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I have a data set looking into whether a farm experienced a livestock disease or not in the year 2011 and 2012 and if several factors could be predictors for the livestock disease.

The independent variables were also collected for both years though some variables did not change e.g Thistles remained the same for both years.

I am looking for an appropriate method that will allow statistical comparison between the two years rather than treating analysis as two separate sets of analyses (i.e not to treating 2011 and 2012 as two separate data set)

Whilst trying to do the analysis I have created dependent variable as farm having the disease or not between year 2011 and 2012(Orf.Yes.No2011.2012)against the dependent variables using logistic regression:

I'm just wondering whether I doing the right thing or what could be the best statistical approach which will allow for statistical comparison between the two years? Any help will be very much appreciated

Here is the R output and sample of dataset:





 > mod=glm(Orf.Yes.No2011.2012~F2011+ F2012+as.factor(Breed)+ 
                                  D2011+D2012,family=binomial, data=orf)
      summary(mod)

    Call:
    glm(formula = Orf.Yes.No2011.2012 ~ F2011 + F2012 + as.factor(Breed) + 
        D2011 + D2012, family = binomial, data = orf)

    Deviance Residuals: 
       Min      1Q  Median      3Q     Max  
    -1.862  -1.293   1.023   1.065   1.318  

    Coefficients:
                        Estimate Std. Error z value Pr(>|z|)  
    (Intercept)        0.3917290  0.1626769   2.408    0.016 *
    F2011              0.0003269  0.0002782   1.175    0.240  
    F2012             -0.0003596  0.0002786  -1.291    0.197  
    as.factor(Breed)2  0.0558285  0.1489246   0.375    0.708  
    D2011             -0.0311978  0.0272068  -1.147    0.252  
    D2012              0.0226963  0.0274981   0.825    0.409  
    Small sample data set:

    F2011   F2012   Breed   Orf.Yes.No2011  Orf.Yes.No2012  Orf.Yes.No2011.2012
    155     150     1       0               0               0
    740     760     2       0               1               1
    1000    850     1       0               0               0
    1630    1520    1       1               1               1
    0       460     1       0               0               0
    1300    1335    1       0               1               1
    450     450     1       0               0               0
    390     730     1       1               0               1
    390     380     2       0               0               0
    600     600     2       0               0               0
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