hmmm.mlfit: fit a hmm model
In hmmm: Hierarchical Multinomial Marginal Models

Description Usage Arguments Details Value References See Also Examples

Function to estimate a hierarchical multinomial marginal model.

hmmm.mlfit(y, model, noineq = TRUE, maxit = 1000, 
norm.diff.conv = 1e-05, norm.score.conv = 1e-05,
y.eps = 0, chscore.criterion = 2,
m.initial = y, mup = 1, step = 1)

`y`	A vector of frequencies of the contingency table
`model`	An object created by ‘hmmm.model’
`noineq`	If TRUE inequality constraints specified in the model are ignored
`maxit`	Maximum number of iterations
`norm.diff.conv`	Convergence criterium value on the parameters
`norm.score.conv`	Convergence criterium value on the constraints
`y.eps`	Non-negative constant to be added to the original counts in y
`chscore.criterion`	If equal to zero, convergence information are printed at every iteration
`m.initial`	Initial estimate of m (expected frequencies)
`mup`	Weight for the constraints penalty part of the merit function
`step`	Interval length for the line search

A sequential quadratic procedure is used to maximize the log-likelihood function under inequality and equality constraints. This function calls the procedure ‘mphineq.fit’ which is a generalization of the procedure ‘mph.fit’ by Lang (2004).

An object of the class hmmmfit; an estimate of a marginal model defined by ‘hmmm.model’. The output can be displayed using ‘summary’ or ‘print’.

Bartolucci F, Colombi R, Forcina A (2007) An extended class of marginal link functions for modelling contingency tables by equality and inequality constraints. Statistica Sinica, 17, 691-711.

Bergsma WP, Rudas T (2002) Marginal models for categorical data. The Annals of Statistics, 30, 140-159.

Colombi R, Giordano S, Cazzaro M (2014) hmmm: An R Package for hierarchical multinomial marginal models. Journal of Statistical Software, 59(11), 1-25, URL http://www.jstatsoft.org/v59/i11/.

Lang JB (2004) Multinomial Poisson homogeneous models for contingency tables. The Annals of Statistics, 32, 340-383.

hmmm.model, hmmm.model.X, summary.hmmmfit, print.hmmmfit

data(relpol)
y<-getnames(relpol,st=12)
# 1 = Religion, 2 = Politics
names<-c("Rel","Pol")
marglist<-c("l-m","m-g","l-g")
marginals<-marg.list(marglist,mflag="m")

# Hypothesis of stochastic independence: all log odds ratios are null 
model<-hmmm.model(marg=marginals,lev=c(3,7),sel=c(9:20),names=names)
fitmodel<-hmmm.mlfit(y,model)
print(fitmodel, aname="Independence model",printflag=TRUE)
summary(fitmodel)