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    <p>FYI: This is more of an extended comment than an answer. </p> <p>To me, this new plot looks like a stacked bar where each bar's height is equal to the intersection points of the upper and lower line at the next trial. </p> <p><img src="https://i.stack.imgur.com/LKZQf.png" alt="enter image description here"></p> <p>The way that I would approach this is to treat "Trials" as a categorical variable. Then we can search each row of xcum for elements that are equal. If they are, then we can consider this to be a point of intersection whose minima also represents the multiple defining the height of our bars.</p> <pre><code>x &lt;- t(xcum) x &lt;- x[duplicated(x),] x[x==0] &lt;- NA </code></pre> <p>Now we have the multiples of the actual points, we need to figure out how to take it to the next step and find a way of binning the information. That means we need to make a decision about how many points will represent each grouping. Let's write some points out for posterity.</p> <pre><code>Trial 1 (2) = 1, 0.5 # multiple = 0.5 Trial 2 (3) = 1, 0.66, 0.33 # multiple = 0.33 Trial 3 (4) = 1, 0.75, 0.5, 0.25 # multiple = 0.25 Trial 4 (5) = 1, 0.8, 0.6, 0.4, 0.2 # multiple = 0.2 Trial 5 (6) = 1, 0.8333335, 0.6666668, 0.5000001, 0.3333334, 0.1666667 ... Trial 36 (35) = 1, 0.9722223, ..., 0.02777778 # mutiple = 0.05555556 / 2 </code></pre> <p>In other words, for each Trial there are n-1 points to plot. In your drawing you have 7 bins. So we need to figure out the multiples for each bin.</p> <p>Let's cheat and divide the last two columns by two, we know from visual inspection that the minima is lower than 0.05</p> <p><code>x[,35:36] &lt;- x[,35:36] / 2</code></p> <p>Then find the minimum of each column:</p> <pre><code>x &lt;- apply(x, 2, function(x) min(x, na.rm=T))[-1] # Drop the 1 x &lt;- x[c(1,2,3,4,8,17,35)] # I'm just guessing here by the "look" of your drawing. </code></pre> <p>The clearest way to do this is to create each bin separately. Obviously, this could be done automatically later. Remembering that each point is</p> <pre><code>bin1 &lt;- data.frame(bin = rep("bin1",2), Frequency = rep(x[1],2)) bin2 &lt;- data.frame(bin = rep("bin2",3), Frequency = rep(x[2],3)) bin3 &lt;- data.frame(bin = rep("bin3",4), Frequency = rep(x[3],4)) bin4 &lt;- data.frame(bin = rep("bin4",5), Frequency = rep(x[4],5)) bin5 &lt;- data.frame(bin = rep("bin5",9), Frequency = rep(x[5],9)) bin6 &lt;- data.frame(bin = rep("bin6",18), Frequency = rep(x[6],18)) bin7 &lt;- data.frame(bin = rep("bin7",36), Frequency = rep(x[7],36)) df &lt;- rbind(bin1,bin2,bin3,bin4,bin5,bin6,bin7) ggplot(df, aes(bin, Frequency, color=Frequency)) + geom_bar(stat="identity", position="stack") </code></pre>
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    1. COI will have to give your answer some thought. I have clarified what I want from the plot, if that helps people understand why I am not quite happy with what I already have. Thanks.
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