How to call a function multiple times with changing arguments and store them in a data frame in R?-CodePudding

I´m a R-beginner and want to run a simulation, where I call the normal distribution multiple times with different values for sd.

For example, I want to run rnorm with each sd one time, where the sd increases from 1 to M with each run. And I want to store the results in a list or data frame. I know that what I try to achieve is a pretty basic step, but I failed trying it by myself.

rnorm(n=1, mean=0, sd=1)

rnorm(n=1, mean=0, sd=2)

... rnorm(n=1, mean=0, sd=M)

I tried to code this with a for loop, but it didnt work.

test <- for(i in 1:10){
  test <- rnorm(n=1, mean=0, sd=i)
  return(test)
}

If I enter test in the console, I just get NULL as output. Hope someone can point me in the right direction.

Thanks in advance.

CodePudding user response：

If you want to generate 1 values per i, you may try

res <- c()
for(i in 1:10){
  res <- c(res,rnorm(n=1, mean=0, sd=i))
}
res
[1]  1.3586796 -0.2055755  1.1630148 -0.2152202 -6.8852978 -2.4899674 -2.7600297 -0.4745072  9.9002283  7.6317575

The result will be saved in a vector.

If you want to generate several values per i, for example, 5 values per i.

res <- matrix(0, nrow = 5, ncol = 10)
for(i in 1:10){
  res[,i] <- rnorm(n=5, mean=0, sd=i)
}
res
           [,1]       [,2]      [,3]       [,4]       [,5]       [,6]      [,7]         [,8]       [,9]     [,10]
[1,] -0.1645236 -1.4149903  1.194318  7.9215996 12.0080888   1.132754  3.328567  2.331569884  -5.118019  3.329504
[2,] -0.2533617  0.7291639 -1.836079 -1.4688859 -0.1962000 -10.829752 -4.969625 -3.546334986  -1.216608 10.630998
[3,]  0.6969634  1.5370658  1.023359 -4.1765385  3.4486968   8.793329  4.275084  0.008842813  10.602783 -3.041839
[4,]  0.5566632 -0.2246924 -3.388089  2.2788785  0.1400108   0.919520 -6.538683  0.594730593 -13.712101  3.700188
[5,] -0.6887557  1.7622155  4.299071 -0.5402184 -3.7163660  13.035670 -8.775434 -4.716167570   5.345516  2.670988

CodePudding user response：

It is good R practice to preallocate arrays. On a sample this small it won't make any functional difference, but your code would be more idiomatic as follows:

> k <- 10
> test <- rep(NA, k)
> for(i in 1:k){
      test[i] <- rnorm(n=1, mean=0, sd=i)
  }
> test
 [1]  -1.0083914   0.4404118   3.0581583  -8.2564404   3.7986092 -10.9064909  -6.5173390  -3.8936812
 [9]   5.2027044 -11.1803524

(In your code above: you don't return values from inside for loops, and you'd be overwriting your array with the unnecessary assignment in every iteration.)

CodePudding user response：

The above solutions work, but here is an alternative that I think is useful for R beginners.

We can achieve this without for-loops by adapting the function to the use case. Using a vectorized solution is usually preferable in R.

v <- Vectorize(rnorm, vectorize.args = c("sd"))

Which we can now call like this

set.seed(10)
v(n = 1, mean = 0, sd = 1:10)
#> 0.01874617  -0.36850508  -4.11399165  -2.39667086   1.47272563   2.33876580  -8.45653323  -2.90940814 -14.64005414  -2.56478394

Which is the same as

set.seed(10)
sds <- 1:10
out <- vector(mode = "numeric", 10)
for (i in seq_along(sds)) {
  out[i] <- rnorm(1, 0, sds[i])
}
out
#> 0.01874617  -0.36850508  -4.11399165  -2.39667086   1.47272563   2.33876580  -8.45653323  -2.90940814 -14.64005414  -2.56478394

but v is much (about 0.05 milliseconds vs 2 milliseconds on this trivial example) faster, and requires less memory.