Simulating a distribution in R?

In summary: Bootstrapping is a common technique in stats.bootstrap(N, k=n, vars=list, use.replacement=F) where N is the number of observations, k is the number of bootstrap replications, and vars is a list of variables to be sampled with replacement. This function will randomly sample from the first k observations, then repeat the process for k-1 observations, and so on, until k bootstrap replications are done. It will then return the mean, standard deviation, and 95% confidence interval for the k-th observation.The bootstrap can give you a more accurate estimate of the population mean than simply sampling from the data. However, bootstrapping is more computationally intensive
  • #1
moonman239
282
0
I have a dataset in R. What I want to do is simulate a variable that holds the same distribution. How do I do this?
 
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  • #2
Are you interested in a computer simulation? Look up Monte Carlo method.
 
  • #3
mathman said:
Are you interested in a computer simulation?

Yes

mathman said:
Look up Monte Carlo method.

I know about Monte Carlo simulations.
 
  • #4
moonman239 said:
I have a dataset in R. What I want to do is simulate a variable that holds the same distribution. How do I do this?

You need a random number generator where you can specify the distribution parameters for N simulations. Of course the simulated distribution parameters will only match your template on average. I worked with simulations in Minitab where you could specify four moments of a normal distributions and also for a few others such as the Poisson and binomial. You can write your own programs by using the PDFs and MGFs with randomly generated parameters (ie simulated random sample means around a specified population mean) if you like doing that sort of thing.
 
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  • #5
SW VandeCarr said:
You need a random number generator where you can specify the distribution parameters for N simulations.

I know. Is there a function to do that in R? I know you can simulate variables from widely-known distributions (normal, Poisson, uniform, chi-square, etc.)
 
  • #6
moonman239 said:
I know. Is there a function to do that in R? I know you can simulate variables from widely-known distributions (normal, Poisson, uniform, chi-square, etc.)

A good stats package should have this ability. I don't specifically know about R. Did you check commands that begin with RAND?
 
  • #8
Anything else? I don't think that helped. Thanks, anyways.
 
  • #9
Rather than trying to estimate the distribution from which the data was drawn, and to then use that (parameterized) distribution to simulate from(involving random number generators), you can just bootstrap sample straight from the observed data, i.e., just keep resampling, with or without replacement (with to get iid sampling) from the observed data. Look at the function: sample(), then there's more sophisticated boot() functions too.
 

FAQ: Simulating a distribution in R?

How do I generate random numbers from a specific distribution in R?

To generate random numbers from a specific distribution in R, you can use the r prefix followed by the distribution's name. For example, to generate 100 random numbers from a normal distribution with mean 0 and standard deviation 1, you can use the command rnorm(100,0,1). You can also specify other parameters for different distributions, such as the degrees of freedom for a t-distribution or the lambda parameter for a Poisson distribution.

How can I visualize the simulated distribution in R?

You can use the hist() function to plot a histogram of the simulated distribution. Alternatively, you can use the plot() function to plot the simulated values against their corresponding probabilities to create a probability density plot. You can also use the qqplot() function to compare the simulated distribution to a theoretical distribution.

Can I set a seed for reproducibility when simulating a distribution in R?

Yes, you can use the set.seed() function to set a seed for reproducibility when simulating a distribution in R. This ensures that the same random numbers are generated each time the code is run, allowing for consistent results.

How do I simulate a specific number of observations from a distribution in R?

You can use the r prefix followed by the distribution's name and specify the desired number of observations as the first argument. For example, to simulate 500 observations from a binomial distribution with 10 trials and a probability of success of 0.5, you can use the command rbinom(500, 10, 0.5). You can also use the sample() function to simulate observations from a custom distribution.

Can I transform the simulated distribution in R?

Yes, you can use the transform() function to apply a transformation to the simulated distribution. For example, you can apply a logarithmic transformation using the command transform(simulated_distribution, log). You can also use the scale() function to standardize the simulated distribution by subtracting the mean and dividing by the standard deviation.

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