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 set
sharing the same stats as a given 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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