generalEM {darts} | R Documentation |
EM algorithm to estimate your variance based on your scores, in the general model.
generalEM(x, Sig.init = c(10^2, 10^2, 0.1 * 10 * 10), niter = 100, seed = NULL)
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). |
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. |
Ryan Tibshirani
# 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