Okay, I think I found the reason. This is due to accumulation of nine 5s in the cumsum. Thanks again for the elegant solution.
But I wonder, if the solution is simple then what is the significance of the Research paper by Bentley and Saxe naming “Generating sorted lists of random numbers” which Richard mentioned? On Wed, 4 Jun 2025 at 17:54, Brian Smith <briansmith199...@gmail.com> wrote: > > Hi Peter, > > Could you please help me to understand what is the basis of choosing > 55 in runif(10,0,55))? > > Thank you! > > On Wed, 4 Jun 2025 at 02:45, peter dalgaard <pda...@gmail.com> wrote: > > > > Can't you just generate 10 values in (0,55), sort them, generate the > > distances, add 5 and cumulate? > > > > > x <- sort(runif(10,0,55)) > > > d <- diff(x)+5 > > > cumsum(c(x[1],d)) > > [1] 12.27815 21.21060 26.37856 36.03812 41.97237 57.02945 67.86113 > > [8] 75.74085 81.28533 98.30792 > > > > > > > On 3 Jun 2025, at 09.21, Brian Smith <briansmith199...@gmail.com> wrote: > > > > > > Hi Richard, > > > > > > Thanks for your insight. > > > > > > As I mentioned in one of my earlier emails to the group, I imposed a > > > constraint of accuracy up to two decimal places in order to obtain a > > > finite set of possible values. For instance, if I were to round values > > > to zero decimal places, the number of unique sequences that could be > > > generated would be strictly finite and quite limited. Therefore, I > > > chose a precision of two decimal places to allow for a larger but > > > still finite number of possibilities. > > > > > > > > > Now, my question is: how can this accuracy constraint be imposed > > > effectively? > > > > > > Is the only practical method to generate samples, round each to two > > > decimal places, and then check for duplicates to ensure uniqueness? If > > > so, I’m concerned this might be inefficient, as many samples could be > > > discarded, making the process time-consuming. > > > > > > Is there a better or more efficient way to directly enforce this > > > constraint while generating the values? > > > > > > ________________________________ > > > > > > Additionally, could you please elaborate on your suggestion regarding > > > imposing minimum gap constraints by subtracting and then adding back > > > certain gaps? > > > > > > > > > For example, based on your earlier guidance, one possible sequence I > > > obtained is: > > > > > > > > > 10.07181, 14.49839, 14.74435, 18.75167, 42.70361, 55.79623, 63.40264, > > > 68.62261, 92.49899, 98.29308 > > > > > > > > > Now, I’d like to post-process this sequence to enforce a minimum > > > difference constraint of, say, 5 units between values (including both > > > lower and upper bounds). > > > > > > What would be the appropriate way to modify the sequence to impose > > > this kind of constraint? > > > > > > > > > Many thanks for your time and insight. > > > > > > On Tue, 3 Jun 2025 at 10:42, Richard O'Keefe <rao...@gmail.com> wrote: > > >> > > >> PS I forgot about the weird gaps requirement. > > >> What you do is subtract the gaps off and then add them back. I hope > > >> that is clear. > > >> > > >> On Sun, 1 Jun 2025 at 6:52 AM, Brian Smith <briansmith199...@gmail.com> > > >> wrote: > > >>> > > >>> Hi, > > >>> > > >>> Let say I have a range [0, 100] > > >>> > > >>> Now I need to simulate 1000 10 mid-points within the range with > > >>> accuracy upto second decimal number. > > >>> > > >>> Let say, one simulated set is > > >>> > > >>> X1, X2, ..., X10 > > >>> > > >>> Ofcourrse > > >>> > > >>> X1 < X2 < ... <X10 > > >>> > > >>> I have one more constraint that the difference between any 2 > > >>> consecutive mid-points shall be at-least 5.00. > > >>> > > >>> I wonder if there is any Statistical theory available to support this > > >>> kind of simulation. > > >>> > > >>> Alternately, is there any way in R to implement this? > > >>> > > >>> ______________________________________________ > > >>> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > > >>> https://stat.ethz.ch/mailman/listinfo/r-help > > >>> PLEASE do read the posting guide > > >>> https://www.R-project.org/posting-guide.html > > >>> and provide commented, minimal, self-contained, reproducible code. > > > > > > ______________________________________________ > > > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > > > https://stat.ethz.ch/mailman/listinfo/r-help > > > PLEASE do read the posting guide > > > https://www.R-project.org/posting-guide.html > > > and provide commented, minimal, self-contained, reproducible code. > > > > -- > > Peter Dalgaard, Professor, > > Center for Statistics, Copenhagen Business SchoolSolbjerg Plads 3, 2000 > > Frederiksberg, Denmark > > Phone: (+45)38153501 > > Office: A 4.23 > > Email: pd....@cbs.dk Priv: pda...@gmail.com > > ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide https://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
