EM | R Documentation |
The incidence matrix, A
is the m by n matrix that represents the
data. There are m probabilities that must be estimated. The EM, or
expectation maximization, method is applied to these data.
EM(A, pvec, maxiter=500, tol=1e-12)
A |
The incidence matrix. |
pvec |
The probability vector. |
maxiter |
The maximum number of iterations. |
tol |
The tolerance used to judge convergence. |
Lots.
An object of class icsurv
containing the following
components:
pf |
The NPMLE of the probability vector. |
numiter |
The number of iterations used. |
converge |
A boolean indicating whether the algorithm converged. |
intmap |
If present indicates the real representation of the
support for the values in |
Alain Vandal and Robert Gentleman.
The EM algorithm applied to the maximal cliques of the intersection graph of the censored data. The empirical distribution function with arbitrarily grouped, censored and truncated data, B. W. Turnbull, 1976, JRSS;B.
VEM
, ISDM
, EMICM
,
PGM
data(cosmesis)
csub1 <- subset(cosmesis, subset= Trt==0, select=c(L,R))
EM(csub1)
data(pruitt)
EM(pruitt)
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