Supplementary Material for Zhang (2015)

R code for data generation

library(sn)
library(mvtnorm)
library(normalp)
 
set.seed(20120301)
 
### normal data
mL<-5
mS<-2
vL<-2
vS<-1
vLS<-0
vE<-1
 
TT<-5
N<-500
R<-100
 
mu<-c(mL,mS)
sigma<-array(c(vL,vLS,vLS,vS), dim=c(2,2))
 
 
for (k in 1:R){
y<-array(NA, dim=c(N, TT))
LS<-array(NA, dim=c(N, 2))
 
error<-NULL
 
for (t in 1:TT){
	error<-cbind(error, rnorm(N,df=df)*sqrt(vE))
}
 
for (i in 1:N){
  LS[i,]<-rmvnorm(1, mu, sigma)
  for (j in 1:TT){
    y[i, j] <- LS[i,1] + LS[i,2]*j + error[i,j]
  }
}
 
 
filename<-paste('data/data',k, '.txt',sep='')
write.table(y,filename,row.names=F,col.names=F)
}
 
### t data
mL<-5
mS<-2
vL<-2
vS<-1
vLS<-0
vE<-1/3
df<-3
 
TT<-5
N<-500
R<-100
 
mu<-c(mL,mS)
sigma<-array(c(vL,vLS,vLS,vS), dim=c(2,2))
 
 
for (k in 1:R){
y<-array(NA, dim=c(N, TT))
LS<-array(NA, dim=c(N, 2))
 
error<-NULL
 
for (t in 1:TT){
	error<-cbind(error, rt(N,df=df)*sqrt(vE))
}
 
for (i in 1:N){
  LS[i,]<-rmvnorm(1, mu, sigma)
  for (j in 1:TT){
    y[i, j] <- LS[i,1] + LS[i,2]*j + error[i,j]
  }
}
 
 
filename<-paste('data/data',k, '.txt',sep='')
write.table(y,filename,row.names=F,col.names=F)
}
 
### Exponential power data
sig<-c(1,1,1,1)
beta<- c(-.8, -.8, .8, .8) #rep(-.8, 5)
mL<-5
mS<-2
vL<-2
vS<-1
vLS<-0
 
TT<-4
N<-500
 
p<-2/(1+beta)
s<-sig*sqrt(gamma(1/p)/gamma(3/p))/p^(1/p)
 
 
mu<-c(mL,mS)
sigma<-array(c(vL,vLS,vLS,vS), dim=c(2,2))
 
y<-array(NA, dim=c(N, TT))
LS<-array(NA, dim=c(N, 2))
 
for (k in 1:100){
error<-NULL
 
for (t in 1:TT){
	error<-cbind(error, rnormp(N,0,s[t],p[t]))
}
 
for (i in 1:N){
  LS[i,]<-rmvnorm(1, mu, sigma)
  for (j in 1:TT){
    y[i, j] <- LS[i,1] + LS[i,2]*j + error[i,j]
  }
}
 
filename<-paste('data/t4data',k, '.txt',sep='')
write.table(y,filename,row.names=F,col.names=F)
}
 
## T=5
sig<-c(1,1,1,1,1)
beta<- c(-.8, -.8, 0, .8, .8) #rep(-.8, 5)
mL<-5
mS<-2
vL<-2
vS<-1
vLS<-0
 
TT<-5
N<-500
 
p<-2/(1+beta)
s<-sig*sqrt(gamma(1/p)/gamma(3/p))/p^(1/p)
 
 
mu<-c(mL,mS)
sigma<-array(c(vL,vLS,vLS,vS), dim=c(2,2))
 
y<-array(NA, dim=c(N, TT))
LS<-array(NA, dim=c(N, 2))
 
for (k in 1:100){
error<-NULL
 
for (t in 1:TT){
	error<-cbind(error, rnormp(N,0,s[t],p[t]))
}
 
for (i in 1:N){
  LS[i,]<-rmvnorm(1, mu, sigma)
  for (j in 1:TT){
    y[i, j] <- LS[i,1] + LS[i,2]*j + error[i,j]
  }
}
 
filename<-paste('data/t5data',k, '.txt',sep='')
write.table(y,filename,row.names=F,col.names=F)
}
 
