# Why does a binomial glm give negative predictions?

I'm using count data in quite a simple way, but I cannot understand how a binomial glm can return negative predictions

example code, where count of successes increases with responce variable:

    suc=c(1:10)
fail=c(10:1)
predict(glm(cbind(suc,fail)~c(1:10),family=binomial))


which results in:

    -1.9974174 -1.5535469 -1.1096763 -0.6658058 -0.2219353  0.2219353  0.6658058 1.1096763  1.5535469  1.9974174


I don't understand this: how can a binomial model give these predictions? It should be integer positive predictions, no?

Assuming that you are using the predict.glm() from the stats package.
predict(glm(cbind(suc,fail)~c(1:10),family=binomial), type="response")