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))

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

> 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