[R] Custom sampling method in R XXXX

[Dan Abner]

Hi Greg,

Thanks, this makes sense. I can envision the call to the sample fn
like you are discribing. Any ideas on how to construct the vector? I
still am unclear about that.

Thanks,

Dan

On Mon, Jun 23, 2014 at 5:26 PM, Greg Snow <538280 at gmail.com> wrote:
> The sample function can be used to sample discrete values with
> designated probabilities.  I would just construct your list of 5
> values based on the selected value (duplicating end values if needed,
> so a choice of x=0 would be the vector c(0,0,0, 0.125, 0.25) ), then
> sample from this vector with the probabilities that you specify.
>
> On Mon, Jun 23, 2014 at 3:11 PM, Dan Abner <dan.abner99 at gmail.com> wrote:
>>  Hi all,
>>
>> I have the following situation and a good efficient way to perform
>> this operation in R has not come to me. Any suggestions/input are
>> welcome.
>>
>> I have a user-defined parameter (let's call it x) whose value is
>> selected from a set of possible values (j). Once the user selects one
>> of the values of j for x, then I need to map a probability
>> distribution to the values of j such that the middle probability of
>> .5385 (see probs below) is associated with the value of x and the tail
>> probabilities are assigned to the 2 values below x and 2 values above
>> x in j. Therefore, in the example below:
>>
>>
>> x<-.250
>> j<-c(0.000,0.125,0.250,0.375,0.500,0.625,0.750,0.875,1.000)
>> probs<-c(0.02307692,0.20769231,0.53846154,0.20769231,0.02307692)
>>
>> probabilities would be assigned to the values of j as such:
>>
>> value     probability
>> 0            0.023077
>> 0.125     0.207692
>> 0.25       0.538462
>> 0.375     0.207692
>> 0.5         0.023077
>>
>> And then 1 value of j is selected based on the associated probability.
>> Any ideas on an efficient way to do this?
>>
>> An added dimension of complexity is when the value of x is selected
>> near the parameter boundary of j. If x = 0, then the easiest thing I
>> can think of is to assign probabilities as:
>>
>> value      probability
>> 0             0.76923077
>> 0.125      0.207692
>> 0.25        0.023077
>>
>> However, I am open to other possibilities.
>>
>> Any assistance is appreciated.
>>
>> Thanks,
>>
>> Dan
>>
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>> and provide commented, minimal, self-contained, reproducible code.
>
>
>
> --
> Gregory (Greg) L. Snow Ph.D.
> 538280 at gmail.com