anova.CAMAN.object {CAMAN}  R Documentation 
ANOVA for finite mixture models
Description
A common problem in the estimation of mixture models is to determine the
number of components. This may be done using a parametric bootstrap.
This function simulates from a mixture model under the null hypothesis
with k0
components. A mixture model with k0 and usually k0 +1 components
is fit to the data and then the likelihood ratio statistic (LRS) is computed.
Based on the bootstrap the distribution of the LRS is obtained which allows to obtain
an approximation to the achieved level of significance corresponding to
the value of 2 \log \xi
obtained from the original sample.
Usage
## S3 method for class 'CAMAN.object'
anova(object, object1, nboot=2500, limit=0.01, acc=10^(7),
numiter=5000, giveBootstrapData=FALSE, giveLikelihood=FALSE, ...)
Arguments
object 
A CAMANobject which quantifies a finite mixture model under null hypothesis. 
object1 
A CAMANobject which quantifies another finite mixture model under the alterative hypothesis. 
nboot 
Number of bootstrap samples. 
limit 
parameter to control the limit of union several components. Default is 0.01. 
acc 
convergence criterion. VEM and EM loops stop when deltaLL<acc. Default is 10^(7). 
numiter 
parameter to control the maximal number of iterations in the VEM and EM loops. Default is 5000. 
giveBootstrapData 
A Boolean that indicates whether the bootstrapped data should be returned or not 
giveLikelihood 
Return the likelihoodvalues of both models for each generated dataset. 
... 
Arguments to be passed on to other methods; currently none. 
Details
The parameters limit, acc
and numiter
are used for the VEM algorithm in each bootstrap sample.
Value
The function returns a list with components

overview
: comparison of the models, including BIC, LL and LLratio

`LL ratios in bootstrapdata`
: 90, 95, 97.5 and 99 percentiles of LLratios

`simulated pvalue`
: pvalue, quantifying the null model
Author(s)
Peter Schlattmann and Johannes Hoehne
References
McLachlan, G. and Peel, D. (2000). Finite Mixture Models, Chichester: Wiley.
Schlattmann, P. (2009). Medical Applications of Finite Mixture Models. Berlin: Springer.
Examples
data(thai_cohort)
mix0 < mixalg(obs="counts", weights="frequency", family="poisson", data=thai_cohort,
numiter=18000, acc=0.00001,startk=25)
em0<mixalg.EM(mix0,p=c(1),t=c(1))
em1<mixalg.EM(mix0,p=c(0.7,0.3),t=c(2,9))
## Not run: ll<anova(em0,em1,nboot=250) #might take some minutes