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gl<-glm(SF~s,family=binomial(link='logit'))

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

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gl<-glm(SF~s,family=binomial(link='logit'))

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

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

  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 H0 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

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