 
### Skew normal data
mL<-5
mS<-2
vL<-2
vS<-1
vLS<-0
vE<-sqrt(pi/(pi-1))
alpha <- 10
delta<-alpha/sqrt(1+alpha^2)
omega<-sqrt(1/(1-2*delta^2/pi))
s.mu<- -omega*delta*sqrt(2/pi)
 
TT<-5
N<-500
 
mu<-c(mL,mS)
sigma<-array(c(vL,vLS,vLS,vS), dim=c(2,2))
 
y<-array(NA, dim=c(N, TT))
LS<-array(NA, dim=c(N, 2))
my<-array(NA, dim=c(N, TT))
 
for (k in 1:100){
error<-NULL
 
for (t in 1:TT){
	error<-cbind(error, rsn(N,s.mu, omega, alpha))
}
 
for (i in 1:N){
  LS[i,]<-rmvnorm(1, mu, sigma)
  for (j in 1:TT){
  	my[i,j]<- LS[i,1] + LS[i,2]*j
    #y[i, j] <- rsn(1,my[i,j],vE,alpha) #+ error[i,j]
	y[i, j] <- my[i,j] + error[i,j]
  }
}
 
filename<-paste('data/data',k, '.txt',sep='')
write.table(y,filename,row.names=F,col.names=F)
}

Mplus code for bootstrap standard errors of growth curve models

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TITLE:        categorical DFM
 
DATA:         FILE = data.txt;
 
VARIABLE:     NAMES = y1-y4; 
              USEVARIABLES = y1-y4
 
ANALYSIS: 
  BOOTSTRAP = 1000;
 
MODEL:         
  i s | y1@0 y2@1 y3@2 y4@3;
  y1-y4 (a);

SAS code

SAS codes for the growth curve model with the normal error distribution

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PROC MCMC DATA=ndata  NBI=5000 NMC=40000 THIN=2 SEED=17 INIT=RANDOM DIAG=(ESS GEWEKE) STATISTICS(ALPHA=0.05)=(SUMMARY INTERVAL) OUTPOST=histnorm; 
ODS SELECT PARAMETERS POSTSUMMARIES POSTINTERVALS GEWEKE ESS TADPANEL;
ARRAY b[2] L S;
ARRAY beta[2];
ARRAY Sigma_b[2,2];
ARRAY beta0[2] (0 0);
ARRAY sigma0[2,2] (1000 0 0 1000);
ARRAY V[2,2] (1 0 0 1);
PARMS beta {5 2};
PARMS sigma_b {1 0 0 1};
PARMS var_e 1;
PRIOR beta ~ MVN(beta0, sigma0);
PRIOR Sigma_b ~ IWISH(2, V);
PRIOR var_e ~ IGAMMA(0.001, SCALE=0.001);
RANDOM b ~ MVN(beta, Sigma_b) SUBJECT=id;
mu = L + S * time;
MODEL y ~ NORMAL(mu, var=var_e);
RUN;

SAS codes for the growth curve model with the t error distribution

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PROC MCMC DATA=ndata NMC=40000  NBI=5000 THIN=2
DIC SEED=17 INIT=RANDOM DIAG=(ESS GEWEKE) 
STATISTICS(ALPHA=0.05)=(SUMMARY INTERVAL)
OUTPOST=histt; 
ODS SELECT PARAMETERS POSTSUMMARIES POSTINTERVALS GEWEKE ESS TADPANEL;
ARRAY b[2] L S;
ARRAY beta[2];
ARRAY Sigma_b[2,2];
ARRAY beta0[2] (0 0);
ARRAY sigma0[2,2] (1000 0 0 1000);
ARRAY V[2,2] (1 0 0 1);
PARMS beta {5 2};
PARMS sigma_b {1 0 0 1};
PARMS sigma_e 1;
PARMS g1 0;
PARMS g2 0;
PARMS g3 0;
PARMS g4 0;
PARMS g5 0;
PRIOR beta ~ MVN(beta0, sigma0);
PRIOR Sigma_b ~ IWISH(2, V);
PRIOR sigma_e ~ IGAMMA(0.01, SCALE=0.01);
PRIOR g:~UNIFORM(-.6,5);
RANDOM b ~ MVN(beta, Sigma_b) SUBJECT=id;
mu = L + S * time;
g = g1*(time=1)+ g2*(time=2)+g3*(time=3)+g4*(time=4)+g5*(time=5);
p = 1+g;
ll = -LOG(sigma_e) + .5*LGAMMA(1.5*p)-1.5*LGAMMA(p/2)-log(p) - (abs(y-mu)/sigma_e)**(2/p) * (GAMMA(1.5*p)/GAMMA(.5*p))**(1/p);
var_e = sigma_e*sigma_e;
MODEL y ~ general(ll);
RUN;

