I am running multivariate multilevel growth model in SAS with two random components (one for intercept and one for slope) on > 4000 subjects each measured 10 times or more. I have two or more dependent variables (DV). My data are structured as follows in which med is the dummy for two DVs, listed as Ys : id       med   Y     t    t1   age 1          x     70    1   11   45 1          x     34    2   12   45 1          x     54    3   13   45 1          y     45    1   21   45 1          y     34    2   22   45 1          y     56    3   23   45 2          x     70    1   11   66 2          x     39    2   12   66 2          x     59    3   13   66 2          y     45    1   21   66 2          y     64    2   22   66 2          y     46    3   23   66 ..... ........ I want to estimate the following variance structures G Matrix                                            R Matrix int(x)       U1                                         t11      r1 int(x)       U2  U3                                   t12      r2   r3 slope(x)   U4  U5  U6                             t13      r4   r5   r6 slope(y)   U7  U8  U9   U10                    t21      r7   r8   r9  r10                                                              t22      r11   r12   r13  r14   r15                                                               t23      r16   r17   r18  r19   r20   r21  * Growth model SAS code; proc mixed data=mult  covtest asycorr noclprint; class med t1; model y= med t age med*t med*age /noint s ddfm=kr; random med med*t / type=un sub=id g gcorr; repeated t1 / type=un subject=id r rcorr;  run; This model runs error.  The 'infinite likelihood' problem is gone if a dummy of med by t (i.e., t1) is used.  However, I have the following issues. If type=un is used on the repeated statement it warns of nonpositive definite;  covariance or variance estimates involving random slopes are nearly zero or too small. If  un(1) on repeated, it provides "Asymptotic variance matrix of covariance parameter estimates has been found to be singular...." It runs only when '  / type=vc ...' is specified on the repeated statement.  I have tried dropping either random or repeated statement and it works but not when both stay on. Where is the issue? How can I correctly estimate the G-matrix and R-matrix as specified above? Please help.   d model ... View more

I am running multivariate multilevel growth model on SAS 9.3 (x64bit) with two random components (one for intercept and one for slope). I have two or more dependent variables (DV). My data are structured as follows in which med is the dummy for two DVs: enrolid med adh   t    t1  age 1          x     70    1   1   45 1          x     34    2   2   45 1          x     54    3   3   45 1          y     45    1   1   45 1          y     34    2   2   45 1          y     56    3   3   45 2          x     70    1   1   66 2          x     39    2   2   66 2          x     59    3   3   66 2          y     45    1   1   66 2          y     64    2   2   66 2          y     46    3   3   66 * Means Model; proc mixed data= mult  covtest asycorr noclprint; class enrolid med t1; model adh = med age med*age /noint s; random med  /type=un sub=enrolid g gcorr; repeated /type=vc group=med subject=enrolid; run; * Growth model; proc mixed data= mult  covtest asycorr noclprint; class enrolid med t1 ; model adh = med age t med*t med*age /noint s; random med med*t /type=un sub=enrolid g gcorr; repeated /type=vc group=med subject=enrolid; run; The above models run. However, when I try to change specifications on the repeated statement it does NOT work (eg., infinite likelihood is assumed..., non positive definite, etc). My data do not have duplicate rows. I am trying to specify a correlated R within each med type and I used '' repeated med/type=un subject=enrolid". Also, I tried '' repeated  t1/ type=un group=med subject=enrolid", it does not resolve. Where am I wrong? Please help me with appropriate coding for testing an alternative/correlated R-side specification. ... View more

Hi, I am running multivariate multilevel growth model on SAS 9.3 (x64bit) with two random components (one for intercept and one for slope). I have two dependent variables (DV). I have approximately 6000 subjects and 80000 rows or 40000 for each DV.  I used the following codes in which med is the dummy for two DVs: proc mixed data= mult  covtest asycorr noclprint; ods output solutionF=Fixed7 covparms=cov7    tests3=test3_7 fitstatistics=fitstat7  gcorr=gcor7; class enrolid med ; model adh = med med*qtr med*age /noint s; random med med*qtr /type=un sub=enrolid g gcorr; repeated /type=vc group=med subject=enrolid; run; It computes when Random intercept model is run (i.e., if 'med*qtr' is dropped from the model and random statements). But, SAS can NOT run it as growth model and reports "integer overflow...2429.72M bytes of memory cannot be allocated" problem although I have 4 GB RAM. I tried to allot as much memory as possible but it did not work out. Please suggest any solutions or share if you see any issues. ... View more