Output of logistic model in R I'm trying to interpret the following type of logistic model:
mdl <- glm(c(suc,fail) ~ fac1 + fac2, data=df, family=binomial)

Is the output of predict(mdl) the expected odds of success for each data point? Is there a simple way to tabulate the odds for each factor level of the model, rather than all the data points?
 A: The help pages for
predict.glm

state: "Thus for a default binomial model the default predictions are of log-odds         (probabilities on logit scale) and ‘type = "response"’ gives the predicted probabilities". So, predict(mdl) returns the log(odds), and using "type = "response" returns the predicted probabilities. You might find this toy example instructive:
> y <- c(0,0,0,1,1,1,1,1,1,1)
> prop.table(table(y))
y
  0   1 
0.3 0.7 
> glm.y <- glm(y~1, family = "binomial")
> ## predicted log(odds)
> predict(glm.y)
        1         2         3         4         5         6         7         8 
0.8472979 0.8472979 0.8472979 0.8472979 0.8472979 0.8472979 0.8472979 0.8472979 
        9        10 
0.8472979 0.8472979 
> ## predicted probabilities (p = odds/(1+odds))
> exp(predict(glm.y))/(1+exp(predict(glm.y)))
  1   2   3   4   5   6   7   8   9  10 
0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 
> predict(glm.y, type = "response")
  1   2   3   4   5   6   7   8   9  10 
0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 

Regarding your second question, you might want to check out the effects-package http://socserv.socsci.mcmaster.ca/jfox/Misc/effects/index.html by John Fox; see also his JSS article "Effect Displays in R for Generalised Linear Models" (pp. 8-10).
