multilevel multiple imputation

//Imputation

//LG5.0//

version = 5.0

infile 'C:\data\blood\elsa0246LGb.txt'

model

title sysvalimpute;

options

  maxthreads=all;

  algorithm

  . . .

  quadrature  nodes=6;

  missing  includeall;

  output parameters=first;

  outfile 'bloodcomplete.txt' imputation=20
     keep othercov;

variables

  caseid idauniq;

  dependent sysval continuous;

  independent age, she, intermed, managerial, msingle, ...;

  latent

     rint continuous 8, rslope continuous;

equations

  sysval <- 1 + (1) rint + age + (1) rslope age + age age + ...;

  rint;  rslope;  rint <-> rslope; sysval;

{

 85.16273763812144

 1.26193281333455  ...

 183.2066291256809

}

end model

//Analysis

//LG5.0//

version = 5.0

infile 'C:\data\_codeELSA\bloodcomplete.txt'

model

title sysfrn3;

options

  maxthreads=all;

  algorithm

  . . .

  quadrature  nodes=6;

  missing  includeall;

  output

     parameters=effect standarderrors estimatedvalues=model probmeans=model predictionstatistics;

variables

  imputationid imputation_#;

  caseid idauniq;

  dependent sysval continuous;

  independent age, she, intermed, managerial, msingle, ...;

  latent

     rint continuous 8, rslope continuous;

equations

  sysval <- 1 + (1) rint + age + (1) rslope age + age age + ...;

  rint;  rslope;  rint <-> rslope; sysval;

{

 87.23514967682135 ...

 1.215938330896753

}

end model