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Stepwise Logistic Regression with R

Stepwise Logistic Regression with RAkaike information criterion: AIC = 2k - 2 log L = 2k + Deviance, where k = number of parametersSmall numbers are betterPenalizes models with lots of parametersPenalizes models with poor fit> fullmod = glm(low ~ age+lwt+racefac+smoke+ptl+ht+ui+ftv,fami ly=binomial)> summary(fullmod)Call:glm(formula = low ~ age + lwt + racefac + smoke + ptl + ht + ui + ftv, family = binomial)Deviance Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) age lwt * racefacBlack * racefacOther * smoke * ptl ht **ui.

Null deviance: 234.67 on 188 degrees of freedom Residual deviance: 234.67 on 188 degrees of freedom AIC: 236.67 Number of Fisher Scoring iterations: 4

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