mixture function

Estimate mixture latent variable model.

Estimate mixture latent variable model

mixture(
  x,
  data,
  k = length(x),
  control = list(),
  vcov = "observed",
  names = FALSE,
  ...
)

Arguments

• x: List of lvm objects. If only a single lvm object is given, then a k-mixture of this model is fitted (free parameters varying between mixture components).
• data: data.frame
• 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

Details

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:

• start: Optional starting values
• nstart: Evaluate nstart different starting values and run the EM-algorithm on the parameters with largest likelihood
• tol: 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
• iter.max: Maximum number of iterations of the EM-algorithm
• gamma: Scale-down (i.e. number between 0 and 1) of the step-size of the Newton-Raphson algorithm in the M-step
• trace: 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).

Examples

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
}

See Also

mvnmix

Author(s)

Klaus K. Holst