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. Any help is highly appreciated, Gang
Gang Chen <gangchen at mail.nih.gov> wrote in news:4B1D9D53-7F9A-4337-912A-B7D29601DC25 at mail.nih.gov:> 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 Agresti?s (2002) Catagorical Data Analysis; https://home.comcast.net/~lthompson221/Splusdiscrete2.pdf
Thanks a lot! This is exactly what I wanted. Gang On Jan 5, 2008, at 2:20 PM, David Winsemius wrote:> Gang Chen <gangchen at mail.nih.gov> wrote in > news:4B1D9D53-7F9A-4337-912A-B7D29601DC25 at mail.nih.gov: > >> 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 Agresti?s > (2002) > Catagorical Data Analysis; > https://home.comcast.net/~lthompson221/Splusdiscrete2.pdf > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting- > guide.html > and provide commented, minimal, self-contained, reproducible code.