Description Usage Arguments Value Author(s) Examples
EM algorithm to estimate your variance based on your scores, in the general model.
1 2 |
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
1 2 3 4 5 6 7 8 9 10 11 | # 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
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