如何为 R 中的功率分析生成具有不同样本大小的负二项分布？

Question

I try to do a power simulation with an outcome variable that is zero-inflated. So I use a negative binomial distribution. What I need is the following distribution:

library(tidyverse)
set.seed(123)
rt_random <- 
rnbinom(n = 2000, mu = 25, size = .9) 
qplot(rt_random)

Now, I tried to create a function where I can use different sample sizes for the power simulation. I use the pmap function for this. However this does not seem to work. The final distribution is not zero-inflated at all and the mean is not close to the defined mean:

generate_distribution <- function(n, mus, sizes){
  tibble(n = n, 
       t_mu = mus, 
       t_size = sizes)%>% 
  mutate(rt = pmap(list(n, t_mu, t_size),
                     ~rnbinom(..1, ..2, ..3))) %>% 
  unnest(rt) 
  
}
set.seed(123)
rt_df <- 
  generate_distribution(n = 2000,
                        mus = 25,
                        sizes = .9)
qplot(rt_df$rt)

Is there an easy way to change my code so that I get the desired distribution?

Answer 1

The problem is that in your lambda function, you are not using the argument names for rnbinom . The default order for the arguments if you don't name them is n, size, prob, mu , so you are passing 2000 to n , 25 to size and 0.9 to prob . Just name the arguments explicitly as you did in your first example, and your code will work.

generate_distribution <- function(n, mus, sizes){
  tibble(n = n, 
       t_mu = mus, 
       t_size = sizes)%>% 
  mutate(rt = pmap(list(n, t_mu, t_size),
                     ~rnbinom(n = ..1, mu = ..2, size = ..3))) %>% 
  unnest(rt) 
}

set.seed(123)
rt_df <- 
  generate_distribution(n = 2000,
                        mus = 25,
                        sizes = .9)
qplot(rt_df$rt)