Estimate mixture latent variable model.
Estimate mixture latent variable model
mixture( x, data, k = length(x), control = list(), vcov = "observed", names = FALSE, ... )
x |
List of |
data |
|
k |
Number of mixture components |
control |
Optimization parameters (see details) #type Type of EM algorithm (standard, classification, stochastic) |
vcov |
of asymptotic covariance matrix (NULL to omit) |
names |
If TRUE returns the names of the parameters (for defining starting values) |
... |
Additional arguments parsed to lower-level functions |
Estimate parameters in a mixture of latent variable models via the EM algorithm.
The performance of the EM algorithm can be tuned via the control
argument, a list where a subset of the following members can be altered:
Optional starting values
Evaluate
nstart different starting values and run the EM-algorithm on the
parameters with largest likelihood
Convergence tolerance of the
EM-algorithm. The algorithm is stopped when the absolute change in
likelihood and parameter (2-norm) between successive iterations is less than
tol
Maximum number of iterations of the EM-algorithm
Scale-down (i.e. number between 0 and 1) of the step-size of the Newton-Raphson algorithm in the M-step
Trace
information on the EM-algorithm is printed on every traceth
iteration
Note that the algorithm can be aborted any time (C-c) and still be saved (via on.exit call).
Klaus K. Holst
mvnmix
m0 <- lvm(list(y~x+z,x~z))
distribution(m0,~z) <- binomial.lvm()
d <- sim(m0,2000,p=c("y~z"=2,"y~x"=1),seed=1)
## unmeasured confounder example
m <- baptize(lvm(y~x, x~1));
intercept(m,~x+y) <- NA
if (requireNamespace('mets', quietly=TRUE)) {
set.seed(42)
M <- mixture(m,k=2,data=d,control=list(trace=1,tol=1e-6))
summary(M)
lm(y~x,d)
estimate(M,"y~x")
## True slope := 1
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