pblm.prop {pblm} | R Documentation |
Auxiliary for specyfing category-dependent covariates in a pblm
model
Description
This is an auxiliary function which allows to specify partially proportional odds for one (or both) the marginals and with the association parameters which can depend (or not) on the categories of the responses. It simply returns a list with its arguments.
Usage
pblm.prop(prop1=NULL, prop2=NULL, prop12=NULL)
Arguments
prop1 |
a |
prop2 |
a logical vector like |
prop12 |
a logical vector like |
Details
The default specification will result in a model with category-dependent intercepts for both the marginal and the association, while all the covariates are assumed independent of the categories.
Note that, for ordered responses, setting category-independent intercepts for the marginals is not a good idea.
Value
A list with the same arguments of the function, unless unlikely specified by the user.
Author(s)
Marco Enea marco.enea@unipa.it
Examples
# an artificial data frame with two five-category responses and two factors
set.seed(10)
da <- expand.grid("Y1"=1:5,"Y2"=1:5,"fat1"=letters[1:3],"fat2"=letters[1:3])
da$Freq <- sample(1:20,5*5*3*3,replace=TRUE)
#A partial proportional-odds model with uniform association
m2 <- pblm(fo1=cbind(Y1,Y2) ~ fat1 + fat2,
fo2=~fat1,
fo12=~1,
data=da, weights=da$Freq,
proportional=pblm.prop(prop1=c(FALSE,TRUE,TRUE,FALSE,FALSE),
prop2=c(FALSE,TRUE,TRUE),
prop12=c(TRUE)))
summary(m2)