generalEM: EM Algorithm for the General Model

In darts: Statistical Tools to Analyze Your Darts Game

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.

Author(s)

Ryan Tibshirani

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

Example output

darts documentation built on May 2, 2019, 8:22 a.m.