\begin{Schunk}
\begin{Sinput}
 M1 <- lm.mi(bmi ~ hyp + chl, miImputeNhanes2)
 display(M1)
\end{Sinput}
\begin{Soutput}
=======================================
Separate Estimates for each Imputation
=======================================

** Chain 1 **
lm(formula = formula, data = mi.data[[i]])
            coef.est coef.se
(Intercept) 21.17     4.42  
hypyes       0.09     2.64  
chl          0.03     0.02  
---
n = 25, k = 3
residual sd = 4.72, R-Squared = 0.07

** Chain 2 **
lm(formula = formula, data = mi.data[[i]])
            coef.est coef.se
(Intercept) 18.71     3.84  
hypyes      -1.16     3.15  
chl          0.05     0.02  
---
n = 25, k = 3
residual sd = 5.18, R-Squared = 0.24

** Chain 3 **
lm(formula = formula, data = mi.data[[i]])
            coef.est coef.se
(Intercept) 21.79     4.41  
hypyes      -2.43     2.54  
chl          0.03     0.02  
---
n = 25, k = 3
residual sd = 4.79, R-Squared = 0.08

** Chain 4 **
lm(formula = formula, data = mi.data[[i]])
            coef.est coef.se
(Intercept) 19.37     3.88  
hypyes      -0.05     2.41  
chl          0.03     0.02  
---
n = 25, k = 3
residual sd = 4.26, R-Squared = 0.12

** Chain 5 **
lm(formula = formula, data = mi.data[[i]])
            coef.est coef.se
(Intercept) 23.78     4.09  
hypyes      -0.70     2.78  
chl          0.02     0.02  
---
n = 25, k = 3
residual sd = 4.40, R-Squared = 0.02

** Chain 6 **
lm(formula = formula, data = mi.data[[i]])
            coef.est coef.se
(Intercept) 16.29     4.66  
hypyes      -4.31     2.41  
chl          0.06     0.02  
---
n = 25, k = 3
residual sd = 4.96, R-Squared = 0.24

** Chain 7 **
lm(formula = formula, data = mi.data[[i]])
            coef.est coef.se
(Intercept) 26.90     3.74  
hypyes       1.38     2.05  
chl          0.00     0.02  
---
n = 25, k = 3
residual sd = 4.09, R-Squared = 0.02

** Chain 8 **
lm(formula = formula, data = mi.data[[i]])
            coef.est coef.se
(Intercept) 28.36     4.70  
hypyes      -0.10     3.78  
chl         -0.01     0.03  
---
n = 25, k = 3
residual sd = 6.24, R-Squared = 0.00

=======================================
Pooled Estimates
=======================================
lm.mi(formula = bmi ~ hyp + chl, mi.object = miImputeNhanes2)
            coef.est coef.se
(Intercept) 22.04     6.08  
hypyes      -0.91     3.33  
chl          0.02     0.03  
---
\end{Soutput}
\begin{Sinput}
 cbind(b=coef(M1), se=se.coef(M1), t=coef(M1)/se.coef(M1))
\end{Sinput}
\begin{Soutput}
                 b    se     t
(Intercept) 22.045 6.082  3.62
hypyes      -0.910 3.334 -0.27
chl          0.025 0.033  0.76
\end{Soutput}
\end{Schunk}
