.getAttribute('href');
try { href = new URL(href).hash; } catch {}
const id = href.replace(/^#\/?/, "");
const note = window.document.getElementById(id);
return note.innerHTML;
});
}
let selectedAnnoteEl;
const selectorForAnnotation = ( cell, annotation) => {
let cellAttr = 'data-code-cell="' + cell + '"';
let lineAttr = 'data-code-annotation="' + annotation + '"';
const selector = 'span[' + cellAttr + '][' + lineAttr + ']';
return selector;
}
const selectCodeLines = (annoteEl) => {
const doc = window.document;
const targetCell = annoteEl.getAttribute("data-target-cell");
const targetAnnotation = annoteEl.getAttribute("data-target-annotation");
const annoteSpan = window.document.querySelector(selectorForAnnotation(targetCell, targetAnnotation));
const lines = annoteSpan.getAttribute("data-code-lines").split(",");
const lineIds = lines.map((line) => {
return targetCell + "-" + line;
})
let top = null;
let height = null;
let parent = null;
if (lineIds.length > 0) {
//compute the position of the single el (top and bottom and make a div)
const el = window.document.getElementById(lineIds[0]);
top = el.offsetTop;
height = el.offsetHeight;
parent = el.parentElement.parentElement;
if (lineIds.length > 1) {
const lastEl = window.document.getElementById(lineIds[lineIds.length - 1]);
const bottom = lastEl.offsetTop + lastEl.offsetHeight;
height = bottom - top;
}
if (top !== null && height !== null && parent !== null) {
// cook up a div (if necessary) and position it
let div = window.document.getElementById("code-annotation-line-highlight");
if (div === null) {
div = window.document.createElement("div");
div.setAttribute("id", "code-annotation-line-highlight");
div.style.position = 'absolute';
parent.appendChild(div);
}
div.style.top = top - 2 + "px";
div.style.height = height + 4 + "px";
let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter");
if (gutterDiv === null) {
gutterDiv = window.document.createElement("div");
gutterDiv.setAttribute("id", "code-annotation-line-highlight-gutter");
gutterDiv.style.position = 'absolute';
const codeCell = window.document.getElementById(targetCell);
const gutter = codeCell.querySelector('.code-annotation-gutter');
gutter.appendChild(gutterDiv);
}
gutterDiv.style.top = top - 2 + "px";
gutterDiv.style.height = height + 4 + "px";
}
selectedAnnoteEl = annoteEl;
}
};
const unselectCodeLines = () => {
const elementsIds = ["code-annotation-line-highlight", "code-annotation-line-highlight-gutter"];
elementsIds.forEach((elId) => {
const div = window.document.getElementById(elId);
if (div) {
div.remove();
}
});
selectedAnnoteEl = undefined;
};
// Attach click handler to the DT
const annoteDls = window.document.querySelectorAll('dt[data-target-cell]');
for (const annoteDlNode of annoteDls) {
annoteDlNode.addEventListener('click', (event) => {
const clickedEl = event.target;
if (clickedEl !== selectedAnnoteEl) {
unselectCodeLines();
const activeEl = window.document.querySelector('dt[data-target-cell].code-annotation-active');
if (activeEl) {
activeEl.classList.remove('code-annotation-active');
}
selectCodeLines(clickedEl);
clickedEl.classList.add('code-annotation-active');
} else {
// Unselect the line
unselectCodeLines();
clickedEl.classList.remove('code-annotation-active');
}
});
}
const findCites = (el) => {
const parentEl = el.parentElement;
if (parentEl) {
const cites = parentEl.dataset.cites;
if (cites) {
return {
el,
cites: cites.split(' ')
};
} else {
return findCites(el.parentElement)
}
} else {
return undefined;
}
};
var bibliorefs = window.document.querySelectorAll('a[role="doc-biblioref"]');
for (var i=0; i<bibliorefs.length; i++) {
const ref = bibliorefs[i];
const citeInfo = findCites(ref);
if (citeInfo) {
tippyHover(citeInfo.el, function() {
var popup = window.document.createElement('div');
citeInfo.cites.forEach(function(cite) {
var citeDiv = window.document.createElement('div');
citeDiv.classList.add('hanging-indent');
citeDiv.classList.add('csl-entry');
var biblioDiv = window.document.getElementById('ref-' + cite);
if (biblioDiv) {
citeDiv.innerHTML = biblioDiv.innerHTML;
}
popup.appendChild(citeDiv);
});
return popup.innerHTML;
});
}
}
});
</script>
</div> <!-- /content -->
</body></html>