Hello R list, I am have question about sampling unique coordinate values. Here's how my data looks like> dat <- cbind(x1 = rep(1:5, 3), x2 = rep(c(3.7, 2.9, 5.2), each=5)) > datx1 x2 [1,] 1 3.7 [2,] 2 3.7 [3,] 3 3.7 [4,] 4 3.7 [5,] 5 3.7 [6,] 1 2.9 [7,] 2 2.9 [8,] 3 2.9 [9,] 4 2.9 [10,] 5 2.9 [11,] 1 5.2 [12,] 2 5.2 [13,] 3 5.2 [14,] 4 5.2 [15,] 5 5.2 If I sampled (1, 3.7), then, I don't want (1, 2.9) or (2, 3.7). I want to avoid either the first or second coordinate repeated. It leads to undefined matrix inversion. I thought of using sampling(), but not sure about applying it to a data frame. Thanks in advance, Mike [[alternative HTML version deleted]]
Mike, There may be a more efficient way to do this, but this works on your example. # mix up the order of the rows mix <- dat[order(runif(dim(dat)[1])), ] # get rid of duplicate x1s and x2s sub <- mix[!duplicated(mix[, "x1"]) & !duplicated(mix[, "x2"]), ] sub Jean On Mon, Jun 22, 2015 at 11:42 AM, C W <tmrsg11 at gmail.com> wrote:> Hello R list, > > I am have question about sampling unique coordinate values. > > Here's how my data looks like > > > dat <- cbind(x1 = rep(1:5, 3), x2 = rep(c(3.7, 2.9, 5.2), each=5)) > > dat > x1 x2 > [1,] 1 3.7 > [2,] 2 3.7 > [3,] 3 3.7 > [4,] 4 3.7 > [5,] 5 3.7 > [6,] 1 2.9 > [7,] 2 2.9 > [8,] 3 2.9 > [9,] 4 2.9 > [10,] 5 2.9 > [11,] 1 5.2 > [12,] 2 5.2 > [13,] 3 5.2 > [14,] 4 5.2 > [15,] 5 5.2 > > > If I sampled (1, 3.7), then, I don't want (1, 2.9) or (2, 3.7). > > I want to avoid either the first or second coordinate repeated. It leads > to undefined matrix inversion. > > I thought of using sampling(), but not sure about applying it to a data > frame. > > Thanks in advance, > > Mike > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > 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. >[[alternative HTML version deleted]]
On 6/22/2015 9:42 AM, C W wrote:> Hello R list, > > I am have question about sampling unique coordinate values. > > Here's how my data looks like > >> dat <- cbind(x1 = rep(1:5, 3), x2 = rep(c(3.7, 2.9, 5.2), each=5)) >> dat > x1 x2 > [1,] 1 3.7 > [2,] 2 3.7 > [3,] 3 3.7 > [4,] 4 3.7 > [5,] 5 3.7 > [6,] 1 2.9 > [7,] 2 2.9 > [8,] 3 2.9 > [9,] 4 2.9 > [10,] 5 2.9 > [11,] 1 5.2 > [12,] 2 5.2 > [13,] 3 5.2 > [14,] 4 5.2 > [15,] 5 5.2 > > > If I sampled (1, 3.7), then, I don't want (1, 2.9) or (2, 3.7). > > I want to avoid either the first or second coordinate repeated. It leads > to undefined matrix inversion. > > I thought of using sampling(), but not sure about applying it to a data > frame. > > Thanks in advance, > > Mike >I am not sure you gave us enough information to solve your real world problem. But I have a few comments and a potential solution. 1. In your example the unique values in in x1 are completely crossed with the unique values in x2. 2. since you don't want duplicates of either number, then the maximum number of samples that you can take is the minimum number of unique values in either vector, x1 or x2 (in this case x2 with 3 unique values). 3. Sample without replace from the smallest set of unique values first. 4. Sample without replacement from the larger set second. > x <- 1:5 > xx <- c(3.7, 2.9, 5.2) > s2 <- sample(xx,2, replace=FALSE) > s1 <- sample(x,2, replace=FALSE) > samp <- cbind(s1,s2) > > samp s1 s2 [1,] 5 3.7 [2,] 1 5.2 > Your actual data is probably larger, and the unique values in each vector may not be completely crossed, in which case the task is a little harder. In that case, you could remove values from your data as you sample. This may not be efficient, but it will work. smpl <- function(dat, size){ mysamp <- numeric(0) for(i in 1:size) { s <- dat[sample(nrow(dat),1),] mysamp <- rbind(mysamp,s, deparse.level=0) dat <- dat[!(dat[,1]==s[1] | dat[,2]==s[2]),] } mysamp } This is just an example of how you might approach your real world problem. There is no error checking, and for large samples it may not scale well. Hope this is helpful, Dan -- Daniel Nordlund Bothell, WA USA
Hi Jean,
Thanks!
