CODEWITHSUNDEEP
rclassmethods
delcast
delcast

Reputation: 516

Creating a new class with parameters from a function

AIM: I would like to add a "distribution" and "parameters" to the summary output of a function I created.

The function I created takes a n and a choice of distribution and returns random numbers from that distribution. The output of the function is an object of the class MyClass.

Further below I created the summary function for objects of MyClass which currently returns the length, mean and standard deviation of the output.

PROBLEM: I would like for the summary function to also print out the original parameters of the random_number_generator function, see DESIRED OUTPUT below.

random_number_generator <- function(n,distribution,mean=NULL,sd=NULL,lambda=NULL,size=NULL,prob=NULL){
  if (distribution=="Normal"){
   x <- rnorm(n=n,mean=mean,sd=sd)
  }
  
  if (distribution=="Poisson"){
  x <- rpois(n=n, lambda=lambda)
  }
  
 if (distribution=="Binomial") {
  x <- rbinom(n=n,size=size,prob=prob)
 }
  
  class(x) <- "MyClass"
  return (x)
}

answer <- random_number_generator(n=10, distribution = "Binomial",size=15,prob=0.4)


summary.MyClass <- function(x) {
    stopifnot(inherits(x, "MyClass"))
    cat("\t\n", 
        sprintf("Sample size: %s\n", length(x)),
        sprintf("Mean: %s\n", mean(x)),
        sprintf("Standard Deviation: %s\n", sd(x))
        )
}

CURRENT OUTPUT:

summary(answer)
	
 Sample size: 10
 Mean: 4.9
 Standard Deviation: 1.59513148186739

DESIRED OUTPUT:

class(answer)
[1] "MyClass"

summary(answer)

    Sample size: 10
    Mean: 4.9
    Standard Deviation: 1.59513148186739
    Distribution: "Binomial"
    Size: 15
    Prob: 0.4

Upvotes: 1

Views: 42

Answers (2)

trosendal
trosendal

Reputation: 1234

You could also store your parameters as attributes of your return value. That way you don't get a list back if you don't want that:

random_number_generator <- function(n,distribution,mean=NULL,sd=NULL,lambda=NULL,size=NULL,prob=NULL){

  if (distribution=="Normal"){

   x <- rnorm(n=n,mean=mean,sd=sd)

  }

  if (distribution=="Poisson"){

  x <- rpois(n=n, lambda=lambda)

  }

 if (distribution=="Binomial") {

  x <- rbinom(n=n,size=size,prob=prob)

 }

    attributes(x) <- list(distribution = distribution,
                          size = size,
                          prob = prob)
    class(x) <- "MyClass"

  return (x)
}

answer <- random_number_generator(n=10, distribution = "Binomial",size=15,prob=0.4)

summary.MyClass <- function(x) {
    stopifnot(inherits(x, "MyClass"))
    cat("\t\n", 
        sprintf("Sample size: %s\n", length(x)),
        sprintf("Mean: %s\n", mean(x)),
        sprintf("Standard Deviation: %s\n", sd(x)),
        sprintf("Distribution: %s\n", attr(x, "distribution")),
        sprintf("Size: %s\n", attr(x, "size")),
        sprintf("Prob: %s\n", attr(x, "prob"))
        )
}

summary(answer)

Upvotes: 1

Julius Vainora
Julius Vainora

Reputation: 48201

We could do the following:

random_number_generator <- function(n, distribution, mean = NULL, sd = NULL, lambda = NULL, size = NULL, prob = NULL) {
  if (distribution == "Normal")
    x <- rnorm(n = n, mean = mean, sd = sd)
  else if (distribution == "Poisson")
    x <- rpois(n = n, lambda = lambda)
  else if (distribution == "Binomial")
    x <- rbinom(n = n, size = size, prob = prob)
  out <- list(x = x, distribution = distribution, size = size, prob = prob)
  class(out) <- "MyClass"
  out
}

summary.MyClass <- function(obj) {
  stopifnot(inherits(obj, "MyClass"))
  cat("\t\n", 
      sprintf("Sample size: %s\n", length(obj$x)),
      sprintf("Mean: %s\n", mean(obj$x)),
      sprintf("Standard Deviation: %s\n", sd(obj$x)),
      sprintf("Distribution: %s\n", obj$distribution),
      sprintf("Size: %d\n", obj$size),
      sprintf("Probability: %s\n", obj$prob)
  )
}

answer <- random_number_generator(n = 10, distribution = "Binomial", size = 15, prob = 0.4)
summary(answer)
#   
#  Sample size: 10
#  Mean: 5.5
#  Standard Deviation: 1.64991582276861
#  Distribution: Binomial
#  Size: 15
#  Probability: 0.4

So, to return extra information, we need to keep this extra information as an output from random_number_generator, which I did with

out <- list(x = x, distribution = distribution, size = size, prob = prob)
class(out) <- "MyClass"

Upvotes: 2

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