Can MICE pool complete GLM output binary logistic

问题:

I am running a logistic regression with a binary outcome variable on data that has been multiply imputed using MICE. It seems straightforward to pool the coefficients of the glm model: imp=mice(nhanes2, print=F)

 imp$meth

 fit0=with(data=imp, glm(hyp~age, family = binomial))

 fit1=with(data=imp, glm(hyp~age+chl, family = binomial))

 summary(pool(fit1))

However, I can't figure out a way to pool other output generated by the glm. For instance, the glm function produces AIC, Null deviance and Residual deviance that can be used for model testing. pool(summary(fit1)) ## summary of imputation 1 :

     Call:
     glm(formula = hyp ~ age + chl, family = binomial)

    Deviance Residuals: 
     Min       1Q   Median       3Q      Max  
      -1.0117  -0.7095  -0.4862  -0.2169   2.2267  

    Coefficients:
   Estimate Std. Error z value Pr(>|z|)
    (Intercept) -5.69937    3.78119  -1.507    0.132
    age2         1.34014    1.35545   0.989    0.323
    age3         1.55824    1.39266   1.119    0.263
    chl          0.01662    0.01749   0.950    0.342

  (Dispersion parameter for binomial family taken to be 1)

  **Null deviance: 25.020  on 24  degrees of freedom
  Residual deviance: 21.898  on 21  degrees of freedom
  AIC: 29.898**

   Number of Fisher Scoring iterations: 5

I have attempted the pool.compare function but was also unable to do it with the binary outcome variable

pool.compare(fit1, fit0, data=imp, method="likelihood")

 Error in Summary.factor(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,  : 
  ‘min’ not meaningful for factors

Is there a way to accomplish these things (or get a log likelihood test output) with multiply imputed data using MICE or is there a way to use another package like rms to do this with the MI data generated by MICE?