generate a random vector with specific number of negative numbers

0
votes

I'm doing a failure analysis, for which I like to try some different scenarios and some random trials. So far I've done this with the mosaic package and its working out great. In one specific scenario I want to generate a vector of (semi)random numbers with from different distributions. No problem so far.

Now I want to have defined number of negative numbers in this vector. For example I want to have between 0-5 negative numbers in the vector of 25 numbers. I thought I could use something like rbinom(n=25,prob=5/25,size=1) to get 5 random ones first but of course 5/25, 25 times can be more than 5 ones. This seems a dead end. I could get it done with some for loops, but probably something easier exists. I've tried all sorts of sample,seq, shuffle combinations but I cannot get it to work so far.

does anyone have any ideas or suggestions?

2
rr-mosaic
What distribution will the absolute value of your numbers be from? Will they be integers? Continuous? – David Robinson
@user1549537 Hi, if any answer solves your problem can you click on "accept it" so that other people can see it? thanks – agenis

2 Answers

3
votes

If you have a vector x where all elements are >= 0, let's say drawn from Poisson:

x = rpois(25, lambda=3)

You can make a random 5 of the negative by doing

x * sample(rep(c(1, -1), c(length(x) - 5, 5)))

This works because

rep(c(1, -1), c(length(x) - 5, 5))

will be

#  [1]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1 -1 -1 -1 -1 -1

and sample(rep(c(1, -1), c(length(x) - 5, 5))) simply shuffles them up randomly:

sample(rep(c(1, -1), c(length(x) - 5, 5)))
#  [1]  1  1 -1  1  1  1  1  1  1  1  1 -1  1  1  1 -1 -1  1  1  1 -1  1  1  1  1
0
votes

I can suggest a very straightforward solution, guaranteeing 5 negative values and working for any continuous distribution. The idea is just to sort the vector and substract the 6th biggest to each value:

x <- rnorm(25)
res <- sort(x, T)[6] - x
#### [1]  0.4956991  1.5799885  2.4207497  1.1639569  0.2161187  0.2443917 -0.4942884 -0.2627706  1.5188197
#### [10]  0.0000000  1.6081025  1.4922573  1.4828059  0.3320079  0.3552913 -0.6435770 -0.3106201  1.5074491
#### [19]  0.6042724  0.3707655 -0.2624150  1.1671077  2.4679686  1.0024573  0.2453597
sum(res<0)
#### [1] 5

It also works for discrete distributions but only if there are no ties..