# Simulating a bimodal distribution in the range of [1;5] in R

I want to simulate a continuous data set/variable with lower/upper bounds of [1;5], while at the same time ensure that the drawn distribution can be considered as bimodal.

Searching for my problem, I found this source, which helps to simulate a bimodal distribution, however, it doesn't apply the lower/upper bounds: https://stats.stackexchange.com/search?q=bimodal+truncated+distribution

In contrast, the rtruncnorm function in R (from package truncnorm) helps me to simulate a normal (but not bimodal) distribution with lower/upper bounds.

Question now is, how can I combine both? Theoretically, I could just use the approach from the first link, i.e. generate a bimodal distribution with two underlying normal distributions and then just recalculate the drawn data with this approach (https://stats.stackexchange.com/a/25897/66544) to get my bounds.

Or I could generate two truncated normal distributions with the rtruncnorm function and then combine it to a bimodal distribution following the approach from the first link.

But I'm not sure if either of these approaches is mathematically justified.

NOTE: why do I want a range of [1;5] anyway? The real data would come from a survey where respondents will answer on a 5 point scale from 1-5 (continuously, not discrete), hence I need to simulate this finiteness.

• Do you want a discrete or a continuous distribution? – Stephan Kolassa Jul 10 '18 at 9:00
• A continuous one, i.e. respondents can select any value between 1 and 5, not only discrete values [1,2,3,4,5]. I edit my post to make this more clear. Thanks! – deschen Jul 10 '18 at 9:01

The easiest approach would be to draw $\frac{n}{2}$ samples from a truncated normal distribution with one mean and another $\frac{n}{2}$ samples from a truncated normal distribution with a different mean. This is a , specifically one with equal weights; you could also use different weights by varying the proportions by which you draw from both distributions.

library(truncnorm)

nn <- 1e4
set.seed(1)
sims <- c(rtruncnorm(nn/2, a=1, b=5, mean=2, sd=.5),
rtruncnorm(nn/2, a=1, b=5, mean=4, sd=.5))

hist(sims)


Another way is to use beta distribution. It is bounded on $[0;1]$.

So you just need to "move" half of simulated sample to $[1;3]$ and another half to $[3;5]$.

Here I use Beta(2,2) and Stephan Kolassa's framework:

nn <- 1e4
set.seed(1)
betas<-rbeta(nn,2,2)
sims <- c(betas[1:(nn/2)]*2+1,
betas[(nn/2+1):nn]*2+3)

hist(sims)