I'm making a regression model using the glm funtion but I noticed that one level of my factors (Agriculture in Landuse) is always missing in the output of the model.

I attached the data I used and below is the code I use.

So my goal is to explain how well a combination of the Dry and Landuse (which consists of three levels; Agriculture, Pastoral and Protected) explains y (which is the distribution of a certain animal).

I followed a similar example in the Crawley R book so I know that the model should give me a result for the different types of Landuse and the combination of Landuse and Wet but as you can see, both Agriculture and Dry: LanduseAgriculture are missing!

I read somewhere that Agriculture might be absorbed in the intercept parameter but this doesn't solve my problem because now I don't have a p-value for two parameters.

Does anywone know why I don't get a result for Agriculture and Agriculturery and what I can do about it?

cheers,

Brenda

> y<-cbind(WD_Y, WD_N)

> pWD<-split(WD,Landuse)

> pDry<-split(Dry,Landuse)

> model<-glm(y~Dry*Landuse, binomial)

> summary(model)

Call:

glm(formula = y ~ Dry * Landuse, family = binomial)

Deviance Residuals:

Min 1Q Median 3Q Max

-3.5847 -1.2198 -0.9366 0.3011 5.6034

Coefficients:

Estimate Std. Error z value Pr(>|z|)

(Intercept) -21.57864 1964.72039 -0.011 0.991

Dry 0.07732 9686.42731 7.98e-06 1.000

LandusePastoral 17.29671 1964.72039 0.009 0.993

LanduseProtected 20.44246 1964.72039 0.010 0.992

Dry:LandusePastoral 0.78610 9686.42732 8.12e-05 1.000

Dry:LanduseProtected -4.46714 9686.42733 -4.61e-04 1.000

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 1001.17 on 244 degrees of freedom

Residual deviance: 566.87 on 239 degrees of freedom

AIC: 833.96