Daniel,
Yes, you are absolutely right. I want sampled vectors to be as different
as possible.
I added a little more to the earlier data set.
x1 x2 x3
[1,] 1 3.7 2.1
[2,] 2 3.7 5.3
[3,] 3 3.7 6.2
[4,] 4 3.7 8.9
[5,] 5 3.7 4.1
[6,] 1 2.9 2.1
[7,] 2 2.9 5.3
[8,] 3 2.9 6.2
[9,] 4 2.9 8.9
[10,] 5 2.9 4.1
[11,] 1 5.2 2.1
[12,] 2 5.2 5.3
[13,] 3 5.2 6.2
[14,] 4 5.2 8.9
[15,] 5 5.2 4.1
If I sampled row, 1, 6, 11, solving the system of equations will not be
possible. So, I am avoiding "similar vectors".
Thanks,
Mike
On Mon, Jun 22, 2015 at 2:19 PM, Daniel Nordlund <djnordlund at
frontier.com>
wrote:
> On 6/22/2015 9:42 AM, C W wrote:
>
>> Hello R list,
>>
>> I am have question about sampling unique coordinate values.
>>
>> Here's how my data looks like
>>
>> dat <- cbind(x1 = rep(1:5, 3), x2 = rep(c(3.7, 2.9, 5.2), each=5))
>>> dat
>>>
>> x1 x2
>> [1,] 1 3.7
>> [2,] 2 3.7
>> [3,] 3 3.7
>> [4,] 4 3.7
>> [5,] 5 3.7
>> [6,] 1 2.9
>> [7,] 2 2.9
>> [8,] 3 2.9
>> [9,] 4 2.9
>> [10,] 5 2.9
>> [11,] 1 5.2
>> [12,] 2 5.2
>> [13,] 3 5.2
>> [14,] 4 5.2
>> [15,] 5 5.2
>>
>>
>> If I sampled (1, 3.7), then, I don't want (1, 2.9) or (2, 3.7).
>>
>> I want to avoid either the first or second coordinate repeated. It
leads
>> to undefined matrix inversion.
>>
>> I thought of using sampling(), but not sure about applying it to a data
>> frame.
>>
>> Thanks in advance,
>>
>> Mike
>>
>>
> I am not sure you gave us enough information to solve your real world
> problem. But I have a few comments and a potential solution.
>
> 1. In your example the unique values in in x1 are completely crossed with
> the unique values in x2.
> 2. since you don't want duplicates of either number, then the maximum
> number of samples that you can take is the minimum number of unique values
> in either vector, x1 or x2 (in this case x2 with 3 unique values).
> 3. Sample without replace from the smallest set of unique values first.
> 4. Sample without replacement from the larger set second.
>
> > x <- 1:5
> > xx <- c(3.7, 2.9, 5.2)
> > s2 <- sample(xx,2, replace=FALSE)
> > s1 <- sample(x,2, replace=FALSE)
> > samp <- cbind(s1,s2)
> >
> > samp
> s1 s2
> [1,] 5 3.7
> [2,] 1 5.2
> >
>
> Your actual data is probably larger, and the unique values in each vector
> may not be completely crossed, in which case the task is a little harder.
> In that case, you could remove values from your data as you sample. This
> may not be efficient, but it will work.
>
> smpl <- function(dat, size){
> mysamp <- numeric(0)
> for(i in 1:size) {
> s <- dat[sample(nrow(dat),1),]
> mysamp <- rbind(mysamp,s, deparse.level=0)
> dat <- dat[!(dat[,1]==s[1] | dat[,2]==s[2]),]
> }
> mysamp
> }
>
>
> This is just an example of how you might approach your real world
> problem. There is no error checking, and for large samples it may not
> scale well.
>
>
> Hope this is helpful,
>
> Dan
>
> --
> Daniel Nordlund
> Bothell, WA USA
>
>
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> 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.
>
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