Hello R-Users,  I have one binary dependent variable and a set of independent variables (glm(formula,â¦,family=âbinomialâ) ) and I am using the function stepAIC (âMASSâ) for choosing an optimal model. However I am not sure if stepAIC considers significance properties like Likelihood ratio test and Wald test (see example below).   > y <- rbinom(30,1,0.4) > x1 <- rnorm(30) > x2 <- rnorm(30) > x3 <- rnorm(30) > xdata <- data.frame(x1,x2,x3) > > fit1 <- glm(y~ . ,family="binomial",data=xdata) > stepAIC(fit1,trace=FALSE)  Call: glm(formula = y ~ x3, family = "binomial", data = xdata)  Coefficients: (Intercept)          x3    -0.3556      0.8404  Degrees of Freedom: 29 Total (i.e. Null); 28 Residual Null Deviance:     40.38 Residual Deviance: 37.86       AIC: 41.86 > > fit <- glm( stepAIC(fit1,trace=FALSE)$formula ,family="binomial") > my.summ <- summary(fit) > # Wald Test > print(my.summ$coeff[,4]) (Intercept)         x3  0.3609638  0.1395215 > > my.anova <- anova(fit,test="Chisq") > #LR Test > print(my.anova$P[2]) [1] 0.1121783 >   Is there an alternative function or a possible way of checking if the added variable and the new model are significant within the regression steps?  Thanks in advance for your help  Regards  Peter-Heinz Fox
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