Download presentation
Presentation is loading. Please wait.
1
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
2
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
3
Deviance residuals show how much each of the observations contributes to the total deviance
4
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)
5
Null deviance: Deviance of the NULL model, which assumes that all parameters (except intercept) equal to 0.
6
Residual deviance: Deviance + a constant chosen in such a way that the saturated model’s deviance is 0.
7
Akaike Information Criterion AIC = -2 Log-Likelihood +2 # of parameters
Similar presentations
© 2025 SlidePlayer.com. Inc.
All rights reserved.