darts (version 1.0)

darts-package: Statistical Tools to Analyze Your Darts Game

Description

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".

Details

ll{ Package: darts Type: Package Version: 1.0 Date: 2011-01-17 License: GPL LazyLoad: yes }

References

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/

Examples

# 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)