gl<-glm(SF~s,family=binomial(link='logit')) Response variable; for binomial link can be a two-column matrix with success/failure counts Explanatory variable Family = distribution of response link = function of the mean response

pl<-predict(gl,data.frame(s=x),type='response') Result of the glm() “s” is the name that we used in our call to glm() “response” = compute the response variable “link” = compute the link function values “x” is the grid on which the predicted values will be calculated

Deviance residuals show how much each of the observations contributes to the total deviance

ML estimation of the model coefficients; standard error (standard deviation of the estimator); corresponding z-value under H 0 that parameter is 0; and Prob(|z|>|parameter|), where z~N(0,1)

Null deviance: Deviance of the NULL model, which assumes that all parameters (except intercept) equal to 0.

Residual deviance: Deviance + a constant chosen in such a way that the saturated model’s deviance is 0.

Akaike Information Criterion AIC = -2 Log-Likelihood +2 # of parameters