getSummary.glm <- function(obj,alpha=.05,...){

    smry <- summary(obj)
    N <- if(length(weights(obj)))
             sum(weights(obj),na.rm=TRUE)
         else sum(smry$df[1:2])

    coef <- smry$coef

    lower <- qnorm(p=alpha/2,mean=coef[,1],sd=coef[,2])
    upper <- qnorm(p=1-alpha/2,mean=coef[,1],sd=coef[,2])

    coef <- cbind(coef,lower,upper)

    dn <- list(
        rownames(coef),
        c("est","se","stat","p","lwr","upr"),
        names(obj$model)[1]
    )
    dim(coef) <- c(dim(coef)[1],dim(coef)[2],1)
    dimnames(coef) <- dn

    phi <- smry$dispersion
    LR <- smry$null.deviance - smry$deviance
    df <- smry$df.null - smry$df.residual

    ll <- logLik(obj)
    deviance <- deviance(obj)

    if(df > 0){
        p <- pchisq(LR,df,lower.tail=FALSE)
        L0.pwr <- exp(-smry$null.deviance/N)
        #LM.pwr <- exp(-smry$deviance/N)
        Aldrich.Nelson <- LR/(LR+N)
        McFadden <- 1- smry$deviance/smry$null.deviance
        Cox.Snell <- 1 - exp(-LR/N)
        Nagelkerke <- Cox.Snell/(1-L0.pwr)
    }
    else {
        LR <- NA
        df <- NA
        p <- NA
        Aldrich.Nelson <- NA
        McFadden <- NA
        Cox.Snell <- NA
        Nagelkerke <- NA
    }

    AIC <- AIC(obj)
    BIC <- AIC(obj,k=log(N))
    sumstat <- c(
        phi            = phi,
        LR             = LR,
        df             = df,
        p              = p,
        logLik         = ll,
        deviance       = deviance,
        Aldrich.Nelson = Aldrich.Nelson,
        McFadden       = McFadden,
        Cox.Snell      = Cox.Snell,
        Nagelkerke     = Nagelkerke,
        AIC            = AIC,
        BIC            = BIC,
        N              = N
    )

    list(coef=coef,
         sumstat=sumstat,
         contrasts=obj$contrasts,
         xlevels=obj$xlevels,
         call=obj$call)
}