loop in r until value converges and stores all the outputs -


I would like to repeat this process until there is no condition to censor results.

Here is a simple matter, where I know the number of cycles in order to perform a loop: 

  # Example of figures Speplus & lt; - rnorm (100,50,50) encyclopedia and lt; - 1000 outcomes & lt; - Matrix (Enro = Encyclix, Nackel = 1)   
 Repeat the process of # N chakras  
 for  (i (sample (SMPLS, 50) ) 1 / NCK () (Output) (SMPLS, 50)) P> But my case is different in that sense I do not know the nosy. I want to continue sampling until the samples get very little variance or convergence (I think this is "while" loop. For example, unless vsd is less than 1 in the following case.  
  vsd & lt; - (SMD, 50)) while (vs> 1) {outm [i] & lt; - Mean (sample (SMPLS, 50)) vsd & lt; - SD  
 
  Edit: - rnorm (100,50,50) ITER & lt; - 0 # Maximum Travel Itermax & LT; - 1000 outm & lt; - Representative (NA, itermax) vsd & lt; - 2, while ((vsd> 1) & amp; (ITER's & lt; itermax)} {outm [ITER] & lt; - Mean (sample (smpls, 50)) vsd & lt; - SD (outm) ITER & lt; Error in late ITER + 1} ((vsd> 1) & amp; amp; amp; amp; amp; amp; amp; amp; amp; amp; amp; amp; amp; amp; amp; amp; amp; amp; amp & amp; amp; amp; amp; amp; amp & amp; amp · amp; amp · amp; amp;;; 
   The main idea of ​​reaching convergence is to save it, though with the above example only the mean function is fast, in my original function it requires important timing to be reiterated e and I I want to stop it when it is converged.     
 
 
 Here's a solution:  
 data  
  set.seed (123) # so that you will repeat what I have done; & Gt; - rnorm (1005050)   
 I think you need some initial cycles (minimum iterations) so that you can get false convergence because you have very few samples Run some specimens - say Ministers You need a maximum trend so that your loops are not wild - say maxter  
  Earthcovers  tolerance) {ITER & lt; - miniter + 1 sdout & lt; - Representative (NA, maxiter), while ((vsd> Tolerance) & amp; amp; (ITER's & lt; maxiter)) {ITER & lt; - ITER + 1 outm [ITER] & lt; - Mean (sample (smpls, 50)) vsd & lt; - SD (outm, na.rm = TRUE) sdout [iter] & lt; - vsd} Out & lt; - List (outm, sdout) returns (outside)} and {return (outm)}} & lt; - meanconverge (= ([2]), pp = ".", col = "red")   
  img src = "https: //i.stack.imgur .com / zUabJ.jpg "alt =" Enter image details here ">  
  plot (uplist (outside [1]), pp =". ", col =" red ") < / code>  
 Enter image details here   < / html>

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