402 > wilner _ fac.design(c(2,2,2,2),c("Sent","Cont","Norm","Prox"))
402 > wilner$Count _ scan("t139.dat")
402 > wil.glm1 _ glm(Count ~ Sent*Cont*Norm*Prox,data=wilner,family=poisson)
402 > qqnorm.aov(wil.glm1)
402 > qqnorm.aov(wil.glm1,label=9)
402 > wil.glm2 _ glm(Count ~ (Sent+Cont+Norm+Prox)^2,data=wilner,family=poisson)
402 > anova(wil.glm2)
Analysis of Deviance Table

Poisson model

Response: Count

Terms added sequentially (first to last)
          Df Deviance Resid. Df Resid. Dev 
     NULL                    15   209.4294
     Sent  1  2.90363        14   206.5257
     Cont  1  0.32240        13   206.2034
     Norm  1  8.54636        12   197.6570
     Prox  1 19.28639        11   178.3706
Sent:Cont  1 45.61234        10   132.7583
Sent:Norm  1 35.52986         9    97.2284
Sent:Prox  1 10.53795         8    86.6905
Cont:Norm  1 19.06215         7    67.6283
Cont:Prox  1 52.65934         6    14.9690
Norm:Prox  1 12.72094         5     2.2480