CODEWITHSUNDEEP
rgamma-distribution
python_person
python_person

Reputation: 15

Function that will generate iter samples of size n from a gamma distribution with shape parameter alpha and rate parameter beta

The function needs to return the mean and standard deviation of each sample.

This is what I have:

sample_gamma <- function(alpha, beta, n, iter) {
  mean = alpha/beta
  var = alpha/(beta)^2
  sd = sqrt(var)
  gamma = rgamma(n,shape = alpha, scale = 1/beta)
  sample_gamma = data.frame(mean = replicate(n = iter, expr = mean))
}

I'm very lost for this. I also need to create a data frame for this function.

Thank you for your time.

Edit:

sample_gamma <- function(alpha, beta, n, iter) {
  output <- rgamma(iter, alpha, 1/beta)
  output_1 <- matrix(output, ncol = iter)
  means <- apply(output_1, 2, mean)
  sds <- apply(output_1, 2, sd)
mystats <- data.frame(means, sds)
return(mystats)
  }

This works except for the sds. It's returning NAs.

Upvotes: 0

Views: 439

Answers (1)

SteveM
SteveM

Reputation: 2301

It's not really clear to me what you want. But say you want to create 10 samples of size 1000, alpha = 1, beta = 2. Then you can create a single stream of rgamma realizations, dimension them into a matrix, then get your stats with apply, and finally create a data frame with those vectors:

output <- rgamma(10*1000, 1, 1/2)
output <- matrix(output, ncol = 10)
means <- apply(output, 2, mean)
sds <- apply(output, 2, sd)
mystats <- data.frame(means, sds)

You could wrap your function around that code, replacing the hard values with parameters.

Upvotes: 3

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