R help - Sep 2015 - Multinomial data

?? Hello everyone,
?? I have some data I would like to analyse where my response variables are
categorical data: several participants were asked to give answers in the form of
1 to 5 (for the degree of importance given to certain items). So, what I have
now is response variables that can just take 5 possible values: 1, 2, 3, 4 or 5.
?? After a bit of research, I have learned this might be a case of multinomial
categorical data, although I am not sure whether ordered multinomial or nominal
multinomial. I would like to apply generalized linear mixed models to my
response variables, but I am a bit lost at the moment. Should I just use lmer
and change the link function? Is it better to try MCMCglmm package and start
from there?

?? Any slight guidance or light shed on this would be greatly appreciated. 

?? Thanks a lot!
?__________________________________________________________________

?? Dr. Iker Vaquero-Alba
?? Visiting Postdoctoral Research Associate
?? Laboratory of Evolutionary Ecology of Adaptations 
?? School of Life Sciences
?? Joseph Banks Laboratories
?? University of Lincoln?? Brayford Campus, Lincoln
?? LN6 7DL
?? United Kingdom

?? https://eric.exeter.ac.uk/repository/handle/10036/3381


	[[alternative HTML version deleted]]

On Mon, 14 Sep 2015, Iker Vaquero Alba wrote:
> ?? I have some data I would like to analyse where my response variables
> are categorical data: several participants were asked to give answers in
> the form of 1 to 5 (for the degree of importance given to certain items).
> So, what I have now is response variables that can just take 5 possible
> values: 1, 2, 3, 4 or 5.
Iker,

   This situation calls for the application of Thomas Saaty's Analytical
Hierarchy Process (AHP). A good summary is on wikipedia:
<https://en.wikipedia.org/wiki/Analytic_hierarchy_process>, and web
searches
will augment that information.

   It's easy to implement with octave as well as with python code. Perhaps
there is a package within R but I've not found one.

Rich