This is really a statistics problem, so I wonder which R packages can be employed best to solve and visualize it. I run a lot of simulations to approach the truth. The truth is a result of very complex computations, and is a real number. The closer it is to 0, the truthier it is. Each simulations has a set of features, some of which are not available for all simulations. Some of the features are numeric (week), some boolean (utility), while others are factors. Each simulation has the final value, the dm column in the data frame. The names of the simulations are rownames of the data frame, and feature names are the column names. Here's the dataframe: http://dl.dropbox.com/u/9300701/Data/sf.dm.pos.r You read it in R with sf <- read.table(sf.dm.pos.r) Seeking the truth questions: -- What kinds of GLM and other models can we run to determine which features are most contributing to the truth, i.e. making dm closer to 0? -- What kind of clustering can emphasize the most contributing features? -- What kind of visualizations can be used to make it clear which features affect the truth the most, and in which combinations? What kind of color visualizations are there to make the truth even clearer? Cheers, Alexy [[alternative HTML version deleted]]