[R] Clarification on Simulation and Iteration

[David Winsemius]

On Jul 31, 2015, at 8:41 PM, Christopher Kelvin wrote:

> Thanks Dave.
> 
> What I actually want is to obtain say 10, different sets of (n=50) data for every 10,000 iterations I run. You will realise that the current code produces one set of data (n=50). I want 10 different sets of 50 observations at one run. I hope this makes sense.

I would think that either `replicate(50, ...)` or `for(i in 1:50) {...}` would suffice. Unless of course the phrase "want 10 different sets of 50 observations at one run` means something different than it appears to request.


> 
> Chris Guure 
> 
> 
> 
> On Saturday, August 1, 2015 3:32 AM, David Winsemius <dwinsemius at comcast.net> wrote:
> 
> 
> On Jul 31, 2015, at 6:36 PM, Christopher Kelvin via R-help wrote:
> 
>> Dear All,
>> I am performing some simulations for a new model. I run about 10,000 iterations with a sample of 50 datasets and this returns one set of 50 simulated data. 
>> 
>> Now, what I need to obtain is 10 sets of the 50 simulated data out of the 10,000 iterations and not just only 1 set.  The model is the Copas selection model for publication bias in Mete-analysis. Any one who knows this model has any suggestion for the improvement of my code is most welcome.
>> 
>> Below is my code. 
>> 
>> 
>> Kind regards
>> 
>> 
>> Chris Guure
>> University of Ghana
>> 
>> 
>> 
>> 
>> install.packages("msm") 
>> library(msm) 
>> 
>> 
>> rho1=-0.3; tua=0.020; n=50; d=-0.2; rr=10000; a=-1.3; b=0.06 
>> si<-rtnorm(n, mean=0, sd=1, lower=0, upper=0.2)# I used this to generate standard errors for each study 
>> set.seed(21111)   ## I have stored the data and the output in this seed 
>> 
>> for( i in 1:rr){ 
>> 
>> mu<-rnorm(n,d,tua^2)              # prob. of each effect estimate 
>> rho<-si*rho1/sqrt(tua^2 + si^2) # estimate of the correlation coefficient 
>> mu0<- a + b/si       # mean of the truncated normal model (Copas selection model) 
>> y1<-rnorm(mu,si^2)            # observed effects zise 
>> z<-rnorm(mu0,1)               # selection model 
>> rho2<-cor(y1, z) 
>> 
>> select<-pnorm((mu0 + rho*(y1-mu)/sqrt(tua^2 + si^2))/sqrt(1-rho^2)) 
>> probselect<-ifelse(select<z, y1, NA)# the prob that the study is selected 
>> 
>> probselect 
>> data<-data.frame(probselect,si)    # this contains both include and exclude data 
>> data 
>> data1<-data[complete.cases(data),] # Contains only the included data for analysis 
>> data1 
>> 
>> 
>> }
>> 
> 
> OK. The code runs without error. So .... what exactly is the problem? (I have no experience with the Copas selection model if in fact that is what is being exemplified.)
> 
> -- 
> 
> David Winsemius
> Alameda, CA, USA

David Winsemius
Alameda, CA, USA