A function to define logits of recursive (or nested) type.
As many inputs as there are variables in the multi-way table. Each input is a matrix of values -1,0,1 to define recursive logits or 0 for logits of different type
This function is used when logits of type "r" are used for at least one variable. An input argument for each categorical variable is necessary. Inputs are ordered according to the order of the variables.
For a categorical variable with k categories, k-1 recursive logits can be defined using a matrix with k-1 rows and k columns. The rows of this matrix specify the categories whose probabilities constitute numerator and denominator of every recursive logit. Specifically, in every row, a value among -1,0,1 is associated to every category: value 1 (-1) corresponds to the category whose probability is cumulated at the numerator (denominator), 0 if the category is not involved.
A zero-one matrix to be assigned to the
cocacontr input argument in defining a model by ‘hmmm.model’ when logits "r"
are used for at least one variable in the multi-way table.
Cazzaro M, Colombi R (2008) Modelling two way contingency tables with recursive logits and odds ratios. Statistical Methods and Applications, 17, 435-453.
Cazzaro M, Colombi R (2013) Marginal nested interactions for contingency tables. Communications in Statistics - Theory and Methods, to appear.
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/.
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data(kentucky) # 1 = injury 2 = restraint 3 = year y<-getnames(kentucky,st=4) marglist<-marg.list(c("m-m-l","m-l-l","r-l-l"),mflag="m") R1<-matrix(c(1,1,1,-1,-1, 0,0,0,1,-1, 1,1,-1,0,0, 1,-1,0,0,0),4,5,byrow=TRUE) # logits of recursive (or nested) type for variable 1: # log p(injury<=3)/p(injury>3); log p(injury=4)/p(injury=5); # log p(injury<=2)/p(injury=3); log p(injury=1)/p(injury=2); rec<-recursive(R1,0,0) # only variable 1 has recursive logits # additive effect of variables 2,3 on the recursive logits of variable 1 model<-hmmm.model(marg=marglist,lev=c(5,2,5),sel=c(34:49),cocacontr=rec) mod<-hmmm.mlfit(y,model) print(mod,printflag=TRUE)
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