Gang Chen <[EMAIL PROTECTED]> wrote in
news:[EMAIL PROTECTED]:
> Suppose I have a two-way table of nominal category (party
> affiliation) X ordinal category (political ideology):
>
> party affiliation X (3 levels) - democratic, independent, and
> republic
> political ideology Y (3 levels) - liberal, moderate, and
> conservative
>
> The dependent variable is the frequency (or count) for all the two-
> way cells sampled from the voters. I want to test whether there is
> any party affiliation effect, and, if there is, the pair-wise
> contrasts. I have never used glm (I assume this the program I should
> use) before, so I am not so sure how I can code the two independent
> variables reflecting the fact that one is nominal while the other
> is ordinal and how to formulate the model.
?factor
Set up affiliation as a factor and ideology as an ordered factor. The
levels argument in factor sets the sort order.
> theo<-c("cons", "mod", "cons", "cons", "lib", "mod")
> table(theo)
theo
cons lib mod
3 1 2
> theo<-ordered(theo, levels=c("lib", "mod", "cons"))
> table(theo)
theo
lib mod cons
1 2 3
The formula in glm would be something similar to counts ~ theo + affil
Much more detail and worked examples regarding modeling count data would
be found in:
Thompson, LA; (2004) R (and S-PLUS)Manual to Accompany Agrestis (2002)
Catagorical Data Analysis;
https://home.comcast.net/~lthompson221/Splusdiscrete2.pdf
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