darts (version 1.0)

generalEM: EM Algorithm for the General Model

Description

EM algorithm to estimate your variance based on your scores, in the general model.

Usage

generalEM(x, Sig.init = c(10^2, 10^2, 0.1 * 10 * 10), niter = 100, seed = NULL)

Arguments

x

Scores of throws aimed at the center of the dartboard.

Sig.init

The initial guess for the covariance matrix, represented as a vector: x marginal variance, then y marginal variance, then x-y covariance.

niter

The number of iterations.

seed

The seed for the random number generator (the E-step is done by importance sampling).

Value

Sig.final

The final estimate of the covariance matrix.

Sig.init

The initial estimate of the covariance matrix.

Sig

The estimate of the covariance at each iteration.

loglik

The log likelihood at each iteration---currently not implemented (this is just an array of 0s).

niter

The number of iterations.

Examples

# Scores of 100 of my dart 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)

# Get my variance in the general Gaussian model
a = generalEM(x,niter=100,seed=0)

# The EM estimate of my covariance matrix
Sig = a$Sig.final