Hi all,   I'm analyzing a longitudinal dataset that examines a quantitative and verbal task across 12 time points. I first fit a nonlinear mixed model to each task separately. However, my goal is to fit a multivariate model that estimates the covariances of the random effects between the tasks.   The dataset looks like this for 1 subject (and part of subject 2):     The SUBID variable identifies the participant. The Response variable the dependent variable that consists of literally stacking the QRTS and VRTS variables. The TimeQ and TimeV variables denote the time points for the quantitative and verbal tasks, respectively. The Type variable denotes whether or not the task is quantitative or verbal.   Here is the code I used to fit the multivariate model:   *multivariate structured latent curve model combining the quant and verbal scores using PROC NLMIXED*; proc nlmixed data = combined method = gauss gconv = 0; parms aV = 7 bV = 7 rV = -.7 s2V var_aV cov_aVbV var_bV cov_aVrV cov_bVrV var_rV aQ = 8 bQ = 8 rQ = -.7s2Q var_aQ cov_aQbQ var_bQ cov_aQrQ cov_bQrQ var_rQ cov_aVaQ cov_aVbQ cov_aVrQ cov_bVaQ cov_bVbQ cov_bVrQ cov_rVaQ cov_rVbQ cov_rVrQ = .1; *aQ = a parameter for quant*; *bQ = b parameter for quant*; *rQ = r parameter for quant*; *aV = v parameter for verbal*; *bV = b parameter for verbal*; *rV = r parameter for verbal*;; pi = arcos(-1); n0Q = aQ + z0Q; n1Q = bQ + z1Q; n2Q = z2Q; *fixed plus random for quantitative*; basis0Q = 1-exp(-rQ*(TimeQ-1)); basis1Q = exp(-rQ*(TimeQ-1)); basis2Q = (aQ-bQ)*(TimeQ-1)*exp(-rQ*(TimeQ-1)); *basis functions for quantitative*; n0V = aV + z0V; n1V = bV + z1V; n2V = z2V; *fixed plus random for verbal*; basis0V = 1-exp(-rV*(TimeV-1)); basis1V = exp(-rV*(TimeV-1)); basis2V = (aV-bV)*(TimeV-1)*exp(-rV*(TimeV-1)); *basis functions for verbal*; if Type = "Q" then predv = n0Q*basis0Q + n1Q*basis1Q + n2Q*basis2Q; else if Type = "V" then predv = n0V*basis0V + n1V*basis1V + n2V*basis2V; llQ = (-1/2)*(log(2*pi) + (((QRTS - predv)**2)/s2Q) + log(s2Q)); llV = (-1/2)*(log(2*pi) + (((VRTS - predv)**2)/s2V) + log(s2V)); if Type = "Q" then ll = llQ; else if Type = "V" then ll = llV; model Response ~ general(ll); random z0Q z1Q z2Q z0V z1V z2V ~ normal([0,0,0,0,0,0], [var_aQ, cov_aQbQ, var_bQ, cov_aQrQ, cov_bQrQ, var_rQ, cov_aVaQ, cov_aVbQ, cov_aVrQ, cov_bVaQ, cov_bVbQ, cov_bVrQ, cov_rVaQ, cov_rVbQ, cov_rVrQ, var_aV, cov_aVbV, var_bV, cov_aVrV, cov_bVrV, var_rV]) subject = SUBID; run; When I run this code, I get an error message saying that SAS stopped processing because of insufficient memory. I'm guessing that this happened because my code was incorrect. Any suggestions or help is greatly appreciated. Thanks! ... View more

Hi all,   I'm fitting a nonlinear mixed-effects model in NLMIXED to a set of longitudinal birth weight data. Because the data is strictly positive, I wanted to model the response with a log-normal distribution. As you may be aware, the log-normal distribution is not a default option in the MODEL statement. So I went ahead and computed the log-likelihood and used the GENERAL statement. Below is the code I used to fit the model:   proc nlmixed data = weight gconv = 0; parms b0 = 1 b1 = .6 b2 = 1 var0 = .02 cov10 = -.01 var1 = .02 cov20 = -.001 cov21 = -.04 var2 = .4 s2e = .001; beta0 = b0 + u0; beta1 = b1 + u1; beta2 = b2 + u2; predv = beta0 + beta1*(1-exp(-beta2*Years)); pi = arcos(-1); ll = (-1/2)*((log(2*pi)) + (log(s2e)) + ((log(Years - predv)**2)/s2e)) + Years; model Weightkg ~ general(ll); random u0 u1 u2 ~ normal([0,0,0], [var0,cov10,var1,cov20,cov21,var2]) subject = ID; run; When I run the code, this is the error code I receive: NOTE: Execution error for observation 1   I've looked around and a possible reason for this error message is that my starting values may be off. I used starting values from fitting the model using Bayesian estimation with PROC MCMC but to no avail. Has anyone ever dealt with this issue and can offer any solutions? Thanks! ... View more

Hi all,       Hi all, I have a longitudinal data set where the above dates are nested by household and person. The above are dates for one person and one household. I would like to compute the difference between two subsequent dates without creating a lagged variable. Is this possible? Thanks and all help is appreciated. ... View more

Hi all,   I am currently remoting into my university's server to run SAS on the UNIX server through batch mode. I am running a simulation study and am at the part where I am analyzing my data and would like to output the analyses as a separate data set. However, when I try and do so, the output data sets are not being created. Any ideas as to what is going on? Any help is much appreciated. Below is the code I have used:   *fitting a misinformed multilevel model to the 4 time point data and assuming a normal response*; *specifying a library for the ods output to go to*; libname output '/home/users/txbanh.AD3/output'; *importing the simulated data set*; proc import datafile = "Four_Normal.csv" out = Four_Normal dbms = csv; getnames = YES; run; *starting the macro to analyze the data*; %macro MCMC; %let Size_List = 5|10|15|20|25|30|35|40|45|50|60|70|80|90|100; %let num_Size = %eval(%sysfunc(count(&Size_list,|))+1); %do i = 1 %to &num_Size; %let Size = %scan(&Size_list.,&i,|); %let v = %eval(&Size-2); proc mcmc data = Four_Normal nmc = 50000 thin = 5; by Iter; where Size = &Size; *here is where I specify the ods output. I am trying to create ods output for each sample size and then I concatenate them all at the end*; ods output PostSummaries = output.PostSummaries_&Size PostIntervals = output.PostIntervals_&Size; ... View more