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POProbabilty heatmap in ggplot
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2013-01-16T16:37:48.177
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2017-05-23T11:46:46.347
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I asked <a href="https://stackoverflow.com/questions/7305803/plot-probability-heatmap-hexbin-with-different-sized-bins">this question</a> a year ago and got code for this "probability heatmap": <img src="https://i.stack.imgur.com/Jf4Mi.png" alt="heatmap"> <pre><code>numbet <- 32 numtri <- 1e5 prob=5/6 #Fill a matrix xcum <- matrix(NA, nrow=numtri, ncol=numbet+1) for (i in 1:numtri) { x <- sample(c(0,1), numbet, prob=c(prob, 1-prob), replace = TRUE) xcum[i, ] <- c(i, cumsum(x)/cumsum(1:numbet)) } colnames(xcum) <- c("trial", paste("bet", 1:numbet, sep="")) mxcum <- reshape(data.frame(xcum), varying=1+1:numbet, idvar="trial", v.names="outcome", direction="long", timevar="bet") library(plyr) mxcum2 <- ddply(mxcum, .(bet, outcome), nrow) mxcum3 <- ddply(mxcum2, .(bet), summarize, ymin=c(0, head(seq_along(V1)/length(V1), -1)), ymax=seq_along(V1)/length(V1), fill=(V1/sum(V1))) head(mxcum3) library(ggplot2) p <- ggplot(mxcum3, aes(xmin=bet-0.5, xmax=bet+0.5, ymin=ymin, ymax=ymax)) + geom_rect(aes(fill=fill), colour="grey80") + scale_fill_gradient("Outcome", formatter="percent", low="red", high="blue") + scale_y_continuous(formatter="percent") + xlab("Bet") print(p) </code></pre> (May need to change this code slightly because of <a href="https://stackoverflow.com/questions/10146109/formatter-argument-in-scale-continuous-throwing-errors-in-r-2-15">this</a>) This is almost exactly what I want. Except each vertical shaft should have different numbers of bins, ie the first should have 2, second 3, third 4 (N+1). In the graph shaft 6 +7 have the same number of bins (7), where 7 should have 8 (N+1). If I'm right, the reason the code does this is because it is the observed data and if I ran more trials we would get more bins. I don't want to rely on the number of trials to get the correct number of bins. How can I adapt this code to give the correct number of bins?
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<r><ggplot2><probability>
Title
Probabilty heatmap in ggplot
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