darts-package {darts} | R Documentation |
Are you aiming at the right spot on the dartboard? Maybe not! Use this package to compute your optimal aiming location. For a better explanation, go to http://stat.stanford.edu/~ryantibs/darts/ or read the paper "A Statistician Plays Darts".
Package: | darts |
Type: | Package |
Version: | 1.0 |
Date: | 2011-01-17 |
License: | GPL |
LazyLoad: | yes |
Ryan Tibshirani <ryantibs@gmail.com>
Ryan Tibshirani, Andrew Price, and Jonathan Taylor. "A Statistician Plays Darts". Journal of the Royal Statistical Society: Series A, Vol. 174, No. 1, 213-226, 2011.
http://stat.stanford.edu/~ryantibs/darts/
# An example of how to use this package to calculate my variance, and # then generate a personalized heatmap instructing me where to aim # Start with 100 scores from throws aimed at the center of the board x = c(12,16,19,3,17,1,25,19,17,50,18,1,3,17,2,2,13,18,16,2,25,5,5, 1,5,4,17,25,25,50,3,7,17,17,3,3,3,7,11,10,25,1,19,15,4,1,5,12,17,16, 50,20,20,20,25,50,2,17,3,20,20,20,5,1,18,15,2,3,25,12,9,3,3,19,16,20, 5,5,1,4,15,16,5,20,16,2,25,6,12,25,11,25,7,2,5,19,17,17,2,12) #################### # Simple model #################### ## Step 1: EM algorithm # Get my variance in the simple Gaussian model a = simpleEM(x,niter=100) # Check the log likelihood plot(1:a$niter,a$loglik,type="l",xlab="Iteration",ylab="Log likelihood") # The EM estimate of my variance s = a$s.final ## Step 2: Generate a heatmap # Build the matrix of expected scores e = simpleExpScores(s) # Plot it par(mfrow=c(1,2)) drawHeatmap(e) drawBoard(new=TRUE) drawAimSpot(e) #################### # General model #################### ## Step 1: EM algorithm # Get my variance in the general Gaussian model aa = generalEM(x,niter=100,seed=0) # The EM estimate of my covariance matrix Sig = aa$Sig.final ## Step 2: Generate a heatmap # Build the matrix of expected scores ee = generalExpScores(Sig) # Plot it par(mfrow=c(1,2)) drawHeatmap(ee) drawBoard(new=TRUE) drawAimSpot(ee)