Data:
FILE = 
longitudinal_dyadic wide2.csv;

VARIABLE:
NAMES = id dyadid person idp lengthymean index
deps audits cudits sats trusts commits csts cons
micros isms psss vics sods 
ismp microp pssp vicp sodp satp trustp commitp
cstp conp depp auditp cuditp
depsl auditsl cuditsl 
satsl trustsl commitsl cstsl consl
satpl trustpl commitpl 
cstpl conpl deppl auditpl cuditpl
noncis pover29;

Missing = ALL(-999);

UseVariables are 
csts cons    
sats trusts commits audits;

cluster= dyadid;

within are csts cons    
sats trusts commits audits;
between =  ;

ANALYSIS:
ESTIMATOR = MLR;
Type = twolevel ;
MODEL:
%WITHIN%
NRIac BY csts cons;
[NRIac@0];


RQac BY sats trusts commits;
[RQac@0];


RQac with NRIac ;
RQac NRIac with audits;

%BETWEEN%





Plot:
Type = plot3;
OUTPUT: svalues residual STAND SAMPstat CINT Modindices(2) tech1 tech2 tech3;