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Hello, I have a survey in which a number of people rated a set of items on a 1..5 scale. I believe it would be desirable to argue that the people's responses are correlated, and thus that the rating task makes sense to people. Is there a standard approach to this? With only 2 people, the correlation coefficient between their responses would be an interpretable number, (though probably

Hi, Is there something like a hashtable or (python) dictionary in R/Splus? (If not, is there a reason why it's not needed / typical way to accomplish the same thing?) Thank you

Hi, a problem involving postscript bounding boxes: I'm composing three scatterplots into a single figure, postsript for publication. The individual scatterplots should be square, so the overall figure should have a roughly 1:3 sort of aspect ratio. By default however, the overall figure comes out nearly square, and the scatterplots are stretched vertically. I fixed this by adding

Hello, I'm curious if anyone has encounted a version of this problem (and it's solution) involving finding a consistent set of scales for subsets of survey data. The goal is to obtain peoples' rankings of pairwise similarity of a large number of items, on a 1..5 scale for example, and average these across people to use as input to MDS: How similar is object A to B on a 1..5

Hello, I'm curious if anyone has encounted a version of this problem (and it's solution) involving finding a consistent set of scales for subsets of survey data. The goal is to obtain peoples' rankings of pairwise similarity of a large number of items, on a 1..5 scale for example. How similar is object A to B on a 1..5 scale ___ How similar is object A to C on a 1..5

Hi, Apology for this question being off the topic (OT) of R, though I expect this list might be the best place on the net to ask this question. In brief, the question is: what classification algorithm can one use if the features are histograms? I have a classification problem, and believe that histograms of the distribution of some values may be the best "feature" to use. To

Hello, suppose one is forming a probability p(x,y), where the x,y axes are somewhat accidental and rotation is possible. I'm thinking about whether the discrete entropy H(x,y) should change if the probability is rotated in the x,y plane. My current conclusion is that it _does_ change, at least if the entropy is estimated via bins. As a simple example, suppose the probability mass is

Hello I will be applying MDS (actually Isomap) to make a psychological "concept map" of the similarities between N concepts. I would like to scale to a large number of concepts, however, the resulting N*(N-1) pairwise similarities is prohibitive for a user survey. I'm thinking of giving people random subsets of the pairwise similarities. Does anyone have recommendations for this

Hello, I recently posted a question about my troubles with importing a lattice/trellis figure into latex. To recap, The figure contains 3 scatterplots, so it should have roughly a 1:3 sort of aspect ratio, in order to make each of the scatterplots square. Instead, the whole figure comes out roughly square, so each scatterplot is badly stretched. I fixed this by adding aspect=1/1 to the