Hello, I have a series of classifications of the same data. I saved this classification in a single dataframe (but it could be a list). X and Y are the variable and Z is the classification by three raters. `I` is the individual identifier of each entry: ``` z1 = c(0,0,0,0,0,1,0,0,0,2, 0,1,1,1,0,0,0,1,0,2, 0,1,1,2,0,0,0,1,0,2, 1,1,1,2,1,0,0,1,1,2, 1,0,0,2,1,1,0,1,2,0) z2 = c(0,0,0,0,0,1,0,0,1,1, 0,1,1,2,0,0,0,1,1,2, 0,0,0,1,0,0,0,1,0,0, 1,2,1,2,1,0,0,1,1,2, 1,0,1,2,1,1,0,1,2,0) z3 = c(0,0,0,2,0,0,0,0,0,2, 0,1,0,2,0,0,0,1,0,2, 0,1,1,2,0,0,0,1,0,2, 1,1,1,2,1,0,0,2,1,2, 2,0,1,1,1,1,0,1,1,0) df = data.frame(X=rep(1:5,3), Y=rep(1:5,3), Z=factor(c(z1,z2,z3)), I =1:150) ``` Is there a way to obtain a kind of heath map for each point? Let's say for the point (x=1,y-1), what was the most common (average) classification? Is it possible to get the 95% CI of that mean? Would Two-Dimensional Kernel Density Estimation be the right path? Thank you
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