mixalg.VEM: VEM algorithm In CAMAN: Finite Mixture Models and Meta-Analysis Tools - Based on C.A.MAN

Description

When fitting finite mixture models two cases must be distinguished. The flexible support size case, where no assumption about the number of components k is made in advance and the fixed support size case. For the flexible support size case the VEM-algorithm can be used.

The algorithm proceeds as follows:
Step 1: Define an approximating grid lambda, ..., lambda[L]
Step 2: Use the VEM algorithm to maximize L(P) in the simplex Ω and identify grid points with positive support.

Usage

 1 2 3 mixalg.VEM(mix = NULL, obs=NULL, weights=NULL, data=NULL, pop.at.risk=NULL, var.lnOR=NULL, family="gaussian", limit=0.01, acc=10^(-7), numiter=5000, startk=50)

Arguments

 mix A CAMAN-object which quantifies a finite mixture model and the input data. obs observed / dependent variable. Vector or colname of data. Must be specified! weights weights of the data. Vector or colname of data. Default is NULL. family the underlying type density function as a character ("gaussian", "poisson" or "binomial")! data an optional data frame. obs, weights, pop.at.risk and var.lnOR can be specified as column name of the data frame. pop.at.risk population at risk: These data could be used to determine a mixture model for Poisson data. Vector or colname of data. Default isNULL. var.lnOR variances of the data: These variances might be given when working with meta analyses! Vector or colname of data. Default is NULL. limit parameter to control the limit of union several components. Default is 0.01. acc convergence criterion. VEM and EM loops stop when deltaLL

Value

The function returns a CAMAN.VEM.object object.

Author(s)

Peter Schlattmann and Johannes Hoehne

Examples

 1 2 3 4 data(vitA) m0<-mixalg.VEM(obs="logrr",var.lnOR="var",family="gaussian", data=vitA,startk=20) plot(m0@totalgrid[,2],m0@totalgrid[,3],type="l",xlab="parameter",ylab="gradient") m1<-mixalg.EM(obs="logrr",var.lnOR="var" ,family="gaussian",p=c(1),t=c(0),data=vitA)

Example output 