SAS codes for the growth curve model with the exponential power error distribution

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PROC MCMC DATA=ndata NMC=100000  NBI=5000 THIN=2 MONITOR=(_PARMS_ var_e)
DIC SEED=13 INIT=RANDOM DIAG=(ESS GEWEKE(F1=.2 F2=.5)) 
STATISTICS(ALPHA=0.05)=(SUMMARY INTERVAL)
OUTPOST=histged; 
ODS SELECT PARAMETERS POSTSUMMARIES POSTINTERVALS GEWEKE ESS TADPANEL;
ARRAY b[2] L S;
ARRAY beta[2];
ARRAY Sigma_b[2,2];
ARRAY beta0[2] (0 0);
ARRAY sigma0[2,2] (1000 0 0 1000);
ARRAY V[2,2] (1 0 0 1);
PARMS beta {5 2};
PARMS sigma_b {1 0 0 1};
PARMS sigma_e 1;
PARMS g1 0;
PARMS g2 0;
PARMS g3 0;
PARMS g4 0;
PARMS g5 0;
PRIOR beta ~ MVN(beta0, sigma0);
PRIOR Sigma_b ~ IWISH(2, V);
PRIOR sigma_e ~ IGAMMA(0.01, SCALE=0.01);
PRIOR g:~UNIFORM(-.6,5);
RANDOM b ~ MVN(beta, Sigma_b) SUBJECT=id;
mu = L + S * time;
g = g1*(time=1)+ g2*(time=2)+g3*(time=3)+g4*(time=4)+g5*(time=5);
p = 1+g;
ll = -LOG(sigma_e) + .5*LGAMMA(1.5*p)-1.5*LGAMMA(p/2)-log(p) - (abs(y-mu)/sigma_e)**(2/p) * (GAMMA(1.5*p)/GAMMA(.5*p))**(1/p);
var_e = sigma_e*sigma_e;
MODEL y ~ general(ll);
RUN;

SAS codes for the growth curve model with the skew normal error distribution

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PROC MCMC DATA=ndata NMC=40000  NBI=5000 THIN=2
DIC SEED=17 INIT=RANDOM DIAG=(ESS GEWEKE) 
STATISTICS(ALPHA=0.05)=(SUMMARY INTERVAL) MONITOR=(_PARMS_ var_e)
OUTPOST=histsn; 
ODS SELECT PARAMETERS POSTSUMMARIES POSTINTERVALS GEWEKE ESS TADPANEL;
ARRAY b[2] L S;
ARRAY beta[2];
ARRAY Sigma_b[2,2];
ARRAY beta0[2] (0 0);
ARRAY sigma0[2,2] (1000 0 0 1000);
ARRAY V[2,2] (1 0 0 1);
PARMS beta {5 2};
PARMS sigma_b {1 0 0 1};
PARMS sigma_e 1;
PARMS alpha 0;
PRIOR beta ~ MVN(beta0, sigma0);
PRIOR Sigma_b ~ IWISH(2, V);
PRIOR sigma_e ~ IGAMMA(0.01, SCALE=0.01);
PRIOR alpha~UNIFORM(-.5,.5);
RANDOM b ~ MVN(beta, Sigma_b) SUBJECT=id;
mu = L + S * time;
var_e = sigma_e*sigma_e;
e = y - mu;
xi = -sigma_e*alpha/sqrt(1+alpha*alpha)*sqrt(2/3.1415926);
ll = log(2)-log(sigma_e)+logpdf('normal',(e-xi)/sigma_e,0,1)+logcdf('normal',alpha*(e-xi)/sigma_e,0,1);
MODEL e ~ general(ll);
RUN;

Procedure to run the simulation

The basic idea is to run SAS within R to conduct the simulation. First, an R script is used to copy data and then run a SAS script to conduct Bayesian analysis. Second, within the SAS script, the results are saved into .txt files. Third, R then reads in the SAS results and combines simulation results.

Bayesian analysis can be time consuming. The code has been run on high performance computers and was not tested on personal computers.

