F & p Plots
PanelCHECK
<div class="calibre2"><span class="calibre3"><span class="calibre4">F & p Plots</span></span></div>
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<div class="calibre2"><span class="calibre7"><span class="calibre8">This function generates F plots, plotting F-values from a one-way ANOVA for each assessor.
Each vertical line is the F-value of one specific assessor for a specific attribute.</span></span></div><div class="calibre2"><span class="calibre7"><span class="calibre8">The F-value is a measure of the ability of an assessor to discriminate between samples. The
higher the F-value, the better.</span></span></div><div class="calibre2"><span class="calibre7"><span class="calibre8"></span></span></div><div class="calibre2"><span class="calibre7"><span class="calibre8">The radio buttons under sorted by, decides whether the F-values are sorted by assessors or
attributes.</span></span></div><div class="calibre2"><span class="calibre7"><span class="calibre8"><br class="calibre10"/></span></span></div><div class="calibre2"><span class="calibre7"><span class="calibre8">The “general plot” in the top of the tree control will plot the F-values in different colours,
whereas by clicking on a specific attribute or assessor (depending on the sorting-mode) will
highlight that specific attribute or assessor.</span></span></div><div class="calibre2"><span class="calibre7"><span class="calibre8">The red and black horizontal lines indicate the significance level at 1% and 5%, respectively.</span></span></div><div class="calibre2"><span class="calibre7"><span class="calibre8"><br class="calibre10"/></span></span></div><div class="calibre2"><span class="calibre7"><span class="calibre8"><br class="calibre10"/></span></span></div><div class="calibre2"><span class="calibre7"><span class="calibre8"><br class="calibre10"/></span></span></div><div class="calibre2"><span class="calibre7"><span class="calibre8"><b class="calibre14">Further reading:</b></span></span></div><div class="calibre2"><span class="calibre7"><span class="calibre8">Oliver Tomic, Asgeir Nilsen, Magni Martens, Tormod Næs, <i class="calibre13">Visualization of sensory profiling
data for performance monitoring</i>, LWT <b class="calibre14">40</b> (2007) 262–269.</span></span></div><div class="calibre2"> </div>
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<a href="introduction.html">Introduction</a>
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<a href="chapter1.html">Getting started</a>
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<a href="chapter2.html">Operating PanelCHECK</a>
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<a href="21.html">Importing data</a>
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<a href="23.html">Enable and disable</a>
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<a href="24.html">Plots and figures</a>
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<a href="25.html">Plots</a>
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<a href="26.html">Overview Plots</a>
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<a href="chapter3.html">Analysis</a>
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<a href="31.html">Univariate</a>
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<a href="32.html">Line Plots</a>
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<a href="33.html">Mean & STD Plots</a>
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<a href="34.html">Correlation Plots</a>
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<a href="35.html">Profile Plots</a>
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<a href="36.html">Eggshell Plots</a>
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<a href="37.html">F & p Plots</a>
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<a href="38.html">MSE Plots</a>
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<a href="39.html">p-MSE Plots</a>
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<a href="310.html">Multivariate</a>
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<a href="311.html">Tucker-1 Plots</a>
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<a href="312.html">Manhattan Plots</a>
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<a href="316.html">STATIS</a>
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<a href="318.html">Mixed Model ANOVA</a>
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