statistics - Displaying factors and levels with logistic regression in R -

i have dataframe model.vars containing discretized data on perform logistical regression, follows:

for(i in varnames) {   modelformula = paste("dep_var~ ", as.factor(i))   modelfits[[i]] = glm(as.formula(modelformula),                         data=model.vars ,na.action="na.exclude",                         family=binomial(link = "logit")) } 

this gets in turn converted dataframe using

fits = ldply(modelfits, function(x) {   as.data.frame((coef(summary(x))),                  row.names=varnames)}) 

however, resulting output stored in modelfits not contain column specifying relevant level labels each discretized variable. rather, like

     .id              estimate   std. error  zscore     pr(>|z|)     abs.zscore 1 twenty_80.age     -0.6911487  0.2813814   -2.456270   1.403875e-02    2.4562 2 ten_80_10.age     -1.0021909  0.2682952   -3.735403   1.874144e-04    3.735403 3 twenty_80.score   -0.7023356  0.3315694   -2.118216   3.415679e-02    2.118216 

unfortunately, need output dataframe (not list). best way add, say, column giving level labels? example, printing out raw modelfits variable has statements like:

coefficients: (intercept)  twenty_80.score[ 11, 19)  twenty_80.score[ 19, 31)  twenty_80.score[ 31,312] 

i these listed in fits dataframe above well.