The R code for running the simulation for t data is

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for (SS in c(100,300,500)){
   for (TT in 4:5){
	for (i in 1:100){
		file.name<-paste('data/data',i,'.txt', sep='')
		y<-read.table(file.name)
		y<-y[1:SS, 1:TT]
		file.name<-paste('data.txt', sep='')
 
		dset<-cbind(rep(1:TT, each=SS),rep(1:SS,TT),c(y))
		write.table(dset,'ndata.txt',row.names=F,col.names=F)
 
		system(paste('sas tsas.sas', sep=''))
		out<-paste('output/norm-SS-',SS,'TT', TT,'-', i, '.txt', sep='')
		temp<-try(file.copy('histnorm.txt', out, overwrite=T))
 
		out<-paste('output/t-SS-',SS,'TT', TT,'-', i, '.txt', sep='')
		temp<-try(file.copy('histt.txt', out, overwrite=T))
 
	}
   }
}

The complete SAS script is

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* Normal - t data;
data ndata;
infile "ndata.txt";
input time id y;
run;
 
PROC MCMC DATA=ndata NMC=40000  NBI=5000 THIN=2
DIC SEED=17 INIT=RANDOM DIAG=(ESS GEWEKE(F1=.2 F2=.5)) 
STATISTICS(ALPHA=0.05)=(SUMMARY INTERVAL)
OUTPOST=histnorm; 
ODS SELECT PARAMETERS POSTSUMMARIES 
   TADPANEL GEWEKE ESS DIC POSTINTERVALS;
ARRAY b[2] L S;
ARRAY beta[2];
ARRAY Sigma_b[2,2];
ARRAY beta0[2] (0 0);
ARRAY sigma0[2,2] (1000 0 0 1000);
ARRAY V[2,2] (1 0 0 1);
PARMS beta {5 2};
PARMS sigma_b {1 0 0 1};
PARMS var_e 1;
PRIOR beta ~ MVN(beta0, sigma0);
PRIOR Sigma_b ~ IWISH(2, V);
PRIOR var_e ~ IGAMMA(0.001, SCALE=0.001);
RANDOM b ~ MVN(beta, Sigma_b) SUBJECT=id;
mu = L + S * time;
MODEL y ~ NORMAL(mu, var=var_e);
RUN;
 
/*
proc sgplot data=histnorm;
series x=Iteration y=beta1;
yaxis label="beta0";
run;
*/
data _NULL_;
set histnorm;
file "histnorm.txt";
put Iteration beta1 beta2 Sigma_b1 Sigma_b2 Sigma_b4 var_e;
run;
 
 
PROC MCMC DATA=ndata NMC=80000  NBI=5000 THIN=2
DIC SEED=17 INIT=RANDOM DIAG=(ESS GEWEKE(F1=.2 F2=.5)) 
STATISTICS(ALPHA=0.05)=(SUMMARY INTERVAL)
OUTPOST=histt; 
ODS SELECT PARAMETERS POSTSUMMARIES 
   TADPANEL GEWEKE ESS DIC POSTINTERVALS;
ARRAY b[2] L S;
ARRAY beta[2];
ARRAY Sigma_b[2,2];
ARRAY beta0[2] (0 0);
ARRAY sigma0[2,2] (1000 0 0 1000);
ARRAY V[2,2] (1 0 0 1);
PARMS beta {5 2};
PARMS sigma_b {1 0 0 1};
PARMS var_e 1;
PARMS df 5;
PRIOR beta ~ MVN(beta0, sigma0);
PRIOR Sigma_b ~ IWISH(2, V);
PRIOR var_e ~ IGAMMA(0.01, SCALE=0.01);
PRIOR df~UNIFORM(1,500);
RANDOM b ~ MVN(beta, Sigma_b) SUBJECT=id;
mu = L + S * time;
MODEL y ~ t(mu, var=var_e, df);
RUN;
/*
proc sgplot data=histt;
series x=Iteration y=beta1;
yaxis label="beta0";
run;
*/
data _NULL_;
set histt;
file "histt.txt";
put Iteration beta1 beta2 Sigma_b1 Sigma_b2 Sigma_b4 var_e df;
run;

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Table of Contents

Supplementary Material for Zhang (2015)

R code for data generation

Mplus code for bootstrap standard errors of growth curve models

SAS code

SAS codes for the growth curve model with the normal error distribution

SAS codes for the growth curve model with the t error distribution

SAS codes for the growth curve model with the exponential power error distribution

SAS codes for the growth curve model with the skew normal error distribution

Procedure to run the simulation

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