create.XMAT: design matrix for a hmm model
In hmmm: Hierarchical Multinomial Marginal Models

Description Usage Arguments Details Value References See Also Examples

Function to specify the matrix X of the linear predictor Cln(Mm)=Xbeta for a hmm model.

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create.XMAT(model, Formula = NULL, 
strata = 1, fnames = NULL, cocacontr = NULL, 
ncocacontr = NULL, replace = TRUE)

`model`	Object created by ‘hmmm.model’
`Formula`	List of model-formulas; one formula for every marginal interaction
`strata`	Number of categories of the factors that describe the strata
`fnames`	Names of the factors that describe the strata
`cocacontr`	A list of zero-one matrices to build "r" logits created by the function ‘recursive’
`ncocacontr`	Number of contrasts for every factor, if NULL the maximum number is used
`replace`	If TRUE a new model object with design matrix X is produced, if FALSE the list of design matrices associated to each element specified in Formula is returned

When the marginal interactions of a hmm model are defined in terms of a linear predictor of covariates Cln(Mm)=Xbeta, the list of model formulas defines additive effects of covariates on the interactions. In a case with two response variables declared by names<-c("A","B") and two covariates, named C and D by fnames=c("C","D"), the additive effect of the covariates on marginal logits of A and B and log odds ratios (A.B) of the two responses is specified by the following Formula: Formula<-list(A=~A*(C+D), B=~B*(C+D), A.B=~A.B*(C+D)). Use "zero" to constrain to zero all the interactions of a given type.

A list of matrices or a hmm model with X as design matrix according to the input argument replace. The parameters beta in the predictor Cln(Mm)=Xbeta are the effects specified in Formula and correspond to the columns of X.

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

Lang JB (2005) Homogeneous linear predictor models for contingency tables. Journal of the American Statistical Association, 100, 121-134.

hmmm.model, hmmm.mlfit, summary.hmmmfit

data(accident)
y<-getnames(accident,st=9,sep=";")
# responses: 1 = Type, 2 = Time; covariates: 3 = Age, 4 = Hour

marglist<-c("l-m","m-g","l-g")
marginals<-marg.list(marglist,mflag="m")
names<-c("Type","Time")

modelsat<-hmmm.model(marg=marginals,lev=c(3,4),
strata=6, names=names) 

# Create X to account for additive effect of Age and Hour on the logits of Type and Time
# and constant association between Type and Time
al<-list(Type=~Type*(Age+Hour),
Time=~Time*(Age+Hour),Type.Time=~Type.Time)
# list of matrices (replace=FALSE)
listmat<-create.XMAT(modelsat,Formula=al,strata=c(3,2),fnames=c("Age","Hour"),replace=FALSE)

# the model obtained by the modified X (replace=TRUE)
model<-create.XMAT(modelsat,Formula=al,strata=c(3,2),fnames=c("Age","Hour")) 
fitmodel<-hmmm.mlfit(y,model,y.eps=0.00001,maxit=2000)
print(fitmodel)