[R] Custom sampling method in R XXXX

[Dan Abner]

Hi Daniel,

Great! Many thanks!

Dan


On Mon, Jun 23, 2014 at 8:23 PM, Daniel Nordlund
<djnordlund at frontier.com> wrote:
> Something like this could work
>
>
> x <- 0.250
> new_sample <- function(xx) {
>   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)
>   jj <- c(0,0,j,1,1)
>   ndx <- which(j == xx)
>   sample(jj[ndx:(ndx+4)], size=1, p=probs, replace=TRUE)
> }
> new_sample(x)
>
>
>
> Daniel Nordlund
> Bothell, WA USA
>
>
>> -----Original Message-----
>> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
>> On Behalf Of Dan Abner
>> Sent: Monday, June 23, 2014 3:19 PM
>> To: Greg Snow
>> Cc: r-help at r-project.org
>> Subject: Re: [R] Custom sampling method in R XXXX
>>
>> 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
>> >>
>> >> ______________________________________________
>> >> R-help at r-project.org mailing list
>> >> https://stat.ethz.ch/mailman/listinfo/r-help
>> >> PLEASE do read the posting guide http://www.R-project.org/posting-
>> guide.html
>> >> and provide commented, minimal, self-contained, reproducible code.
>> >
>> >
>> >
>> > --
>> > Gregory (Greg) L. Snow Ph.D.
>> > 538280 at gmail.com
>>
>> ______________________________________________
>> R-help at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide http://www.R-project.org/posting-
>> guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.