mixture: Estimate mixture latent variable model.
In kkholst/lava.mixture: Mixtures of Linear Latent Variable Models

Description Usage Arguments Details Author(s) See Also Examples

Estimate mixture latent variable model

1 2	mixture(x, data, k = length(x), control = list(), type = c("standard", "CEM", "SEM"), vcov = "observed", ...)

`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)
`...`	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:

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).

Klaus K. Holst

mvnmix

set.seed(1)
m0 <- lvm(list(y~x+z,x~z))
distribution(m0,~z) <- binomial.lvm()
d <- sim(m0,500,p=c("y<-z"=2,"y<-x"=1))

## unmeasured confounder example
m <- baptize(lvm(y~x));
covariance(m,~x) <- "v"
intercept(m,~x+y) <- NA

M <- mixture(m,k=2,data=d,control=list(trace=1,tol=1e-6))
summary(M)
lm(y~x,d)
## True slope := 1