> On Aug 5, 2015, at 4:21 PM, Bert Gunter <bgunter.4...@gmail.com> wrote:
>
> Hint: See below.
>
> On Wednesday, August 5, 2015, John Sorkin <jsor...@grecc.umaryland.edu>
> wrote:
>
>> Colleagues,
>> I need to simulate skewed data so I can run a sample size calculation.
>>
>> I know the 2.5th, 25th, 50th, and 75th centiles of the data (32, 43, 48,
>> 250).
>>
>> data <- matrix(c(75,250,50,48,25,43,2.5,32),nrow=4,ncol=2,byrow=TRUE)
>> dimnames(data) <- list(NULL,c("x","y"))
>> data
>>
>> Is there a way I can use these values to generate simulations of the
>> original data? Of course if the data were normally distributed this would
>> be a piece of cake,
>
>
> Oh -- how? ( a normal distribution is defined by 2 parameters. You appear
> to have 4. ) If you can answer this question, you can probably answer the
> same question for skew data. See also things like Johnson distributions,
> Pearson distributions, and other flexible distribution families. You should
> also probably move to stackexchange, as this is definitely a statistical
> matter. Once you decide what to do, R will have a package to do it.
>
> Others may be able to offer better advice, so wait a bit before proceeding,
> though.
>
> -- Bert
>
> but given the skewness, I don't know how to go about the generating the
>> values that would be expected from a distribution having the observed
>> values at the four centiles.
>> Thank you,
>> John
John,
Just to pick up on Bert’s suggestion, there are some threads over on SE that
discuss similar subject matter, one of which, due to my own curiosity, led me
to:
https://cran.r-project.org/web/packages/rriskDistributions/index.html
which you may find of value.
Regards,
Marc Schwartz
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