Built using Zelig version 5.1.0.90000

---

zelig() accepts multiply imputed data output objects from the Amelia package as the data argument.

First, using the Amelia package, we multiply impute a dataset with missing values:

library("Amelia")

## Loading required package: Rcpp

## ## 
## ## Amelia II: Multiple Imputation
## ## (Version 1.7.4, built: 2015-12-05)
## ## Copyright (C) 2005-2017 James Honaker, Gary King and Matthew Blackwell
## ## Refer to http://gking.harvard.edu/amelia/ for more information
## ##

data(africa)
a.out <- amelia(x = africa, cs = "country", ts = "year", logs = "gdp_pc")

## -- Imputation 1 --
## 
##   1  2  3
## 
## -- Imputation 2 --
## 
##   1  2  3
## 
## -- Imputation 3 --
## 
##   1  2
## 
## -- Imputation 4 --
## 
##   1  2
## 
## -- Imputation 5 --
## 
##   1  2  3

Then we can use the output object from the Amelia package directly in the data argument for Zelig:

z.out <- zelig(gdp_pc ~ trade + civlib, model = "ls", data = a.out)

Zelig will automatically extract the imputed datasets from the Amelia object, and run the requested model in each of them. When the estimated model parameters are summarized, the results from each imputed dataset are available, but more importantly, the combined answer across the imputed datasets calculated by “Rubin’s Rules” are automatically presented:

summary(z.out)

## Model: Combined Imputations 
## 
##             Estimate Std.Error z value Pr(>|z|)
## (Intercept)   111.76     97.74    1.14  0.25287
## trade          18.13      1.25   14.54  < 2e-16
## civlib       -628.06    182.57   -3.44  0.00058
## 
## For results from individual imputed datasets, use summary(x, subset = i:j)
## Next step: Use 'setx' method

To see the result from an individual imputed dataset, we use the subset argument as:

summary(z.out, subset = 2:3)

## Imputed Dataset 2
## Call:
## z5$zelig(formula = gdp_pc ~ trade + civlib, data = a.out)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -676.07 -227.89  -97.05  171.76  960.10 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  111.436     97.503   1.143 0.255414
## trade         18.109      1.245  14.550  < 2e-16
## civlib      -621.304    181.579  -3.422 0.000858
## 
## Residual standard error: 349.5 on 117 degrees of freedom
## Multiple R-squared:  0.6541, Adjusted R-squared:  0.6482 
## F-statistic: 110.6 on 2 and 117 DF,  p-value: < 2.2e-16
## 
## Imputed Dataset 3
## Call:
## z5$zelig(formula = gdp_pc ~ trade + civlib, data = a.out)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -679.4 -217.8  -95.6  164.1  960.8 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  121.960     95.679   1.275 0.204947
## trade         18.119      1.224  14.803  < 2e-16
## civlib      -645.386    179.835  -3.589 0.000486
## 
## Residual standard error: 346.1 on 117 degrees of freedom
## Multiple R-squared:  0.662,  Adjusted R-squared:  0.6563 
## F-statistic: 114.6 on 2 and 117 DF,  p-value: < 2.2e-16
## 
## Next step: Use 'setx' method

When quantities of interest are plotted, such as expected and predicted values and first differenences, these are correctly pooled across those from each of the m imputed datasets:

z.out$setx()
z.out$sim()
plot(z.out)

Other multiply imputed data

Zelig also includes a function called from_zelig_model() to conform multiply imputed data sets from other sources to a form that zelig can use.