Combine multiple runs of Amelia

Combines multiple runs of amelia with the same arguments and data into one amelia object.

ameliabind(...)

Arguments

...	one or more objects of class amelia with the same arguments and created from the same data.

Value

An object of class amelia.

Details

ameliabind will combine multiple runs of amelia into one object so that you can utilize diagnostics and modelling on all the imputations together. This function is useful for combining multiple runs of amelia run on parallel machines.

Note that ameliabind only checks that they arguments and the missingness matrix are identical. Thus, it could be fooled by two datasets that are identical up to a transformation of one variable.

See also

amelia

Examples

data(africa)
a1.out <- amelia(x = africa, cs = "country", ts = "year", logs = "gdp_pc")
#> -- Imputation 1 --
#> 
#>   1  2
#> 
#> -- Imputation 2 --
#> 
#>   1  2
#> 
#> -- Imputation 3 --
#> 
#>   1  2  3
#> 
#> -- Imputation 4 --
#> 
#>   1  2
#> 
#> -- Imputation 5 --
#> 
#>   1  2  3
#> 
a2.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  3
#> 
#> -- Imputation 4 --
#> 
#>   1  2
#> 
#> -- Imputation 5 --
#> 
#>   1  2  3
#> 
all.out <- ameliabind(a1.out, a2.out)
summary(all.out)
#> 
#> Amelia output with 10 imputed datasets.
#> Return code:  1 
#> Message:  Normal EM convergence 
#> 
#> Chain Lengths:
#> --------------
#> Imputation 1:  2
#> Imputation 2:  2
#> Imputation 3:  3
#> Imputation 4:  2
#> Imputation 5:  3
#> Imputation 6:  3
#> Imputation 7:  3
#> Imputation 8:  3
#> Imputation 9:  2
#> Imputation 10:  3
#> 
#> Rows after Listwise Deletion:  115 
#> Rows after Imputation:  120 
#> Patterns of missingness in the data:  3 
#> 
#> Fraction Missing for original variables: 
#> -----------------------------------------
#> 
#>            Fraction Missing
#> year             0.00000000
#> country          0.00000000
#> gdp_pc           0.01666667
#> infl             0.00000000
#> trade            0.04166667
#> civlib           0.00000000
#> population       0.00000000
#> 
plot(all.out)