set.seed(4444)
   x = sample(7, 1e2, replace=T)
   cf = c(0,2,-2,3,1,-1,1.5)
   yrate= exp(2+cf[x])
   y = rpois(1e2, yrate)
> aggregate(y, list(x), mean)
  Group.1          x
1       1   8.272727
2       2  53.666667
3       3   1.363636
4       4 152.750000
5       5  18.875000
6       6   2.666667
7       7  35.400000

> summary(y)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    0.0     3.0    22.5    33.7    46.5   186.0 

> fram = cbind.data.frame(x=factor(x),y=y)
  round(rbind(cf, glm(y~x, data=fram, family=poisson)$coef),3)
   (Intercept)   x2     x3    x4    x5     x6    x7
cf       0.000 2.00 -2.000 3.000 1.000 -1.000 1.500
         2.113 1.87 -1.803 2.916 0.825 -1.132 1.454

> x2 = factor(x); contrasts(x2) = contr.sum(x2, contrasts=F)
  fram2 = cbind.data.frame(x2,y=y)
  tmp = glm(y~x, data=fram2, family=poisson)

  round(rbind(cf, glm(y~x, data=fram2, family=poisson)$coef),3)



> contrasts(factor(1:7))
  2 3 4 5 6 7
1 0 0 0 0 0 0
2 1 0 0 0 0 0
3 0 1 0 0 0 0
4 0 0 1 0 0 0
5 0 0 0 1 0 0
6 0 0 0 0 1 0
7 0 0 0 0 0 1

> contr.sum(7)
  [,1] [,2] [,3] [,4] [,5] [,6]
1    1    0    0    0    0    0
2    0    1    0    0    0    0
3    0    0    1    0    0    0
4    0    0    0    1    0    0
5    0    0    0    0    1    0
6    0    0    0    0    0    1
7   -1   -1   -1   -1   -1   -1

> contr.sum(7)[c(7,1:6),]
  [,1] [,2] [,3] [,4] [,5] [,6]
7   -1   -1   -1   -1   -1   -1
1    1    0    0    0    0    0
2    0    1    0    0    0    0
3    0    0    1    0    0    0
4    0    0    0    1    0    0
5    0    0    0    0    1    0
6    0    0    0    0    0    1