Dependent Variable Discrete 2 values – binomial 3 or more discrete values – multinomial Skewed – e.g. Poisson Continuous Non-normal
Link Function Connection between dependent variable and predictor: Logit – ln(p/(1-p)) Probit – inverse normal Other nonlinear connections (exponential, logarithmic, power, etc.)
Function link(y) = a + b1*x1 + b2*x2 + … + bn*xn + e) The link function should connect the (discrete) dependent observation to the linear predictor. y = inverse-link (a + b1*x1 …)
Link Functions DistributionLink Normal, gamma, PoissonLinear, log, power BinomialLogit, probit MultinomialLog(x1/(1 – x2 - … - xn))
Solution Requires numeric solution (rather than algebraic for traditional GLM)
Significance Wald statistic Likelihood Ratio statistic Score statistic
Residuals Pearson residuals – based on observed – predicted values Deviance residuals – contribution to log likelihood statistic Leverage Studentized Cook’s D
Models ANOVA Regression ANCOVA More complex linear models
SAS PROC GENMOD: procedure call CLASS: categorical (ANOVA) variables MODEL: dependent= independent
MODEL Model= dependent Model = events/trials = (ratio of events divided by number of trials for summarized binomial responses)
Model Options CORR, COVB: parameter correlations or covariances DIST= lists the assumed distribution of the dependent variable (see SAS docs) LINK= specifies the link function. SAS will pick a default for a DIST if you don’t Type1 (sequential), Type3 (partial), Wald statistics P (predicted estimates) R (residuals)