Wsrtjtyk

I wanted to ask if someone knows a workaround of how I can dynamically assign arguments to R's sampling functions, i.e., I want to write a list with different names, say "mean" and "sd", and the elements of both of these sub-lists contain the corresponding numeric values for these parameters I want to have. As an example, I would like to do this:

#Distribution of Interest

SamplingDistribution <- rnorm



#Parameters of Interest for Normal Distribution

Parameters <- list(mean    = c(1, -1), 

                   sd      =  c(1, 2)   

               )



#Not Working Example

SamplingDistribution (n = 1, 

                      for(i in 1:length(Parameters) ){

                      names(Parameters)[i] <- Parameters[i]

                      }

  )

So ideally i just clarify the sampling distribution of interest at the beginning and then can put any argument that I want (in any order) in the Parameters list. Then the for loop just loops through the names of the parameters lists, and assigns the corresponding numeric values to the sample. Thanks to your input!

Best regards,

Edit: I get that I can just use the listnames in the rnorm function, but the focus of this question is really to somehow dynamically assign that, i.e. I can just expand the parameter list with more arguments and I dont have to assign anything new to the sampling procedure. I tried already around quite a bit with message/pasteo/cat/..., but the rnorm() function seems to not really accept any of these ...

There are a number of approaches to this, but to begin you'll want to check out the apply family of functions, helpful link here:

Parameters <- list(mean    = c(1, -1), 

                   sd      =  c(1, 2))



set.seed(1)



mapply(function(mn, sd) rnorm(1, mean = mn, sd = sd),

       Parameters[[1]],

       Parameters[[2]])



[1]  0.3735462 -0.6327134

Second Attempt:

This doesn't perfectly recreate what you're looking for, but I believe it gets close.

library(purrr)



my_sampling <- function(dst, par_list){

  map(transpose(par_list), 

         function(params){

           do.call(dst, params)

         })

}



norm_params <- list(n       = c(2,1),

                    mean    = c(1, -1), 

                    sd      = c(1, 2))



pois_params <- list(n      = c(5, 6),

                    lambda = c(3, 4))



set.seed(1)



my_sampling(rnorm, norm_params)



[[1]]

[1] 0.3735462 1.1836433



[[2]]

[1] -2.671257



my_sampling(rpois, pois_params)



[[1]]

[1] 6 4 3 1 2



[[2]]

[1] 2 5 3 5 4 5

You can vectorize a function with Vectorize such that vectors can be used for its parameters:

rnormV <- Vectorize(rnorm)



rnormV(1, Parameters[[1]], Parameters[[2]])

# [1] -0.0530436 -0.2327272

mapply works

#Parameters of Interest for Normal Distribution

Parameters <- list(mean = c(1, -1), 

                   sd = c(1, 2)   

)

#Working Example

mapply(rnorm, n=1, mean=Parameters$mean, sd=Parameters$sd)

[1]  0.03164361 -1.12035840

Update

If you don't want to explicitly name the parameters or rely on any external packages you could simply do (see a similar answer for one set of parameters here) :

#Parameters of Interest for Normal Distribution

Parameters <- list(n = 1,

                   mean = c(10, -1), 

                   sd = c(1, 2))



do.call(Vectorize(rnorm),Parameters)

And if you will always have the same n and don't want it in your parameters list then you could do:

#Parameters of Interest for Normal Distribution

Parameters <- list(mean = c(1, -1), 

                   sd = c(1, 2)   

)



do.call(Vectorize(rnorm),c(list(n=1),Parameters))

Wrapped up in a nice function:

sampling <- function(fun, n, params{

  do.call(Vectorize(fun),c(list(n=n),params)

}



sampling(rnorm, 1, Parameters)