\begin{Schunk}
\begin{Sinput}
 fmi2 <- fit.mult.impute( survived ~ pclass + sex + age, lrm, rmsImputeTitanic, data= titanic, fit.reps=TRUE)
\end{Sinput}
\begin{Soutput}
Variance Inflation Factors Due to Imputation:

 Intercept pclass=2nd pclass=3rd   sex=male        age 
       2.8        2.1        4.5        1.9        2.5 

Rate of Missing Information:

 Intercept pclass=2nd pclass=3rd   sex=male        age 
      0.65       0.53       0.78       0.48       0.59 

d.f. for t-distribution for Tests of Single Coefficients:

 Intercept pclass=2nd pclass=3rd   sex=male        age 
        22         32         15         39         26 

The following fit components were averaged over the 10 model fits:

  stats linear.predictors 
\end{Soutput}
\begin{Sinput}
 fmi2
\end{Sinput}
\begin{Soutput}
Logistic Regression Model

fitter(formula = formula, data = completed.data)

                      Model Likelihood     Discrimination    Rank Discrim.    
                         Ratio Test            Indexes          Indexes       
Obs          1313    LR chi2     568.84    R2       0.486    C       0.862    
 0            869    d.f.             4    g        2.123    Dxy     0.724    
 1            444    Pr(> chi2) <0.0001    gr       8.441    gamma   0.726    
max |deriv| 9e-07                          gp       0.328    tau-a   0.326    
                                           Brier    0.134                     

           Coef    S.E.   Wald Z Pr(>|Z|)
Intercept   4.2452 0.5458   7.78 <0.0001 
pclass=2nd -1.3704 0.3132  -4.38 <0.0001 
pclass=3rd -2.8590 0.4361  -6.56 <0.0001 
sex=male   -2.3800 0.2273 -10.47 <0.0001 
age        -0.0553 0.0093  -5.93 <0.0001 
\end{Soutput}
\end{Schunk}
