To generate binomial random numbers in R, you can use the “rbinom()” function. The rbinom() function in R is “used to generate a vector of binomial distributed random variables”.
The rbinom() function accepts the following three arguments.
- n: It is the number of trials.
- size: It is the number of successes.
- prob: It is the probability of success for each trial.
Binomial distribution has two possible outcomes in statistics.
- Success
- Failure
The probability of success (p) and failure (1 – p) are fixed and do not change from one trial to the next. The number of successes (x) in a given number of trials (n) follows a binomial distribution.
A binomial random number is a random variable that follows a binomial distribution.
Example 1
Generate 10 binomial distributed random numbers using the rbinom() function.
set.seed(99)
binom_dist_random_nums <- rbinom(10, size = 1, prob = 0.5)
print(binom_dist_random_nums)Output
[1] 1 0 1 1 1 1 1 0 0 0We get an output of 10 random numbers with 0 and 1 values. A value of 1 suggests success, and a value of 0 suggests failure.
In this example, we defined a probability of 0.5, which means the output has a minimum of five success outcomes. To increase the success rate of the binomial distribution, increase the probability rate.
Example 2
To create a different number of trials and probability of success, change the values of the size and prob arguments.
set.seed(99)
binom_dist_random_nums <- rbinom(10, size = 30, prob = 0.9)
print(binom_dist_random_nums)Output
[1] 27 29 26 22 27 24 26 28 28 29In this example, we generated 10 binomial random numbers with a probability of success of 0.9 and 30 trials.
Example 3
To create a histogram in R, you can use the “hist()” function and provide the binomial random numbers.
set.seed(99)
binom_dist_random_nums <- rbinom(10, size = 30, prob = 0.9)
hist(binom_dist_random_nums)Output
That’s it.
Krunal Lathiya is a Software Engineer with over eight years of experience. He has developed a strong foundation in computer science principles and a passion for problem-solving. In addition, Krunal has excellent knowledge of Data Science and Machine Learning, and he is an expert in R Language.