\begin{Schunk}
\begin{Sinput}
 M2 <- glm.mi( survived ~ pclass + sex + age, miImpTitanic, family = binomial(link = "logit"))  
 display(M2)
\end{Sinput}
\begin{Soutput}
=======================================
Separate Estimates for each Imputation
=======================================

** Chain 1 **
glm(formula = formula, family = family, data = mi.data[[i]])
            coef.est coef.se
(Intercept)  3.70     0.32  
pclass2nd   -0.64     0.21  
pclass3rd   -4.12     0.24  
sexmale     -1.61     0.17  
age         -0.04     0.01  
---
  n = 1313, k = 5
  residual deviance = 1053.3, null deviance = 1740.9 (difference = 687.6)

** Chain 2 **
glm(formula = formula, family = family, data = mi.data[[i]])
            coef.est coef.se
(Intercept)  4.73     0.37  
pclass2nd   -1.34     0.23  
pclass3rd   -4.03     0.27  
sexmale     -2.93     0.20  
age         -0.07     0.01  
---
  n = 1313, k = 5
  residual deviance = 929.3, null deviance = 1559.7 (difference = 630.4)

** Chain 3 **
glm(formula = formula, family = family, data = mi.data[[i]])
            coef.est coef.se
(Intercept)  4.85     0.35  
pclass2nd   -1.33     0.22  
pclass3rd   -2.86     0.23  
sexmale     -2.62     0.17  
age         -0.09     0.01  
---
  n = 1313, k = 5
  residual deviance = 1067.9, null deviance = 1628.9 (difference = 561.0)

** Chain 4 **
glm(formula = formula, family = family, data = mi.data[[i]])
            coef.est coef.se
(Intercept)  5.30     0.38  
pclass2nd   -1.67     0.24  
pclass3rd   -3.39     0.26  
sexmale     -3.56     0.20  
age         -0.06     0.01  
---
  n = 1313, k = 5
  residual deviance = 967.6, null deviance = 1694.5 (difference = 726.9)

** Chain 5 **
glm(formula = formula, family = family, data = mi.data[[i]])
            coef.est coef.se
(Intercept)  5.52     0.39  
pclass2nd   -2.12     0.23  
pclass3rd   -4.84     0.28  
sexmale     -1.73     0.17  
age         -0.08     0.01  
---
  n = 1313, k = 5
  residual deviance = 1027.7, null deviance = 1698.3 (difference = 670.6)

** Chain 6 **
glm(formula = formula, family = family, data = mi.data[[i]])
            coef.est coef.se
(Intercept)  5.43     0.39  
pclass2nd   -1.40     0.24  
pclass3rd   -3.46     0.26  
sexmale     -3.74     0.21  
age         -0.07     0.01  
---
  n = 1313, k = 5
  residual deviance = 937.2, null deviance = 1699.5 (difference = 762.3)

** Chain 7 **
glm(formula = formula, family = family, data = mi.data[[i]])
            coef.est coef.se
(Intercept)  4.55     0.34  
pclass2nd   -1.58     0.21  
pclass3rd   -4.32     0.26  
sexmale     -1.74     0.16  
age         -0.06     0.01  
---
  n = 1313, k = 5
  residual deviance = 1080.9, null deviance = 1678.8 (difference = 597.9)

** Chain 8 **
glm(formula = formula, family = family, data = mi.data[[i]])
            coef.est coef.se
(Intercept)  4.30     0.32  
pclass2nd   -0.88     0.21  
pclass3rd   -3.51     0.22  
sexmale     -2.30     0.17  
age         -0.04     0.01  
---
  n = 1313, k = 5
  residual deviance = 1185.1, null deviance = 1796.8 (difference = 611.8)

** Chain 9 **
glm(formula = formula, family = family, data = mi.data[[i]])
            coef.est coef.se
(Intercept)  5.00     0.38  
pclass2nd   -1.54     0.25  
pclass3rd   -3.26     0.26  
sexmale     -3.90     0.21  
age         -0.05     0.01  
---
  n = 1313, k = 5
  residual deviance = 918.0, null deviance = 1691.9 (difference = 773.9)

** Chain 10 **
glm(formula = formula, family = family, data = mi.data[[i]])
            coef.est coef.se
(Intercept)  4.74     0.35  
pclass2nd   -1.48     0.22  
pclass3rd   -4.09     0.25  
sexmale     -1.48     0.16  
age         -0.08     0.01  
---
  n = 1313, k = 5
  residual deviance = 1083.3, null deviance = 1654.8 (difference = 571.5)

=======================================
Pooled Estimates
=======================================
glm.mi(formula = survived ~ pclass + sex + age, mi.object = miImpTitanic, 
    family = binomial(link = "logit"))
            coef.est coef.se
(Intercept)  4.81     0.68  
pclass2nd   -1.40     0.48  
pclass3rd   -3.79     0.67  
sexmale     -2.56     1.00  
age         -0.06     0.02  
---
\end{Soutput}
\end{Schunk}
