
Hello,

 

I have some data, and I want to generate random numbers following the distribution of this data (in other words, to generate a synthetic data sets having the stats of a give data set). Reading an old thread I found the following text:

 

>If you can compute the quantile function of the distribution (i.e., the 
>inverse of the integral of the pdf), then you can use the probability 
>integral transform: If U is a U(0,1) random variable and Q is the quantile 
>function of the distribution F, then Q(U) is a random variable distributed 
>as F. 

 

That sounds good, but is there a quick way to do this in R? Let's say my data is contained in "ee", I can get the quantiles using:

 

qq = quantile(ee, probs=(0,1,0.25))

           0%           25%           50%           75%          100% 
-0.2573385519 -0.0041451053  0.0004538924  0.0049276991  0.1037823292

 

Then I "know" how to use the above method to generate Q(U) (by looking up U in the first row, and then mapping it to a number using the second row), but is there an R function that does that? Otherwise I need to write my own to lookup the table.

 

Thanks in advance,

Ivan

 

 

 

 

 

 
 		 	   		  
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