mixalg {CAMAN} R Documentation

## Fitting Finite Mixture Models

### Description

This hybrid mixture algorithm combines the VEM algorithm for flexible support size and the EM algorithm for a fixed number of components. The solution of the VEM algorithm provides starting values for the EM algorithm. By the NPMLE theorem the EM algorithm thus starts very close to the global maximum and proper convergence of the EM algorithm to a global maximum is ensured.

The algorithm proceeds as follows

Step 1: Define an approximating grid lambda[1], ..., lambda[L]

Step 2: Use the VEM algorithm to maximize L(P) in the simplex \Omega and identify grid points with positive support. Here positive support is defined as p[j] >= epsilon (often epsilon = 10^-2).
This gives an initial estimate of k.

Step 3: Use these k points and corresponding mixing weights p[j] as starting values for the EM algorithm

Step 4: Collapse identical components if | lambda[j]- lambda[i] | < delta  (often delta=0.05) for i != j

Step 5: Obtain the final number of components k

This sequential algorithm leads to an initial estimate of the NPMLE and a proper solution for the subsequent EM algorithm. Crucial points are the definitions of \delta and \epsilon. Depending on these settings different solutions could result from this algorithm.

### Usage

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


### Arguments

 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

### Details

The documentation of leukDat contains a disease mapping example using mixalg and the documentation of golubMerge contains a microarray analysis example.

### Value

The function returns a CAMAN.object, describing a finite mixture model. The main information about the mixture model is printed by just typing the <object>. Additional information is given in summary(object) (summary.CAMAN.object). Single attributes can be accessed using the @, e.g. mix@LL.

 dat (input) data

 family underlying type density function LL Likelihood of the final (best) iteration BIC Likelihood of the final (best) iteration num.k number of components obtained p probability of each component t parameter of distribution (normal distr. -> mean, poisson distr. -> lambda, binomial distr. -> prob) component.var variance of each component (ONLY if family == "gaussian") prob probabilies, belonging to each component classification classification labels for each observation (which.max of @prob). steps number of steps performed (EM, VEM). VEM_result result of VEM algorithm. cl the matched call. is_metaAnalysis parameter specifying, whether a meta analysis was performed. VEM_result Outcome of the VEM-algorithm, which was run before the EM. finalacc deltaLL of the final iteration (for VEM and EM)

### Author(s)

Peter Schlattmann and Johannes Hoehne

### References

D. B\"ohning, P. Schlattmann, B.G. Lindsay: C.A.MAN - Computer Assisted Analysis of Mixtures: Statistical Algorithms.Biometrics, 1992, 48, 283-303

P. Schlattmann: On bootstrapping the unknown number of components in finite mixtures of Poisson distributions. Statistics and Computing, 2005, 15, 179-188

Schlattmann, P. (2009). Medical Applications of Finite Mixture Models. Berlin: Springer.

mixalg.EM, mixalg.VEM, anova.CAMAN.object, mixcov, mixalg.boot

### Examples

### POISSON data with weights: thai_cohort
data(thai_cohort)
mix <- mixalg(obs="counts", weights="frequency", family="poisson",
data=thai_cohort, numiter=18000, acc=0.00001, startk=25)

# meta analysis
data(aspirin)
mix <- mixalg(obs="logrr", var.lnOR="var", data=aspirin)

## See the documentation of golub.Merge for a
## microarray analysis example using mixalg

## See the documentation of leukDat for a disease
## mapping example using mixalg



[Package CAMAN version 0.78 Index]