simpleEM: EM Algorithm for the Simple Model

In darts: Statistical Tools to Analyze Your Darts Game

Description

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

Usage

simpleEM(x, s.init = 100, niter = 100)

Arguments

x	Scores of throws aimed at the center of the dartboard.
s.init	The initial guess for the marginal variance.
niter	The number of iterations.

Value

s.final	The final estimate of the variance.
s.init	The initial estimate of the variance.
s	The estimate of the variance at each iteration of the EM algorithm.
loglik	The (observed) log likelihood at each iteration of the EM algorithm.
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 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

Example output

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