Description Usage Arguments Value Author(s) Examples
EM algorithm to estimate your variance based on your scores, in the simple model.
1 | simpleEM(x, s.init = 100, niter = 100)
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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. |
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. |
Ryan Tibshirani
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | # 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
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